Organizations are evolving from human-driven operations to supervised autonomous operations, where AI investigates, recommends, and remediates, and humans stay in control of what matters most. A big part of delivering on that vision is working with the agents that customers already run in their cloud environments.

Harness the power of hyperscale agents

Each hyperscaler has AI agents that automatically investigate and help resolve production incidents using native cloud telemetry and tools. They act like embedded site reliability engineers, analyzing issues and recommending or executing remediation steps without waiting for a human to start the process.

AWS DevOps Agent provides investigation and remediation in AWS using native tooling. An Azure SRE Agent specializes in investigating and remediating Azure issues. And Google Gemini Cloud Assist is for incident analysis across Google Cloud Platform (GCP).

Over the past year, we’ve published how Dynatrace supercharges each of these cloud agents individually. When an issue occurs, Dynatrace Intelligence combines causal, predictive, and agentic AI using the Smartscape dependency graph to automatically link related symptoms and root causes across the environment into one unified problem card.

When Dynatrace integrates with the AWS DevOps Agent, dependency-aware root cause analysis combines with AWS frontier-agent capabilities, and joint customers report up to 70% reductions in mean time to resolution. When Azure SRE Agent connects with Dynatrace, deterministic, causation-based AI flows directly into Azure-native remediation workflows, cutting the back-and-forth between teams. And with Google Gemini Cloud Assist, Dynatrace delivers the same production context layer to GCP-hosted incidents: precise root cause, full topology, real business impact.

From integrations to intelligent orchestration

Many enterprises run workloads across AWS, Azure, and Google Cloud simultaneously, and managing three separate integrations with separate routing logic and separate cost controls is its own operational tax. Cloud SRE Agents provides a single orchestration layer that routes problems to specific hyperscaler agents based on configurable profiles to see everything happening across all three cloud agents.

The Cloud SRE Agents app writes findings back to Dynatrace, and provides your team with measurable visibility into autonomous actions.

How Cloud SRE Agents works

When Dynatrace Intelligence detects a problem and identifies the root cause, Cloud SRE Agents calls dedicated cloud-native agents from AWS, Azure, and Google Cloud to retrieve deeper insights from the sources that only they can reach: CloudTrail history, Azure subscription policy, GCP project IAM, recent deployments, and native runbooks. These agents run in parallel, gathering evidence as soon as the problem is detected. Their findings, and, where applicable, the recommended remediation path, are displayed in the same Dynatrace problem view that the on-call SRE is already using in their day-to-day workflow.

One view. No tab-switching. The work starts without you.

Three workflows do the orchestration in the background:

• Investigate evaluates your Interaction Profiles and dispatches matching problems to the right agents in parallel.
• Periodic Tasks polls each cloud provider for completion, detects stalled or timed-out investigations, and writes findings back as problem annotations.
• Event Handlers normalize the cloud-provider event stream so every action correlates back to its originating problem, end to end.

Cloud SRE Agents has the insights and intelligence to decide which agent gets which problem, tracks each run to completion, and brings the answers back together in a single view. The Overview tab provides a real-time, interactive network graph of problems, agents, and activities. The replay view allows the user to step back in time and get an overview of what has happened when, as well as the status of each investigation.

Intelligent routing with Interaction Profiles

In agentic operations, routing rules make the difference between turning autonomous systems loose on every alert and pointing them precisely where they earn their keep. Interaction Profiles are how you express routing judgment in Cloud SRE Agents. Each profile pairs a set of conditions with the agent or agents that should handle the problems flagged by the profile, and evaluates the conditions whenever Dynatrace Intelligence detects a problem.

The conditions you can write are deliberately broad. You can route by the cloud account, subscription, or project an incident touches; by problem category (availability, error, slowdown, resource contention); by affected entity type (a Kubernetes cluster, a database, a Lambda function); by tag, label, or any custom attribute carried in the problem record. Conditions combine with AND/OR logic and nest as deeply as you need, keeping real production routing policy inside the app rather than spilling into custom workflows or scripts.

Three ways teams put it to work

Route problems to the right cloud, automatically

A spike in Lambda error rates belongs to AWS DevOps Agent. An Azure App Service degradation calls for Azure SRE Agent. A Pub/Sub latency issue lands with Gemini Cloud Assist. In a multicloud estate, none of those decisions should fall to a human at 2:00 AM. A profile filtered by AWS Account ID, Azure Subscription ID, or GCP Project ID, then narrowed by resource type or tag, settles the routing question once. Every matching problem is automatically routed to the right specialist with the right cloud-native context.

Optimize spend with budget-aware routing

Cloud AI agents do work, and that work has a cost. Cloud SRE Agents lets you set a Monthly Duration Budget per agent and gate dispatch on it via a Has Available Budget filter: once the budget is exhausted, new investigations either stop (in strict enforcement mode) or proceed with a logged warning. The duration figure itself is a proxy, derived from Dynatrace event timestamps rather than the cloud provider’s clock, which makes it useful as a circuit breaker and directional signal, not a substitute for AWS, Azure, or GCP usage reports. The governance value is what matters: you decide how much autonomous investigation you’re willing to underwrite each month, and the system holds the line.

Tier autonomous investigation by problem type and entity

Not every Dynatrace problem warrants an autonomous investigation. Problem Category filters let you dispatch agents only to the problem categories that warrant it, for example, availability or error problems that require immediate action, rather than slowdowns or custom alerts where human triage might still be the right call. Layer on Entity Type filters, and you can further focus on specific infrastructure tiers (hosts, services, process groups, Kubernetes clusters). The result is a tiered model: high-severity issues receive immediate autonomous investigation, lower-severity signals queue for human review, and your team controls the threshold.

Governance that makes autonomous work measurable

Agentic operations earn trust when teams can see what the agents did, why, and whether it worked. Cloud SRE Agents treats that as a first-class concern, with two views built for the two audiences who care about it.

The Activity tab is the audit trail. Every investigation and mitigation appears as a card on a unified timeline; expand any card to see the agent’s full findings, the evidence it pulled, and the action it took or recommended. Each response can be rated Good, OK, or Bad, building a quality signal grounded in what your team actually saw rather than what the system predicted. When a single problem triggers work across multiple agents, those activities roll up to a single status (in progress, done, or stalled), so you always know where things stand without having to reconstruct the run from individual records.

The Statistics tab is where autonomous operations become a number you can show to a leadership team: problems handled, mitigations executed, average investigation time, MTTR and MTTI trends, success rates, and satisfaction scores broken down by agent. The same view doubles as a directional cost lens, since agent working time is the dominant driver on the cloud side of the bill. Treat the number as a trend signal and a circuit-breaker input, not a billing record (reconcile against AWS, Azure, and GCP usage reports for exact spend), and it makes the case for expanding agentic coverage with evidence rather than anecdote.

Why production context multiplies the value

What changes Cloud SRE Agents from a smart dispatcher into something more is what Dynatrace Intelligence contributes before an agent ever begins its analysis. Dynatrace delivers deterministic, causation-based root cause analysis grounded in Dynatrace’s Smartscape real-time dependency mapping, alongside business impact assessment and correlated telemetry. That context shapes the entire direction of the investigation. A cloud agent arriving with that foundation starts from “this specific service on this specific host is the root cause, and here’s the customer impact” rather than “something is wrong somewhere in this account.”

The numbers reflect it. According to AWS, organizations using the AWS DevOps Agent with Dynatrace see up to a 75% reduction in mean time to resolution.

Western Governors University, which runs a fully online learning environment for 200,000 students, uses AWS DevOps Agent with Dynatrace to automate cross-system correlation that previously required manual effort across multiple tools. At a larger scale, United Airlines transports more than 500,000 passengers daily across a hybrid environment that includes more than 500 AWS accounts, 20,000 Lambda functions, and 38,000 OneAgent deployments.

The team’s description of the before and after status is direct: previously, multiple tools with overlapping functions created gaps and black boxes during troubleshooting. With AWS DevOps Agent and Dynatrace, Dynatrace identifies the responsible layer, the agent investigates and provides resolution steps, and everything surfaces in a single Dynatrace view. No 3:00 AM tool-switching required.

Get started

For a closer look at the individual integrations, read the posts on AWS DevOps Agent and Dynatrace and Azure SRE Agent and Dynatrace, or see how Dynatrace Intelligence powers autonomous operations. To put your cloud agents to work today, install Cloud SRE Agents from the Dynatrace Hub. Cloud SRE Agents is currently available as a community-supported app.

The post Orchestrate multicloud AI agents for autonomous incident resolution appeared first on Dynatrace news.

What is the Kiro power for Dynatrace?

The Kiro power for Dynatrace delivers live observability data, root cause analysis, and remediation suggestions directly into the Kiro IDE, with no JSON editing or manual MCP setup.

Kiro is an AI-powered IDE that helps developers move from idea to working code through spec-driven development and an agentic assistant. To make the assistant genuinely useful in unfamiliar domains, Kiro recently introduced powers: curated, partner-validated bundles of MCP servers, steering files, and best practices that install with a single click and load on demand when a relevant task comes up. Install a power, and Kiro’s agent gains specialized expertise the moment you need it.

For Dynatrace customers already working in Kiro, it’s the shortest path yet from code to production insight. For developers new to Dynatrace, it’s a one-click way to ground Kiro’s reasoning in real facts from your environment, not guesses.

Why this matters for developers

Developers have historically been one step removed from production. When something breaks after deployment, the path to figuring out what went wrong usually runs through a Site Reliability Engineering (SRE) or operations team, and AI coding assistants can’t automatically and reliably remediate issues in software they’re unfamiliar with. Agents that can write code are guessing about how their code behaves in production unless they have access to real telemetry data.

The Dynatrace Kiro power for Dynatrace closes this gap through Dynatrace Intelligence, the agentic operations system at the core of the Dynatrace platform. Kiro’s answers are grounded in deterministic, causal AI and real-time production data, not probabilistic guesses.

With the tools provided by the Kiro power, developers can:

• Investigate live incidents and get root cause analysis directly in Kiro chat
• Query metrics, logs, and traces from production using natural language
• Surface security vulnerabilities affecting the code they’re working on
• Get remediation suggestions grounded in what’s actually happening in their environment

“Using Kiro powers for Dynatrace has been a total game-changer in the observability space. Deep-dive root cause analysis of complex system issues that once required lengthy manual intervention now happens in seconds, giving us unprecedented speed and confidence.”

Mike Kobush, Sr. Software Performance Engineer, NAIC

How to install the Kiro power for Dynatrace

Getting started takes only a few steps. Once installed, the Kiro power activates automatically when Kiro detects a relevant task. Mention an incident, a slow service, or anything that needs production context, and the Dynatrace tools and guidance will load in Kiro chat.

Prerequisites

• A Dynatrace account. If you don’t already have one, you can start a free 15-day trial.
• Kiro installed on your system.

Prepare the Dynatrace connection

First, create a Dynatrace Platform Token, which Kiro will use to authenticate. Then add the required permissions for the Dynatrace MCP server.

Install the Kiro power

The power can be installed from either the Kiro IDE or the Kiro powers website. For this walkthrough, we’ll use the IDE.

1. Launch the Kiro IDE.
2. Select the Ghosty icon with the lightning bolt to open the powers panel.
3. Select Dynatrace Observability from the Recommended
4. Select Install. The power is registered with placeholder values for the Dynatrace URL and token. Therefore, Kiro will show an error message that the MCP server can’t be reached.
5. To complete the configuration, select Open Settings and replace the placeholders with your environment details.

Configure your tenant and token

In the settings file, replace the two placeholders:

Start asking questions

Open a new chat in Kiro and start interacting with your Dynatrace environment using natural language. Query active problems or security vulnerabilities, request a root cause analysis to identify critical issues in production, or pull related logs and traces, all without leaving the IDE.

See it in action

The short demo below walks through installing the Kiro power for Dynatrace, verifying the connection, and running a first query against your environment to list the top 10 vulnerabilities detected by Dynatrace.

Get started with the Kiro power for Dynatrace

Kiro powers transform what used to be a stitching exercise (MCP servers here, steering files there, custom instructions somewhere else) into one single, ready-to-use bundle. The Kiro power for Dynatrace applies the same idea to observability: live production insight, causal root cause analysis, and remediation grounded in real telemetry, all available the moment a developer needs them.

The result is a tighter loop between writing code and understanding how it behaves in production. Less waiting for diagnostic data from someone else. Less guesswork from an AI assistant operating without context. And, more time spent on the work that actually matters.

Ready to try it? The Kiro Power for Dynatrace is publicly available: install it from kiro.dev or the Kiro IDE and start asking your environment questions.

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If you’ve already wired an AI coding assistant up to Dynatrace, through the MCP server, the Dynatrace CLI (dtctl), or a custom agent you built yourself, you’ve seen your agent have difficulty interpreting data or calling for fields that don’t exist. This makes sense, your agent may be making assumptions based upon training that isn’t relevant. It lacks the skills to understand how to get the best value from Dynatrace. That is where Dynatrace for AI fills the gap.

What are agent skills?

Agent skills are an open format for packaging domain knowledge that AI agents can load on demand. A skill is a folder containing a SKILL.md file with focused instructions, examples, and optional reference material. Compatible agents, such as Claude Code, GitHub Copilot, Cursor, Cline, or others, discover installed skills and load the full content only when it’s relevant to the task at hand.

The net effect: you can install dozens of skills without bloating an agent’s context window. Agents pull in exactly what’s relevant when it’s relevant, and ignore the rest.

Built for agents working with Dynatrace

Dynatrace for AI is a curated set of skills that give an agent the three things it needs to efficiently do real work on Dynatrace:

• Access to Dynatrace data and insights: through DQL queries against Grail®, Smartscape® dependency graph, or problem records.
• Dynatrace expertise: the syntax rules, entity-model distinctions, and query patterns that separate a working query from one that looks correct but returns nothing.
• Task-level starting points: ready-made prompt templates for common engineering workflows, so teams don’t have to invent the approach from scratch.

Skills don’t connect to Dynatrace directly. You have to pair them with the MCP server or dtctl to perform live queries and initiate actions. Together, they turn an agent with generic observability intuition into one that easily extracts value from Dynatrace.

Complement your agent with domain expertise

The first release of Dynatrace for AI agent skills is focused on the workflows that engineering teams run every day:

• DQL fundamentals: covering the pipeline model, core data objects, and when to use fetch, timeseries, or smartscapeNodes to prevent failures that typically come from models trained on generic query-language data.
• Observability across the stack: services, traces, logs, frontends, and problems, each covering the entity model, key fields, and query patterns that make answers correct rather than merely plausible.
• Infrastructure and cloud: covering Kubernetes, AWS, and hosts.
• Platform tasks worth delegating: providing programmatic creation of dashboards and notebooks

Prompt templates for common workflows

Alongside the skills, the repo hosts a small set of prompt templates you can use as structured starting points to invoke the right skills for specific tasks. These save teams from having to design their approach from scratch and make outcomes more consistent across agents and users.

Current templates include:

• Performance regression: walks the agent through comparing RED metrics before and after a deployment, correlating any regression with distributed traces, and summarizing the root cause.
• Daily standup: pulls the last 24 hours of problems, deployment activity, and notable anomalies for a team’s services, so anyone can walk into a standup with the relevant production context already framed.
• Troubleshoot a problem: takes a problem ID and guides the agent through root-cause analysis, including affected entities, correlated events, relevant logs and traces, and creates a structured summary for the incident channel.

These are a starting point, not a ceiling, designed to be forked and shaped to your team’s on-call runbooks.

What Dynatrace for AI is and what it isn’t

Skills and prompts are a knowledge and workflow layer. They don’t connect to your Dynatrace environment, define what actions your agent can take, or set guardrails. That’s the job of the tool you pair them with and your Dynatrace permission model.

The quality of what your agent can produce also depends on the entities your environment is instrumented to capture. Skills help agents ask better questions of data, but they don’t control what data is collected.

Think of this skill as onboarding a smart new hire who already knows software, but needs to learn your platform. The skills are the platform user guide; your observability data is the work itself.

Get started

It’s super simple to install the skills and prompts in one go. Just run:

npx skills add dynatrace/dynatrace-for-ai

…or activate the skills as a Claude Code plugin:

claude plugin marketplace add dynatrace/dynatrace-for-ai
claude plugin install dynatrace@dynatrace-for-ai

Make sure your agent can reach Dynatrace, then try a real agent-skill task. A few good example starting prompts:

• “Compare the error rate of the checkout service over the last hour vs the same hour yesterday.”
• “Are any pods in the production namespace restarting or getting OOM-killed right now?”
• “Use the performance-regression prompt to check the deployment I just shipped.”

The difference in output quality is immediate: fewer corrections, cleaner queries, and answers that accurately reflect how Dynatrace continuously models your environment in real-time.

The Dynatrace for AI project is open source and actively developed. Issues, discussions, and pull requests are all welcome, especially from teams running agent skills against real workloads. We’d love to hear from you.

The post Dynatrace for AI: Teach your AI coding agent how to use Dynatrace appeared first on Dynatrace news.

Dynatrace is the first AWS Partner to integrate as an MCP target for Amazon Bedrock AgentCore Gateway, marking a significant milestone in how AI agents interact with enterprise observability systems. This integration demonstrates how the model context protocol (MCP) allows AI agents to securely access real-time system intelligence, unlocking new automation and decision-making capabilities. For developers building agentic AI workflows, this integration opens new possibilities, from querying live observability data and detecting anomalies to empowering agents that proactively respond to system issues with comprehensive monitoring insights.

As organizations adopt agentic AI to automate complex workflows, AI agents increasingly need real-time context to make reliable, safe, and efficient decisions. Amazon Bedrock AgentCore provides powerful capabilities for building and orchestrating agents. The new integration with Dynatrace MCP Server gives agents direct access to the deep, real-time observability signals they need to understand system health, anomalies, dependencies, and performance trends.

Dynatrace unifies metrics, logs, traces, problems, topology, and causal context into a single, real-time source of truth for human operators. With our new Amazon Bedrock AgentCore Gateway + Dynatrace MCP Server integration, your agents can now access these same high‑quality signals programmatically through the MCP.

This gives Bedrock agents continuous visibility into system behavior, allowing them to reason with real production data, detect issues early, validate assumptions, and act intelligently within automated workflows, all through a simple, lightweight integration that includes a Dynatrace environment, a Bedrock account, and a few configuration steps in AgentCore Gateway.

Give your Bedrock agents real-time system awareness

With this integration, agents access high‑quality, causal, real‑time signals that they can query directly through the AgentCore Gateway, enabling them to operate with full environmental awareness. Your agents can access:

• Real‑time service metrics (latency, error rates, throughput, resource consumption)
• Live problem and anomaly feeds (issues detected by Dynatrace Intelligence with full causal context)
• Distributed traces and end‑to‑end execution paths (span data, timing, dependencies)
• Dependency and topology information (Smartscape® entities, relationships, and service maps)
• Logs and event streams (structured logs, events, audit information)
• Entity metadata and health states (services, processes, hosts, cloud resources, statuses)

These signals allow agents to ground their reasoning in real production conditions, allowing for faster diagnostics, proactive adaptation, and more intelligent workflow automation.

What this looks like in practice

Once connected through MCP, your Bedrock agents can request Dynatrace insights using simple natural‑language instructions. For example:

• “Show me any active problems in my environment.”
• “Retrieve the latency for my checkout service over the last hour.”

Use the Dynatrace MCP Server to access insights from Dynatrace Intelligence directly within your agent workflows.

Bringing it all together: the integration architecture

The integration architecture consists of four primary components working together:

1. AI agent: Your AI agent running on Amazon Bedrock, equipped with access to tools and knowledge bases. When the agent needs observability data to make decisions, it invokes the AgentCore Gateway.
2. Bedrock AgentCore Gateway: The AgentCore Gateway acts as the orchestration layer that manages connections to multiple MCP servers. It manages the complexity of MCP protocol handling, request routing, and response aggregation, allowing your Bedrock agents to focus on decision-making logic by receiving requests from Bedrock agents and routing the requests to the appropriate targets—including your Dynatrace MCP server.
3. Dynatrace MCP Server: A specialized MCP server that exposes Dynatrace observability capabilities through standardized MCP protocols. It provides secure, real‑time access to Dynatrace Intelligence and returns structured observability data to the agent.
4. Dynatrace environment: Your organization’s Dynatrace environment, providing root cause analysis, optimization recommendations, and comprehensive observability data across your infrastructure, applications, and services.

How to connect Amazon Bedrock AgentCore to Dynatrace via MCP

This integration is intentionally lightweight and can be set up in minutes. You only need:

• A Dynatrace environment (SaaS)
• An Amazon Bedrock account with AgentCore access
• A few configuration steps in AgentCore Gateway
• (Optional but recommended) A deployed Dynatrace MCP Server

Here’s the high-level setup flow:

1. Set up the Dynatrace MCP Server: Configure your MCP Server endpoint and OAuth 2.0 credentials for secure agent access.
2. Create an AgentCore Gateway: Use the AgentCore starter toolkit, AWS CLI, AWS Console, or SDK to create a gateway with MCP turned on and configure inbound authentication (Amazon Cognito or another OAuth provider).
3. Add Dynatrace as a gateway target: Register your MCP server as a target and map appropriate OAuth credentials and scopes.
4. Connect your Bedrock agent: Connect your agent to the Gateway and verify available MCP tools via listTools.
5. Start querying Dynatrace via MCP: Your agent can now retrieve problems, metrics, traces, logs, and topology directly from Dynatrace.

This integration gives your Bedrock agents direct access to Dynatrace real‑time observability and topology insights, allowing more informed reasoning, safer decisions, and autonomous, context‑aware workflows.

Give your agents direct access to Dynatrace real‑time observability insights. Get started today by exploring the Bedrock AgentCore reference implementation.

The post Fuel Bedrock agents with observability data via the Dynatrace MCP Server appeared first on Dynatrace news.

The whole industry is using and adopting agentic AI. In fact, AI agents are only as effective as the data that powers them. Whether supporting developers through code assistants, accelerating ITSM workflows, automating cloud operations, or enhancing threat detection, you need high-quality data with full context awareness. Only this allows AI to be a trusted and effective partner, delivering reliable recommendations based on a deterministic foundation. The Dynatrace MCP Server, as part of Dynatrace Intelligence, allows AI assistants to interact with Dynatrace and access live, context-rich observability data and reliable AI-insights for smarter, autonomous operations.

Get real-time insights from your digital systems right inside your processes

In traditional IT monitoring, fragmented data and manual tagging were inconvenient. In an AI-native world, they’re a fundamental flaw. AI systems built on incomplete or disconnected data can’t make informed decisions, leading to unreliable recommendations or, even worse, unreliable autonomous action—a scenario that keeps executives on edge.

Dynatrace Intelligence, the agentic operations system built into the Dynatrace platform, solves this with a real-time, deterministic, and context-rich understanding of your entire digital systems.

The Dynatrace MCP Server, as part of Dynatrace Intelligence, provides a secure and governed interface that allows your AI ecosystem to interact with the Dynatrace platform and access data and findings powered by Dynatrace Intelligence. It uses the Model Context Protocol (MCP), an open standard that defines and handles safe interactions between agents, external data sources, and tools.

Together, Grail and Smartscape provide the technical foundation for Dynatrace Intelligence to let agentic AI act on facts, not guesses, ensuring that AI-powered decisions are accurate, scalable, and actionable, a prerequisite for reliable autonomous operations.

By exposing Dynatrace’s unique capabilities, the MCP Server delivers trustworthy, real-time knowledge directly into agentic workflows. This includes:

• Contextualized signals with a holistic understanding of every data point within its full operational and business context, provided by Grail, Dynatrace’s AI-optimized, unified data lakehouse.
• Real‑time topology and causal dependencies, powered by Smartscape, a real-time dependency graph that reveals how systems, services, and cloud components relate and influence one another.
• Deterministic, causal‑AI‑driven root‑cause analysis with correlation to real business impact.

Empower your AI agents to deliver greater value through live production data

You can connect Dynatrace to any MCP client in minutes: no server to deploy, install, host, or maintain. Use the Dynatrace MCP Server to accelerate integration and ensure a smooth transition from pilot to enterprise-scale adoption.

Through the tools hosted on the Dynatrace MCP Server, you can use natural language to query all your data on Grail, check system health, and get problem analyses and remediation recommendations. This allows seamless access to production insights across the applications you already use, including your IDE, Microsoft Copilot, Slack, and automation platforms like n8n. The MCP Server powers numerous more integrations: Azure SRE, AWS DevOps, GitHub Copilot, Atlassian Rovo Ops, AWS DevOps Agent, Kiro, and Amazon Q, to name just a few.

For customers exploring their own tailored solutions, the community-driven local MCP Server is also available. It already sees wide adoption as a flexible way to experiment with new use cases, adapting the MCP to specific needs, and prototyping ideas before moving them into production.

Where the MCP Server delivers value

The Dynatrace MCP Server brings production truth directly into development, operations, ITSM, and business workflows, allowing AI assistants to reason, decide, and act with full contextual awareness. Examples include:

• Development workflows: In-IDE access to live production signals, root causes, exceptions, impacts, and even code-level stack traces, for example, GitHub Copilot querying Dynatrace data in natural language to validate changes and accelerate fixes, with no tool switching required.
• Operations & SRE: Real-time context for accelerated triage, proactive remediation, and evidence-rich postmortems, for example, the Azure SRE Agent integrates with the MCP Server to diagnose anomalies and trigger automated responses.
• ITSM & Incident Management: Enriched tickets with topology, dependencies, business impact, and AI-driven recommendations, for example, Atlassian Rovo Ops delivers end-to-end, context-aware incident management.
• Business Intelligence: Live production insights for product, sales, support, and process teams to link system behavior to customer and business outcomes, provided through MCP clients like Slack, Microsoft Copilot, or ChatGPT.

The Dynatrace MCP Server is now generally available

Dynatrace Intelligence provides insights grounded in real-time topology, deterministic causal analysis, and deep semantic understanding of your environments. Every recommendation is rooted in precise, explainable, and actionable production truth, giving AI systems the reliable foundation they need to operate safely and autonomously.

The Dynatrace MCP Server brings contextualized observability data and insights from Dynatrace Intelligence directly into your broader AI ecosystem.

Connect Dynatrace to any MCP client and start using it in minutes.

You can work with widely available MCP clients such as Microsoft Copilot or ChatGPT, integrate the Dynatrace MCP Server into your ITSM and incident management workflows through ServiceNow or Atlassian Rovo, bring production context directly into VS Code or other IDEs, or orchestrate automated actions through workflow tools like n8n or Copilot Studio.

Explore real-world examples in our latest blog post on the AI ecosystem, or explore all Dynatrace AI ecosystem-related announcements.

For details on configuring the Dynatrace MCP Server, go to our documentation.

The post Dynatrace MCP Server: Empower your AI assistants to interact with Dynatrace and access live production insights appeared first on Dynatrace news.

In today’s cloud-native environments, having real-time observability data at your fingertips is crucial. By integrating Kiro powered by AWS with Dynatrace, you can leverage powerful AI-assisted monitoring and troubleshooting capabilities directly in your development workflow.

Kiro—which recently reached general availability—helps developers by bringing structure to AI coding with spec-driven development. When a developer needs to fix an issue, investigate an error, or optimize resource usage, it’s crucial they can analyze what happened just before the issue occurred and delve deeper into the infrastructure utilization of your applications in your cloud or container environment.

Unlock development productivity with live production insights

Developers typically face restricted access to production environments, being fully dependent on site reliability engineers (SREs) or operations teams to detect and report issues post-deployment, and provide them with the necessary information to fix an issue. This segmented workflow can result in delayed problem identification and resolution, an increased risk of failures in production, and reduced efficiency throughout the development lifecycle.

By connecting Dynatrace with Kiro, developers can access real-time insights from production environments, gain contextual information down to the root cause of an incident, and receive remediation proposals—all within their Kiro environment.

Kiro has a built-in Model Context Protocol (MCP) client that can be used to extend its capabilities to communicate securely and flexibly with external data sources and tools such as Dynatrace.

Let’s dig deeper into how to leverage this capability and provide Dynatrace’s unique insights to your development teams.

Step-by-step integration guide

Prerequisites

• You’ll need a Dynatrace account. If you don’t already have one, you can start a free 15-day trial.
• Kiro must be installed on your system.
• You must have basic familiarity with AWS services and the Dynatrace platform.

Prepare integration with Dynatrace

First, you need to create a Dynatrace Platform Token, which is used to define Kiro access, and then add the required permissions for the Dynatrace MCP server.

Configure Kiro MCP Settings

The Kiro MCP configuration is managed through a JSON file. The interface supports two levels of configuration:

• User-level: ~/.kiro/settings/mcp.json applies to all workspaces
• Workspace-level: .kiro/settings/mcp.json is specific to the current workspace

You can apply the configuration using two different methods:

Method 1: Open the command palette (use Cmd + Shift + P on Mac or Ctrl + Shift + P on Windows/Linux), search for MCP and select Kiro: Open workspace MCP config (JSON) or Kiro: Open user MCP config (JSON), depending on whether or not you want to configure the settings for the workspace or user level.

Method 2: Alternatively, you can use the Kiro Panel. Open Kiro and select the Kiro ghost icon to open the left-side panel. Locate the MCP SERVERS section, select  Open MCP Config, and then start configuring the connection for the Dynatrace MCP Server.

Dynatrace specific settings

Note: Only add one of the following configurations, depending on whether you want to use the remote MCP server or the local MCP server. You can’t use both at the same time.

Using the remote MCP server

Use the following configuration. Replace $TENANT_ID with your Dynatrace environment ID. (You can find your environment ID in the URL of your Dynatrace environment — for example, https://<ENVIRONMENT_id>.apps.dynatrace.com/ui.) Then, replace $DT_PLATFORM_TOKEN with the ID of the Dynatrace platform token you created previously (for example, dt0s16.XXXXX).

{ 
"mcpServers": 
  { 
    "dynatrace": {
      "type": "http",
      "url": "https://$TENANT_ID.apps.dynatrace.com/platform-reserved/mcp-gateway/v0.1/servers/dynatrace-mcp/mcp",
      "headers": {
        "Authorization": "Bearer $DT_PLATFORM_TOKEN"
      },
      "tools": ["*"]
      }
  }
}

Connect the local MCP server

The configuration for the local Dynatrace MCP server can be added to the Kiro IDE using one-click installation or by following the manual configuration as shown below. Don’t forget to replace $TENANT_ID with the ID of your tenant.

{
  "mcpServers": {
    "dynatrace-mcp-server": {
      "command": "npx",
      "args": ["-y", "@dynatrace-oss/dynatrace-mcp-server@latest"],
      "env": {
        "DT_ENVIRONMENT": "https://$TENANT_ID.apps.dynatrace.com"
      }
    }
  }
}

Verify the integration

Once configured, you can use the Kiro chat to interact with Dynatrace through natural language conversations. Simply tell Kiro what you need, whether it’s investigating a critical incident, gaining insights into metrics, logs, or traces from your application, analyzing dependencies, or setting up automated alerts.

In the screenshot below, you can see in the lower left which capabilities are provided by the Dynatrace MCP Server. Beyond the standardized actions, such as listing active vulnerabilities or problems, querying data stored in Dynatrace, or creating a workflow, you can also interact with Davis CoPilot®, the Dynatrace natural language assistant.

Conclusion

This integration isn’t just another feature; it’s a fundamental shift in how Dynatrace integrates with your development workflow. It brings together the power of Kiro’s AI capabilities with the Dynatrace unified observability platform, allowing developers to access critical monitoring data and gain a real-time understanding of their production environments via natural language interaction.

Spend less time context switching and more time creating value for your customers. Start today and benefit from real-time insights, precise root cause analysis based on causal understanding or improved troubleshooting capabilities, and enhanced development workflows.

Explore how Dynatrace can integrate seamlessly into your development landscape using our remote MCP Server. If you’re interested in learning more about Kiro, have a look at their launch blog post or visit the documentation and dig deeper into how to connect with MCP Servers.

The post Real-time insights: Leverage Dynatrace observability capabilities within Kiro powered by AWS appeared first on Dynatrace news.

The integration of Dynatrace with Microsoft Azure SRE Agent establishes a new benchmark for cloud operations by leveraging AI-based root cause analysis and real-time production insights, alongside a comprehensive understanding of complex, large-scale IT environments. You can leverage the combined strengths of Dynatrace and Microsoft, enabling teams to resolve complex problems in large-scale IT environments more quickly and efficiently, and automate incident remediation, moving one step closer to driving autonomous operations across their complex environments.

In today’s cloud-first world, reliability isn’t just a goal; it’s a competitive advantage. As more services move online and LLM-powered assistants evolve into autonomous agents, maintaining the reliability, scalability, and cost-efficiency of critical systems becomes essential.

That’s why Dynatrace and Microsoft teamed up to integrate Dynatrace® AI-powered observability with the Azure SRE Agent. This collaboration allows site reliability engineers (SREs) to ensure seamless operations while proactively planning for future scalability and reliability requirements.

Transform your incident management through the combined capabilities of Azure SRE Agent and Dynatrace AI

Azure SRE Agent, introduced earlier this year, provides SREs and developers with the tools they need to increase the speed and efficiency of incident responses, diagnostics, and collaboration, allowing them to resolve problems quickly.

Seamlessly integrated with incident management tools such as ServiceNow, as well as the developer ecosystem, represented by GitHub Copilot or Azure DevOps, the agent runs in the background 24/7, learning and monitoring the health and performance of your cloud environment.

As a reliability assistant, Azure SRE Agent supports teams by efficiently diagnosing and resolving production issues. You can ask the agent questions in natural language, easily access clear and concise problem summaries, and coordinate incident workflows with integrated human-in-the-loop approvals.

Dynatrace enhances Azure SRE Agent’s troubleshooting and automation capabilities with advanced observability insights. By mapping topology, data, and business context, Dynatrace gains a comprehensive understanding and delivers production-accurate visibility across your entire IT system. This visibility feeds Dynatrace deterministic AI, allowing precise root-cause identification and impact analysis. All these insights are now seamlessly supplied to the Azure SRE Agent, equipping it with real-time production context and reliable root cause analysis.

This allows your teams to move beyond simply receiving alerts; teams are now provided with AI that acts, guides safe mitigations, and accelerates resolution within Azure-native workflows.

Gain efficiency across every stage of the incident lifecycle

Using the Model Context Protocol (MCP), the Azure SRE Agent is securely connected with Dynatrace. Whether a team member uses the agent to ask questions in plain natural language, or the agent interacts with Dynatrace directly—sharing insights, asking for real-time observability data, or root cause analysis, together with remediation steps—the close collaboration supports use cases across every stage of incident management, allowing you to:

• Cut MTTR by automating routine runbooks and diagnostics, with safe, approved mitigation actions based on full context.
• Reduce security risk by triaging vulnerabilities faster with production evidence, triggering guided fixes, and validating outcomes.
• Accelerate delivery with contextual GitHub issues and PRs that include root cause, blast radius, and tests, minimizing issue reproduction time and rework.
• Improve fix accuracy by correlating Azure and Dynatrace telemetry for precise root-cause and impact analysis.
• Prevent incidents before they happen using real-time signals and historical trends to stop regressions and reduce toil.

Illustrating the value: Proactively detect and remediate security vulnerabilities

Let’s take a look at a concrete example, which we presented at Microsoft Ignite. Imagine you run a Java-based payroll app on Azure, and a new security warning (CVE) appears. Every second matters now, and there’s no room for error: you need the issue fixed quickly, without lots of back-and-forth between teams.

• Once the vulnerability is detected, Dynatrace automatically identifies the library that caused the vulnerability, opens a GitHub issue containing all relevant information, such as which parts of your app are affected, and informs Azure SRE Agent.
• The SRE agent reviews the GitHub issue and requests additional information from Dynatrace via the MCP server, such as the number of users affected, how often it happens, which endpoints are involved, or which customers might be affected, to assess the scope and impact of the vulnerability.

• After gathering all necessary details, the SRE Agent synthesizes the information and creates a new GitHub issue, assigning it to GitHub Copilot for remediation.
• GitHub Copilot then takes action by updating the configuration and code in the GitHub repository to resolve the vulnerability automatically.
• The pull request not only includes the necessary version changes but also includes documentation, highlighting all findings as well as how the issue was remediated, along with unit tests, to prevent the issue from recurring.

Try the power of Agentic AI for incident resolution

Dynatrace delivers deep, causation-based insights into your live systems, now seamlessly integrated with Azure SRE Agent to elevate your incident management. With this integration, you can unlock:

• Smarter detection and remediation: Deep contextual observability from Dynatrace, correlated with Azure telemetry, enhances issue identification and resolution across complex environments.
• Automated operations: Routine runbook actions and diagnostic workflows can be automated, reducing mean time to repair and freeing teams to focus on innovation.
• Proactive reliability: Continuous analysis of real-time and historical data identifies leading indicators of failure, allowing teams to prevent incidents before they impact customers.

Azure customers can now access Azure SRE Agent directly in the Azure portal. To connect Dynatrace with the agent and learn how to set up Dynatrace MCP Server, see Dynatrace Documentation.

The post Boost cloud reliability: Dynatrace and Azure SRE Agent unite for autonomous operations appeared first on Dynatrace news.

The new Dynatrace integration with Atlassian solves this by embedding real-time production insights directly into incident management processes. Teams gain instant visibility into what’s happening, who’s impacted, and the actions required to resolve issues faster — all without the need to switch tools.

Dynatrace uniquely detects problems in real time by understanding topology, data context, and dependencies across your entire digital ecosystem. Incidents are automatically tied to underlying root causes, giving teams a complete, production-accurate, “live” picture of problem details, severity, and impact.

Dynatrace insights are now accessible in Jira Service Management through human-readable summaries generated by Atlassian Rovo. By bringing production context directly into Jira, Confluence, and Jira Service Management, you’ll accelerate response times and significantly reduce mean time to resolution (MTTR).

At Dynatrace, context is our mantra, sitting at the core of everything we do. This means more than just data enrichment: Every piece of data is automatically contextualized, and dependencies are mapped to reveal the full picture. However, context also means delivering the right data exactly when and where you need it. To do just that, Dynatrace is bringing these insights directly into Atlassian. This is not just limited to IT service management (ITSM). You can get access to contextualized insights directly within an IDE as described in our latest blog post about the new Dynatrace  MCP Server.

Diagnose faster with context from production at your fingertips

Most incident tickets land on an engineer’s desk with little more than a timestamp, a vague description, or a user complaint. They rarely reveal the severity of the issue, which systems are affected, or what might be causing it. This lack of context in an ITSM workflow forces teams to spend unnecessary time digging through monitoring dashboards, chasing logs, or switching between tools just to piece together the basics of the problem.

Instead of getting frustrated, you can now instantly ask the Rovo Ops agent to identify anomalies that occurred around the incident timeframe. The agent queries Dynatrace via our MCP Server and returns the findings directly in the same browser window.

Having contextual details and alerts available directly in the ticket context means you gain immediate clarity into health, what’s wrong, the impact, and the evidence. This leads to faster diagnosis and quicker recovery, while also reducing unnecessary escalations of already-known or related issues, ensuring internal resources aren’t tied up with redundant work.

Remediate smarter with AI-driven root-cause analysis and automation

Once an incident is identified, the Rovo Ops agent utilizes Dynatrace production insights, which accelerate triage and root-cause analysis for incident managers, pinpointing the actual root cause in real time and delivering a higher level of insight and accuracy.

Rovo can now pull in Dynatrace Causal AI insights, including the precise root cause and blast radius of the issue, and combines these with Jira Service Management incident and change history. With Dynatrace contextual intelligence, Rovo delivers fact-based, AI-generated problem summaries and clear remediation recommendations, outperforming the guesswork of pure GenAI approaches.

From this point, just follow the remediation recommendation and trigger a suggested automation action in Jira Service Management, or ask follow-up questions for clarification.

Learn for the future with automated post-incident reviews

The job isn’t finished after an incident is mitigated and marked resolved in Jira Service Management, as you still need to capture what happened and determine how to prevent its recurrence. Instead of spending hours on manual write-ups, Rovo automatically triggers the post-incident review (PIR) process.

In the auto-generated PIR, Rovo surfaces all of the relevant details and history, from the root cause to detected anomalies, all of which are enriched by Dynatrace AI-driven insights. This provides a complete, time-ordered view of the incident, which is combined with Jira Service Management context attributes like assignees, tags, outage duration, and related change logs. With this context, the agent generates a draft PIR. Inside the PIR, you’ll find monitoring charts showing the status before, during, and after the incident, a clear summary of the cause, and a pre-filled prevention plan. All that’s left for you to do is review, refine, and finalize the PIR.

The automatically documented PIRs act as built-in retrospectives, helping teams continuously mature their operations. They also feed insights back into Rovo to sharpen its future recommendations.

Transform how you work, beyond incident management

These are just a few examples of what’s now possible through the extended Dynatrace + Atlassian integration. We’ll continue to explore deeper integrations to make your troubleshooting journey even more efficient in the future.

Imagine directly following up on investigations from within Rovo, with seamless drill-downs into Dynatrace® Apps, or surfacing related post-mortem information and runbooks stored in Jira or Confluence to SREs when investigating an issue in Dynatrace.

And the potential impact goes well beyond incident management. By bringing reliable, real-time production truth into daily workflow and connecting that truth directly to business outcomes, more teams and roles can fundamentally transform the way they work, harnessing the full power of agentic AI.

• Get instant release validation: Developers can query Rovo for pre- and post-deployment failure rates, SLOs, and outcome metrics, allowing them to release with confidence, roll back faster when needed, and validate hypotheses with real data.
• Make decisions based on outcomes: Product managers can ask Rovo or Davis CoPilot® to analyze the impact of a new feature or release by investigating KPI shifts such as user engagement or a drop in check-outs.
• Speed up triage based on business impact: Support engineers working on Jira tickets see Dynatrace insights related to the root cause, blast radius, affected applications, and services. These insights are enriched with further details on user and business impact, allowing engineers to perform instant impact analysis before assigning tickets.
• Run smarter daily stand-ups: Development teams receive ready-made summaries, including exceptions, user analysis, and deployment reports from the last 24 hours, providing relevant insights into what’s actually happening in production.

Start benefiting from deeper integrations with Dynatrace as your trusted foundation for agentic AI

Dynatrace delivers a deep, causation-based understanding of your live digital systems, providing the precise, reliable insights that enterprises can trust as a foundation for agentic AI.

Ready to see how Dynatrace and Atlassian work together and benefit from adopting agentic AI concepts? Then dig deeper into the new possibilities using our remote MCP Server and experience how real-time production context makes your operations more efficient.

See our documentation to learn more about how to connect the Dynatrace MCP Server.

The post Dynatrace and Atlassian deliver agentic AI that transforms end-to-end incident management appeared first on Dynatrace news.

AI, especially AIOps, has emerged as a pivotal solution, promising to avoid downtime. The 2024 State of AI Report highlights this trend, with 89% of technology leaders anticipating that AI will significantly enhance incident response by learning to automate and optimize various tasks, such as performance monitoring and workload scheduling.

AIOps can identify and address potential issues before they become major incidents by learning from history and analyzing large amounts of data in real time. This approach improves operational efficiency and resilience, though it’s not without flaws. The complexity of IT environments and the changing nature of threats necessitate human oversight and ongoing adjustment of AIOps systems to handle unforeseen challenges and ensure optimal performance. Additionally, predictions based on historical data are reactive, solely relying on past information to anticipate future events, and can’t prevent all new or emerging issues. This limitation highlights the importance of continuous innovation and adaptation in IT operations and AIOps strategies.

“The shift from reactive to preventive operations represents the next evolution in AIOps.”
Bernd Greifeneder, CTO Dynatrace

When Dynatrace set out with Davis® AI over 10 years ago, pioneering AI-driven operations, we focused initially on problem identification before moving on to problem remediation. The next milestone in enhancing the capabilities of Davis AI—another pioneering step forward in AI-driven operations—is outright problem prevention. In this blog post, we explain how the unique combination of causal, predictive, and generative AI—augmented by the latest Davis AI advancements—is transforming how Dynatrace customers manage and optimize their IT infrastructure.

Automatic root cause detection

Modern, complex, and distributed environments generate a substantial number of events. This necessitates additional requirements such as minimizing the total number of issues, eliminating false positives, and conducting accurate root cause analysis.

Dynatrace has a longstanding reputation for accurately analyzing root causes and identifying related events. While other methods typically rely on mere correlation and historical data analysis, we’ve further enhanced our capabilities by implementing causational analysis, which leverages contextual information automatically gathered during data ingestion and processing in addition to historical data analysis. This is achieved using Dynatrace Grail™, our causational data lakehouse, which unifies all data in an always-up-to-date topology model. By applying causal AI to incoming data in real time, Davis instantly learns and continuously adapts to new information. This facilitates more precise root cause analysis and anomaly detection, including identifying seasonal anomalies and establishing auto-adaptive thresholds.

When applying this Davis root cause detection within our own IT environment, Davis effectively filters out over 99.9% of incoming data noise, condensing hundreds of thousands of daily system events into no more than four or five incidents that require attention from our IT operations team.

These algorithms are not limited to monitoring IT environments. At our February 2025 Dynatrace Perform session on exploratory analytics with AI-driven insights, the Performance Engineering Lead of XXXLutz—one of the world’s largest furniture retailers operating more than 370 stores across Europe—explains how XXXLutz utilizes Davis AI to proactively identify critical order drops, allowing them to respond quickly and effectively to changing market conditions and ensuring that their business remains agile and responsive to the needs of their customers.

Problem journey and reactive remediation

At the core of Dynatrace problem remediation stands the Problems app—an optimized view into opinionated insights, details, and context of each detected issue—for Operations, SREs, and developers. It filters billions of log lines, including the topology of each incident and its affected entities, for efficient problem triaging and troubleshooting, resulting in a 56% faster mean time to repair (MTTR) for critical incidents.

With the latest release, we drive this further by improving the automatic connection of relevant log and trace data for further drill down, presenting the full context of an issue in a single view. This provides comprehensive visibility into even complex architectures, simplifying the process of examining relevant details and addressing code-level issues, reducing 100 clicks and manual filtering to a single click with no loss of context.

By utilizing Davis CoPilot™, you can conduct comparative analyses of multiple issues, obtain natural language summaries of individual problems, and receive contextual recommendations along with specific remediation steps.

You can also link troubleshooting guides created in Notebooks to remediated issues, thereby building an intelligent knowledge base. Davis automatically connects additional documents as well as stored workflows. So the next time a similar problem arises, Davis brings up related guides, enabling teams to learn from previous experiences and reducing the risk of knowledge loss.

Please refer to our recent blog posts for more information on utilizing Problems for AI-driven insights and the latest Davis CoPilot advancements.

Automating the remediation

While obtaining comprehensive insights is beneficial, true transformation occurs through the use of tools that automatically execute remediation steps. To implement these “AI-driven operations,” it’s essential to forecast future requirements, including capacity demands, potential system failures, and security incidents.

Traditional forecasting engines typically depend on historical data, stored in metrics. In contrast, Davis AI generates real-time predictions, facilitating proactive operations. This capability is due to Davis’s ability to process raw data, such as logs, for forecasting, leveraging Grail to execute previously unattainable queries.

Consider the following scenario: You begin by retrieving and analyzing logs to identify relevant values for automation. Once this task is complete, you proceed to your pipelining tool to configure ingestion rules that extract these values into metrics and then wait several weeks for your prediction engine to generate alerts that can serve as triggers for your workflows.

However, when utilizing Dynatrace with its integrated anomaly detection and forecasting capabilities, you gain the advantage of schema-less data analysis and the ability to process any raw data into time series in real time. This significantly reduces the time required to establish AIOps workflows from several weeks to less than 30 minutes.

Preventive operations

The complexity of modern software environments makes it challenging to determine a service’s reliability solely through testing. It’s impractical to emulate scenarios such as generating a million tickets to assess performance capabilities. This necessitates real-time insights and operations rather than reactive problem-solving or raising alerts to notify personnel.

Preventive operations address this need by enabling proactive corrective actions before issues arise, akin to predictive maintenance. AI-supported anomaly detection identifies parameters that deviate from the norm, allowing for automatic configuration adjustment to mitigate potential problems preemptively.

Davis CoPilot combines the “power of three”:

• Davis causal AI for identifying anomalies and root cause analysis
• Davis predictive AI for precise forecasting and determining when to take action
• Generative AI capabilities that perform actions beyond simply sending notifications or restarting services

In this way, Dynatrace extends AIOps beyond traditional IT operations tasks and addresses complex scenarios, including security use cases such as threat observability. Consider the following real-world example:

At Dynatrace, we log all failed login attempts. We can predict potential threats when abnormal patterns are identified and raise a security event by utilizing seasonal baselining. The subsequent workflow involves checking the IP address and generating a threat score. Upon reaching a certain threshold, a new ruleset is automatically added to the web application firewall. This entire process is fully automated, running before a problem even occurs, significantly reducing the response time from over an hour to a fraction of a second.

In another instance, automatic log pattern analysis crawling our application logs decreased the number of bugs in the production environment by 15% and freed up time previously spent on log analysis and triaging (in pre-prod), equivalent to 17 full-time employees. Consequently, these 17 developers can now dedicate their efforts to adding more value to Dynatrace.

Summary

The State of AI report states that over 88% of technology leaders anticipate AI will enhance incident responses and improve their teams’ ability to predict and proactively resolve service-affecting issues.

With Dynatrace, organizations are prepared to evolve their ITOps and SRE departments from troubleshooting to prevention, getting proactive with forecasting, and utilizing generative AI instead of purely focusing on history-focused root cause analysis.

Start your preventive operations journey with smart automation and auto-remediation that prevents larger issues.

Are you interested in gaining more insights?

• Read about recent advancements in our Problems app
• Get insights into Davis root cause analysis
• Learn more about how Davis CoPilot helps you be more productive
• Or dig deeper into how to utilize Davis forecasting capabilities within Dashboards

The post Advancing AIOps: Preventive operations powered by Davis AI appeared first on Dynatrace news.

An earlier blog post introduced how Akamas helps optimize Kubernetes clusters without “breaking the bank.” As one of the first Dynatrace partners, Akamas used its domain expertise and unique product capabilities to build a custom app on the Dynatrace platform. The app empowers platform engineering teams with insights into infrastructure health, FinOps, and security combined with alerting and automatic lifecycle management. In this way, the app reduces costs and improves the reliability and performance of all Kubernetes applications.

For example, the Akamas app optimized a Dynatrace-monitored Kubernetes environment, found 22 workloads with reliability issues and 53 workloads with sub-optimal performance, and identified potential savings of about $50,000 per month.

Insights into your Kubernetes environment

Leveraging Dynatrace observability data, Akamas analyzes all available Kubernetes workloads and identifies optimization opportunities such as cost reduction, reliability, and performance improvements.

As you can see in the screenshot below, you get a summary of all optimization opportunities identified in the environment. The table below lists all the individual Kubernetes workloads that can be optimized.

There is a cost reduction opportunity for the notification workload of $2,300 per month and other opportunities related to performance improvements and reliability issues due to misconfigured resource settings.

Optimize your workloads

Select Optimize next to the notification workload to open the Optimize workload page, which offers options for optimizing the workload. Akamas is a goal-driven optimization solution, which means you choose if you want to improve the application’s performance or lower the cost, which in Kubernetes translates to reducing the resource requirements of your containers for CPU, memory requests, and memory limits.

When you manually reduce your container’s resources to save costs, you risk impacting service performance and reliability. To avoid slowing down your apps or—even worse—harming SLOs, you can define constraints that need to be considered, such as response time or error rate. Once constraints are set, Akamas AI considers application-level performance signals so cost-reduction recommendations don’t impact your SLOs.

Before you can start optimizing, you need to define the scope. When you choose Container, Akamas tunes the Kubernetes CPU and memory limits. It also supports full stack optimization, which optimizes JVM parameters such as maximum heap size and garbage collection.

Stay in control: Monitor the optimization process

Returning to the overview, you can switch to the Optimizations tab, which summarizes all your running optimizations, including the optimization created in the previous step.

Select See details for any service to dive into more details about running optimization tasks. Besides showing a short summary, including optimization goals, constraints, and scope, you can dive into cost trends and the relevant SLOs.

In this example, despite the cost going down (from over $80 down to about $50), the application performance was not impacted and stayed well below the defined threshold of 340 milliseconds.

Apply configurations

Akamas can apply configurations automatically or suggest configurations that SRE teams can use for further manual improvements. In the example below, you can see the current CPU and memory limits and the new values suggested by Akamas: change the server.cpu_limit from 1,000 down to 982 millicores and reduce the server.memory_limit by 53 MB. Select the button in the top-right of the Pending Recommendation pane to reveal the kubectl command, which you can use to apply the suggested recommendations.

Summary

The Akamas app helps achieve three critical goals by optimizing Kubernetes application configurations, harnessing the power of artificial intelligence of the Akamas platform, and leveraging the capabilities of the Dynatrace platform:

• Cost reduction: Identify opportunities to trim unnecessary expenses related to Kubernetes workloads. Imagine saving thousands of dollars each month by fine-tuning your resource allocations.
• Reliability enhancement: Pinpoint reliability issues within your Kubernetes environment and ensure your applications run smoothly, minimizing downtime and improving overall system stability.
• Performance optimization: Fine-tune your software stack configurations to maximize performance parameters, ensuring applications meet or exceed their service level objectives (SLOs).

This is a perfect example of how to easily build custom apps on top of observability data stored within Dynatrace and leverage its enterprise-grade platform. Utilize the power of the Dynatrace platform, with its easy-to-use building blocks, to address specific use cases based on your business requirements.

Interested in learning more?

Watch the recording of one of this year’s Perform breakout sessions, where Stefano Doni, CTO and Co-founder of Akamas, and Alois Mayr, Principal Product Manager at Dynatrace, gave a quick intro to how Dynatrace and Akamas work better together, introducing the new Dynatrace Kubernetes monitoring app and how Akamas helps optimize your Kubernetes environment.

Start building your own Dynatrace app to address the specific needs of your users:

• Familiarize yourself with the possibilities for extending Dynatrace.
• Watch a recording and learn how to build your own Dynatrace app in less than 10 minutes.
• Head over to Dynatrace Developer and learn about the tools and technologies of Dynatrace AppEngine.

Are you interested in learning more about Akamas and why Dynatrace and Akamas are better together? Have a look at the Dynatrace Hub, watch a video tour, or contact Akamas directly to book a demo and get more insights into how Akamas can optimize your Kubernetes environment.

The post Dynatrace® Apps showcase: Akamas Kubernetes optimization appeared first on Dynatrace news.

Organizations choose data-driven approaches to maximize the value of their data, achieve better business outcomes, and realize cost savings by improving their products, services, and processes. However, there are many obstacles and limitations along the way to becoming a data-driven organization. The following are some of the most pressing challenges:

• Managing cost and scale of large data. The exponential growth of data volume—including observability, security, software lifecycle, and business data—forces organizations to deal with cost increases while providing flexible, robust, and scalable ingest.
• Understanding the context. With siloed data sources, heterogeneous data types—including metrics, traces, logs, user behavior, business events, vulnerabilities, threats, lifecycle events, and more—and increasing tool sprawl, it’s next to impossible to offer users real-time access to data in a unified, contextualized view.
• Addressing security requirements. Organizations need to ensure their solutions meet security and privacy requirements through certified high-performance filtering, masking, routing, and encryption technologies while remaining easy to configure and operate.

Dynatrace is addressing these challenges with a single, built-in data ingest functionality: Dynatrace OpenPipeline™, the ultimate addition for data-driven organizations.

Introducing Dynatrace OpenPipeline

OpenPipeline is a stream-processing technology that transforms how the Dynatrace platform ingests data from any source, at any scale, and in any format. With OpenPipeline, you can easily collect data from Dynatrace OneAgent®, open source collectors such as OpenTelemetry, or other third-party tools. OpenPipeline then filters and preprocesses that data to manage and reduce costs.

OpenPipeline also includes data contextualization technology, which enriches data with metadata and links it to other relevant data sources. By putting data in context, OpenPipeline enables the Dynatrace platform to deliver AI-driven insights, analytics, and automation for customers across observability, security, software lifecycle, and business domains.

Furthermore, OpenPipeline is a data security and privacy technology that ensures data is collected and processed securely and compliantly, with high-performance filtering, masking, routing, and encryption capabilities that are easy to configure and operate.

OpenPipeline works seamlessly with Dynatrace Grail™ to handle data ingest at an unparalleled scale. During the data ingestion process, OpenPipeline enriches data and data signals with their context and simultaneously interconnects them with Smartscape®, a real-time interactive map that reflects the topology and dependencies of all data signals. This “data in context” feeds Davis® AI, the Dynatrace hypermodal AI, and enables schema-less and index-free analytics. Unlike other pipelining solutions that serve to consolidate and move data from one place to another, OpenPipeline enhances the Dynatrace platform and allows users to extract even more value from their data.

Scale beyond petabytes

Dynatrace can collect data from the full application stack without configuration—including metrics, traces, logs, user sessions, security events, business events, and more. OpenPipeline unifies the ingestion of data sent to Dynatrace from any source in any format. During transport, data is prioritized, compressed and encrypted, ensuring data integrity and protection. Data is then dynamically routed into pipelines for further processing.

Designed to reach beyond petabyte (PB) scale, OpenPipeline will—at its launch—quintuple the ingest throughput from 100 TB a day per tenant to 500 TB a day per tenant. Further scaling, to and beyond 1 PB per day, will be announced in the near future.

This massive increase in data-ingestion throughput is possible thanks to several patent-pending high-performance stream-processing technologies, including a breakthrough in the simultaneous processing of thousands of data-processing rules. OpenPipeline rule processing outperforms most rule-processing algorithms by magnitudes (by a factor of 6-10 compared to prior art), with lower memory consumption and no degradation in data throughput.

Manage the cost of data with ease

With the exponential growth of data, and the need to retain data longer for business and security reasons, it’s important to preprocess and manage data appropriately to maximize value and minimize cost. OpenPipeline high-performance filtering and preprocessing provides full ingest and storage control for the Dynatrace platform. As a result, dedicated data pipeline tools are unnecessary for preprocessing data before ingest.

Filtering data is crucial for privacy and compliance to minimize the exposure of sensitive data. Additionally, it helps to reduce data volume and keep the cost of storing and querying data under control by eliminating duplicates, redundancies, and dropping unnecessary data fields. As an example, in early preview usage, AWS GuardDuty events were reduced by 84% by filtering out security-irrelevant events, which reduced cost and alert noise at the same time.

Transformations preprocess data and reduce data volume further, especially in situations where raw data is not required. OpenPipeline extracts data with context and transforms it into more efficient formats, for example, logs to metrics. Such transformations can reduce storage costs by 99%.

Routing of data to specific Grail buckets of varying retention durations lets you decide which data to keep and for how long. It also separates data organizationally for improved access control and focused query scope.

Protect your sensitive data

• Privacy by design. One essential aspect of OpenPipeline is the ability to mask data at capture using OneAgent® and automatic, rule-based processing. This approach suppresses sensitive data capture entirely before the data leaves the process, service, or customer environment. This ensures compliance and protects sensitive information from the start.
• Commitment to privacy. Dynatrace Trust Center demonstrates the Dynatrace commitment to privacy (and security) by design. Masking personal and sensitive data is of vital importance. With OpenPipeline, Dynatrace users can configure filtering and masking to their specific needs.

Bring data into context for improved analytics, automation, and AI

As OpenPipeline processes data streams in real time and it retains context during data normalization. At the same time, it performs contextual enrichment to ensure high-fidelity analytics, automation, and AI. With OpenPipeline processing power, you can:

• Enrich data and improve its value and quality by adding supplemental attributes (such as IP address geolocation or the related trace ID to a log line).
• Normalize data without losing context, as it detects known data structures automatically and provides optional rules for custom data structures.
• Transform contents into well-defined fields, convert raw data to time series, calculate metrics, or create business events from log lines.
• Converge heterogeneous data sources with ease, as data normalization and contextual enrichment can also happen on read, thanks to the schemaless and indexless approach of Grail.
• Contextualize and map data and get a unified view by identifying and connecting data points to the topology and dependencies within a software environment using the Dynatrace Semantic Dictionary. This contextualization provides Grail with additional semantic information in real time.
• Prioritize business data for a desired quality of service (QoS). Dynatrace provides unmatched accuracy by treating relevant business data (for example, real-time consumption or revenue dashboarding and analytics) with a higher priority and ensuring that data is not only in context but also not dropped or sampled.

Such contextually enriched data is the key to unlocking the full potential of your data for improved analytics, automation, and AI. By adding metadata and linking data to other relevant data sources, you enhance the quality, accuracy, and value of your data.

Davis—the unique Dynatrace hypermodal AI—builds on more than a decade of AI expertise in predictive and causal AI, and as announced recently, generative AI technologies. Automation and business observability require precise results. Contextually enriched data enables unrivaled predictive and causal AI power that provides real-time risk and root-cause analysis, enabling immediate insights and automated, AI-powered IT operations.

OpenPipeline in action

Let’s take a look at a concrete example—a logline that contains the following information:

2024-01-31 15:08:12 INFO [user-service] User john.doe@example.com logged in from 192.168.0.1

OpenPipeline can parse this logline and extract the following fields:

• Timestamp: 2024-01-31 15:08:12
• Log level: INFO
• Service name: user-service
• User email: john.doe@example.com
• User IP: 192.168.0.1

From here, OpenPipeline can convert this log entry into a time series metric that counts the number of logins per service, or create a business event that triggers an alert or notification when a user logs in.

With no schema or indexing, Grail even handles such data normalization and contextual enrichment “on read” as well, which makes it even easier to converge heterogeneous data sources. This additional power is enabled by the Dynatrace Semantic Dictionary, which provides Grail additional semantic information in real-time for the mapping of topology and dependencies within a software environment. Let’s look at another example, where a data source contains the following information:

{

"user_id": "123456789",

"user_email": "john.doe@example.com",

"user_name": "John Doe",

"user_role": "admin",

"user_location": "Linz, Austria"

}

Grail can use the Dynatrace Semantic Dictionary to map the user_email field to the User email field extracted above by OpenPipeline and enrich the data with additional context, such as user_id, user_name, user_role, and user_location. This way, Grail can provide a holistic view of the user and their activities across different data sources.

OpenPipeline is part of the unified Settings experience, inside the built-in Settings app 

OpenPipeline offers an easy way to create and configure routes and pipelines at scale, comfortably situated within the Settings app. This app simplifies the configuration process by utilizing Dynatrace query language (DQL) for matching and processing routes. Of course, configuration-as-code using an application programming interface (API) is also available.

Visual guidance within the user interface is available to administrators for managing rules, setting up ingestion configurations, dynamic routing into individual pipelines, controlling data enrichment and transformation policies, and sending data from a source to its destination (such as specific Grail buckets).

You can read more about setting up and managing pipelines in our documentation. 

Get more value from your data with OpenPipeline

OpenPipeline complements Grail with high-performance stream processing to maximize security and ease the management of large heterogeneous data, while minimizing cost. It manages parallel pipelines to ingest, transport, mask, filter, enrich, normalize, transform, contextualize, route, and persist data. OpenPipeline is available to Dynatrace customers at no additional cost.

• OpenPipeline enables cost-effective and scalable data ingest, with up to 500 TB per day per tenant, with plans to go beyond the petabyte-per-day level in the near future. It also offers built-in data transformation features that reduce storage costs by up to 99%.
• OpenPipeline contextualizes data in real time with enrichment, discovered topology, and tags. Using its patent-pending stream-processing technologies, OpenPipeline optimizes data for Dynatrace analytics and AI.
• OpenPipeline ensures data security and privacy with source-side masking and encryption at the source, additional filtering, and masking at ingest. In combination with Grail, this ensures data privacy and compliance at the storage and query level. Such a centralized data management approach has substantial potential to reduce security and audit efforts.

The first release of Dynatrace OpenPipeline, supporting logs, business events, and generic events, will be released within 90 days. Once available, Dynatrace SaaS customers running on AWS or Azure, and using the latest version of Dynatrace, can start using OpenPipeline without installing anything: the OpenPipeline Configuration app will be pre-installed on all eligible tenants. Existing processing rules for logs and business events will be automatically migrated to new pipelines, so existing customers will benefit from the new OpenPipeline functionality from day one.

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Many organizations attempt to combine tools, products, and do-it-yourself solutions with custom code to fulfill custom use cases that are specific to their unique business requirements and technology stacks. Not only are these approaches difficult and costly to maintain, they also lack proper security and scalability.

Especially when seamless end-to-end solutions are needed, it’s necessary to add relevant business context to data to unlock the value of insights that are hidden in the vast amount of observability, security, and business data derived from modern clouds, and overcome the challenges of data that’s locked in organizational silos.

AppEngine empowers organizations to tame cloud complexity, innovate faster and more securely, and ensure consistently better business results, thus delivering answers and driving collaboration across teams.

Multiply the value of your data with Dynatrace

In contrast to other custom app development solutions and extensibility frameworks that embed third-party interfaces and self-hosted custom code via iFrame, Dynatrace AppEngine enables you to create custom apps tailored to your specific needs that are integrated into the Dynatrace platform and offer a simplified user experience.

AppEngine is a core technology within the Dynatrace platform. It empowers Dynatrace customers to use the same tools and technologies as Dynatrace engineers to develop new tailored apps. App developers have the same limitless possibilities for creating customized analytics and integrations in any IT environment, whether in the cloud or on-premises. Apps enable organizations to leverage a goldmine of observability, security, and business data—and add further value by addressing any imaginable use case.

Boundless possibilities

You now have unlimited possibilities to address any use case you can imagine. Consider a simple interactive app that helps convey the business impact of new product enhancements by combining IT Ops metrics with business data—or an app that not only calculates the costs of your cloud usage but also automatically optimizes it by leveraging the power of Davis, our causational AI, and the new Dynatrace AutomationEngine. Or imagine a vulnerability notification generated by Dynatrace Application Security triggering a custom security app that initiates a fully automated action flow, informs your SecOps team, and creates and assigns tickets to the right people with all the relevant contextualized information. These and an unlimited array of other use cases are now possible with AppEngine.

We’re also releasing a variety of apps that we developed using AppEngine. These will be built into the Dynatrace platform and address use cases ranging from automation to security and business analytics, and include apps such as Smartscape Health View, Carbon Impact, and Site Reliability Guardian.

Dynatrace Carbon Impact is a perfect example of combining data stored in Dynatrace with external data and custom logic to explore and visualize the sources of your IT carbon footprint, regardless of where your workloads run. It measures and reports carbon emissions for all Dynatrace-monitored hosts across your entire environment, including hybrid and multicloud, in a single interface and translates utilization metrics, including CPU, memory, disk, and network I/O, into carbon emission metrics. The app automatically identifies opportunities to reduce carbon emissions, capturing the details needed for analysis and making informed decisions.

From code to app to code

Apps built with Dynatrace AppEngine can take different forms. From full-blown apps addressing end-to-end use cases, like the Carbon Impact app example shown above, to small “components” that focus on a particular task, such as:

• offering integration with external line-of-business (LOB) applications
• connecting apps with external databases, normalizing data for immediate platform-wide usage
• extending the Dynatrace AutomationEngine with new custom actions
• adding reusable UI components to extend Dashboards and Notebooks with new chart types

Once you write an app, you can use it everywhere within the Dynatrace platform: within Notebooks, as part of a workflow running on AutomationEngine, or as a standalone app.

Logic to data: Understanding the value of data at its source

Many data-related requirements, such as GDPR, governance, data quality, or auditing, must be addressed when writing software. These requirements are mandatory from both a performance and a cost perspective.

Analyzing petabytes of data only works if the logic is very close to where the data is stored and processed. Grail, the Dynatrace causational data lakehouse, is built for managing and querying enormous amounts of data from the entire hybrid and multicloud environment. Utilizing the capabilities of Grail together with Smartscape topology mapping and Davis AI, AppEngine understands the IT, security, and business value of data in context. This contextual understanding enables you to do any analysis you need and helps to answer questions that can’t be answered with disconnected data stored in different silos.

Addressing enterprise needs: Security, scalability, and manageability

Traditionally, building apps and integrations on different enterprise software platforms was tough and required a lot of effort. By using Dynatrace AppEngine, developers can focus their time on adding value by meeting the urgent needs of the business instead of managing integrations and runtime infrastructure or addressing security needs. The apps run in the Dynatrace environment, thus automatically meeting enterprise requirements. This provides benefits including:

• auto-scaling runtime environments
• automated delivery and operations
• governance by Dynatrace Identity and Access Management (IAM)
• built-in security capabilities such as code shielding, secure runtime environment, audit logs, SSO, secret vaults, and more
• safe connection for secure remote invocations using EdgeConnect

And all of this comes with full app-lifecycle support, including extensive developer experience for creating, building, deploying, and managing apps.

Data value at scale: Become a data-driven enterprise

Dynatrace AppEngine enables teams to achieve better business results by offering a secure, serverless, auto-scaling runtime environment that makes it easy to create custom, compliant, data-driven apps, that address the use cases that are most important to them.​

Everything thinkable is now possible. With apps delivering answers and driving collaboration across IT, development, security, and business teams, you can multiply the value of your data.

Dynatrace AppEngine will be available in Q2 2023. In the meantime, look for upcoming webinars sharing more insights on the app offering of the Dynatrace platform and how to build your own custom apps. Visit our webpage or follow us on LinkedIn to stay current with upcoming announcements and activities.

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Grail – the foundation of exploratory analytics

Grail can already store and process log and business events. Now we’re adding Smartscape to DQL and two new data sources to Grail: Metrics on Grail and Traces on Grail.

Introducing Metrics on Grail

Despite their many advantages, modern cloud-native architectures can result in scalability and fragmentation challenges. Ensuring observability across these environments requires access to data at a massive scale. The proliferation of metrics can quickly result in a high cardinality challenge, with each service, host, or Kubernetes pod adding its own unique values to the data set.

Grail solves this scalability issue! Metrics on Grail is architected to manage billions of metrics to cope with cardinalities and unique value combinations of 1 trillion potential permutations for timeframes beyond a year. This is only possible because of our no-index approach and massive parallel processing capabilities, which enable Dynatrace to offer extra-long data retention (15+ months) at full granularity that is cost-efficient and fast.

You no longer need to split, distribute, or pre-aggregate your data. Let Grail do the work, and benefit from instant visualization, precise analytics in context, and spot-on predictive analytics.

Introducing Traces on Grail

A distributed trace follows a transaction on its journey through every service, cloud platform, and host in your environment. Having access to traces that span the full hybrid and multicloud stack enables developers to debug their applications in production and understand dependencies in live environments. For more complex cloud-native architectures, adding more services and applications leads to a massive increase in the volume of collected traces.

With Grail, we address these customer challenges by offering the most powerful and future-proof trace analytics solution on the market, which:

• Handles data volumes of hundreds of terabytes a day
• Retains large data volumes for up to 15 months in a highly cost-efficient way
• Ensures that data retains its context by assembling trace spans into PurePath® distributed traces (including additional code and thread profiling data)
• Returns instant query results in real-time using indexless queries

Smartscape for DQL: Context is king

Bill Gates wrote an essay in 1996 entitled “Content is King” in which he described the future of the internet as a marketplace for content. In DevSecOps, content includes applications and services—in addition to information about the environments where they run and the users who use them. Observability and application security use cases rely on data. However, data on its own, without context, doesn’t reveal all its insights. Whereas Bill Gates’ observation is still valid, for the DevSecOps industry today, a more accurate description is “context is king.”

In a traditional monitoring environment, metrics are aggregated data points that lose their context and granularity when data sets are trimmed to make them more manageable. With Dynatrace and Smartscape for DQL, metrics are a completely different game. Whether it’s metrics, logs, events, traces, or any other data type, Dynatrace not only retains the data context but also enables you to analyze data in its semantic context without boundaries.

These capabilities are powered by Smartscape for DQL, a directional graph representing the real-time topology and dependencies of a data architecture. Smartscape unifies the different data types ingested into Dynatrace and retains the full context of this data to enable holistic and precise data analytics.

With the Dynatrace Query Language (DQL), teams can perform these analyses by asking questions that weren’t possible in the past. There are now boundless possibilities, such as identifying users affected by a service outage in a red-alert scenario or doing forensic research on a recent data breach. With DQL, you can easily combine different data types into a single query.

The power of Smartscape is, of course, not limited to manual queries. The same data model fuels Davis, the causational AI engine at the core of the Dynatrace platform. Dynatrace has used Davis for many years and is leveraging its power for root cause analysis, identifying security risks, and many other use cases. Davis doesn’t rely on machine learning or statistical correlations—the models that power most available AIs and try to correlate data points by timestamp analysis, searching for similarities, or processing manual instrumentations. Alternatively, Davis is causal AI that reflects continuously updated topology and dependencies (powered by Dynatrace Smartscape) and understands the precise relationships and dependencies between isolated signals.

Whereas other AIs must guess, Davis knows and eliminates false positives. With the addition of Dynatrace Grail, which ingests, retains, and maintains data in context, we’re revolutionizing the observability industry and extending Dynatrace further to provide answer-driven analytics and automation for unlimited observability and security use cases.

Exploratory analytics – empowering people and data

While data is considered by some to be the new gold, it’s people that still make the difference. Gaining insights from data stored within Dynatrace has traditionally been limited to people within an organization who have specific expertise and training. This is no longer the case.

With the new Dynatrace user experience, we’re introducing new concepts and changing how people across organizations work and interact with data.

New user experience

How many user interfaces have you used that are defined by the data and data types they show rather than the use cases they support? How often have you spent time decluttering or trying to make sense of the information presented on a dashboard? How often have you wished you could interact with data in the same intuitive way you interact with information on your smartphone, quickly switching between visualizations, easily understanding the context behind a spike in a chart or diagram, or digging deeper to perform ad-hoc analysis?

When we started working on Strato—the new Dynatrace design language that powers our new user experience—we developed a few core principles to address the design challenges stated above:

• Designing software for DevSecOps use cases means handling data—large volumes of data that need to be accessible for in-depth analysis in an easy-to-digest interface. We therefore completely rethought the user experience: the interface is user-centric rather than data-centric. We designed Dynatrace in a way that places the user in the middle, offering a flexible UI—tailored to individual needs and deriving rich insights from different perspectives.
• The interface is simple—whether you’re a first-time user, an occasional user, or an SRE using Dynatrace as your single source of truth, the experience is simple and easy to learn.
• The interface offers infinite possibilities. Users need to be able to work efficiently regardless of how large their environments are. Sharing and collaborating with teams is now easier than ever before.

These principles all align with a single, overarching goal: making data and insights derived from analytics available to a wider audience. To achieve this, we designed the new Dynatrace user experience (UX) to facilitate collaboration with teams across organizations—IT, development, security, and business—and solve everyday problems. We focused on democratizing the user interface, making it less trivial and more accessible, and empowering teams to better understand data signals and make data-backed decisions.

User in context

We already mentioned above that putting data in context is vital for Dynatrace. This is also true from a user experience perspective. With Strato, we add user context to the Dynatrace UI.

Charts are now interactive—data points are clickable. Think of a chart that shows a spike in response time because of a deployment two hours earlier—any user can now hover over this data point and begin interacting with the underlying data, whether it’s a drill-down or just the context of the spike.

Introducing Every component and view in the Dynatrace web UI is interlinked based on user context or “intents”. Similar to what you know from your smartphone when sharing an image on your favorite social media channel, when opening a page within Dynatrace, you can easily pass and share the context of your analyses to any other app on the Dynatrace platform. The selection of available apps that are presented is completely context-sensitive and can even be expanded based on your needs.

Simplifying data analytics with Dashboards and Notebooks

In addition to new concepts revolutionizing the overall Dynatrace user experience, we’re introducing two new apps to the Dynatrace platform: Dynatrace® Dashboards, a complete overhaul of the dashboarding experience, and Dynatrace® Notebooks, for on-demand data exploration. These apps make it easier for more team members to explore, visualize, and collaborate on analytics projects. The following principles guided the development of these new capabilities:

• Offer drastically faster and simpler flows, guided by Strato, the new Dynatrace design system
• Fetch all Dynatrace data from one place and even combine it in a single query with Grail
• Integrate external data easily with Dynatrace functions
• Add flexibility and versatile filtering with variables
• Take context with you as you seamlessly navigate the Dynatrace UI using intents

Dashboards and Notebooks have individual strengths that make them the best choice for solving specific use cases.

Observe data with Dynatrace Dashboards

Dynatrace® Dashboards transforms complex data into easy-to-understand visualizations. Dashboards is your go-to app for quick and clear data overviews, whether you need a status-quo view that can be observed over time or you need to share aggregated views with management or business teams.

Dashboards offers an interactive experience with full support for all capabilities and datatypes offered by Grail, allowing you to query not only metrics, but also logs, events, and even external data. It serves as your starting point for further deep-dive analysis, offering more detailed drill-downs via Notebooks (see below).

The all-new Dashboards app is your answer to live data visualization and observation. From data to insights in seconds, and we’re just at the beginning.

Explore data with Dynatrace Notebooks

Dynatrace Notebooks is your on-demand window into data exploration. It addresses the challenge of finding the right data, cleaning, filtering and transforming the data and finally connecting with other data to understand underlying dependencies. You no longer need the help of a data scientist for such tasks—Notebooks enables every Dynatrace user to perform any type of analysis on data stored in Dynatrace.

Start creating data-driven documents and perform custom analytics. Depending on your use case, Notebooks can persist a status quo and create a snapshot whenever necessary or be “self-updating” using current data to always reflect the actual status. You can easily interact with any query result by “slicing and dicing” the data stored in Grail: advanced filters, refinements, aggregations, and sort orders, are just a click away. It’s even possible to harness the power of Davis by adding predictive forecasts to identify future trends with a simple click.

Suppose you need the limitless power of Grail. In that case, you can easily create and edit DQL queries to filter, join, and transform data any way you need it, or even extend Notebooks with custom logic and external data by adding ad-hoc functions powered by Dynatrace® AppEngine.

Whether you’re analyzing new opportunities, the latest vulnerability post-mortem, or your executive production report, Notebooks does it all and ensures that both your query and results are persisted and ready to be shared with your team members. Empowered by Grail, Notebooks is the Swiss Army knife of the Dynatrace platform—built for collaborative data exploration and analysis.

Summary

With these newly added capabilities, Dynatrace users can now perform any custom query, leveraging Dynatrace Grail AI-fueled graph analytics power. This delivers instant and precise answers for an unlimited array of use cases:

• Business impact: quickly identify impacted users by mapping observability and security findings to your business context and improve customer satisfaction by querying for e-commerce customers who are unable to finalize their check-outs due to a service outage.
• Automation: understand the potential impact of remediation actions on dependent components.
• Security: protect customers and brands by conducting application security forensics to identify, mitigate, and prevent data breaches.
• Business process health: show and analyze the health status of complex processes even if dependencies are non-transactional.
• Optimize: enable more efficient multicloud operations by predicting cloud performance and utilization over time to optimize resource allocation based on user needs.

The Dynatrace analytics platform converges security and observability data, enabling cost-effective end-to-end analytics at a large scale with long retention times, in context with your business, thus multiplying your value from data: every imaginable analysis of data in Dynatrace is now possible!

What’s next?

The new Dynatrace user experience, including the newly designed Dashboards, Notebooks, and Dynatrace Grail support for metrics and Dynatrace® Smartscape for DQL, will be available in Q2 2023. Grail support for Dynatrace PurePath® distributed traces will be open for preview in Q2 2023.

In the meantime, watch out for upcoming Observability Clinics and “Ask me anything” sessions covering the main topics of this blog post. You can either view a list of the next webinars on our website or follow us on LinkedIn to stay up to date with upcoming announcements and activities.

The post Expanded Grail data lakehouse and new Dynatrace user experience unlock boundless analytics appeared first on Dynatrace news.

Some time ago, at a restaurant near Boston, three Dynatrace colleagues dined and discussed the growing data challenge for enterprises. At its core, this challenge involves a rapid increase in the amount—and complexity—of data collected within a company.

Existing observability and monitoring solutions have built-in limitations when it comes to storing, retaining, querying, and analyzing massive amounts of data. These technologies are poorly suited to address the needs of modern enterprises—getting useful insights and real value from data beyond isolated metrics. So, there was a need to do something revolutionary. Thus, Grail was born.

From these humble beginnings, Grail was purpose-built with one fundamental goal: making all data accessible—and thus valuable—to provide precise answers in real-time, boost insights gathered by Davis AI and drive automation. This goal isn’t limited to observability efforts. Grail needs to support security data as well as business analytics data and use cases. With that in mind, Grail needs to achieve three main goals with minimal impact to cost:

• Cope with and manage an enormous amount of data—both on ingest and analytics
• Work with different and independent data types
• Put data in context and enrich it with topology metadata

Grail architectural basics

The aforementioned principles have, of course, a major impact on the overall architecture. In its essence, Grail is a data lakehouse. But what does that mean? A data lakehouse combines the benefits of data warehouses with those of data lakes. A data warehouse is purpose-built and optimized for specific use cases, providing valuable insights on structured data and able to handle large data sizes. In contrast, data lakes can handle different types of unstructured and semi-structured data in unknown extent, thus introducing openness and flexibility. This openness is accompanied by reduced data quality which limits the value of analyses run on data lakes.

A data lakehouse addresses these limitations and introduces an entirely new architectural design. This architecture offers rich data management and analytics features (taken from the data warehouse model) on top of low-cost cloud storage systems (which are used by data lakes).

For Dynatrace this is the start of a new journey – with more powerful offerings to come. To enable this architecture and build a data lakehouse, we had to decouple storage from analytics and compute. This decoupling ensures the openness of data and storage formats, while also preserving data in context. Further, it builds a rich analytics layer powered by Dynatrace causational artificial intelligence, Davis® AI, and creates a query engine that offers insights at unmatched speed.

As a result, we created Grail with three different building blocks, each serving a special duty:

• Ingest and process: High performance, automated data collection and processing
• Retain: A storage solution built specifically for observability and security data
• Analytics: Query petabytes in real time and start getting real value out of your data

Ingest and process with Grail

From the beginning, Grail was built to be fast and scalable to manage massive volumes of data. It’s based on cloud-native architecture and built for the cloud. Thus, it can scale massively. This starts with a highly efficient ingestion pipeline that is architectured to support adding hundreds of petabytes daily. To add data to the ingest pipeline, customers can choose between Dynatrace® OneAgent or open source observability frameworks, such as OpenTelemetry, Prometheus, Micrometer, and others.

Dynatrace OneAgent is the only agent that automatically collects data from every tier of your application stack without requiring configuration. Just a few minutes after installation, you get all the performance metrics and log data you need to monitor IT infrastructure of any complexity—from front end to back end. This unique, end-to-end data collection, together with Smartscape® topology mapping, will ensure Grail is fueled with all available data—in context—and ready for manual or AI-driven analytics tasks.

Alongside this data ingest, Grail enables highly efficient processing pipelines to enrich ingested data, extract metrics, or add a privacy filter. Check out this blog post and learn—based on the example of log data—how to derive even more value from data with simple pattern matching and powerful processing rules.

Retain data

Forget about having to choose which data to keep and which to drop and stop getting headaches when managing different storage tiers. Do you remember your organization’s last security breach, sitting in a war room and doing forensic research without effectively identifying the root cause because the data was either dropped at ingest or no longer available? Can you count the number of days you have lost because you have waited for data to get rehydrated?

This scenario is a thing of the past. You don’t need to find a needle in the haystack. Thanks to Grail’s unique architecture, you can afford to ingest and store all your data. Once ingested, Grail ensures that data is retained for up to three years – fully flexible, based on your needs and individual settings – while still being accessible by the analytics layer. Data is available in real time without requiring indexing by our powerful Dynatrace Query Language.

High-performance analytics—no indexing required

Traditional data management and observability solutions rely on schemas and indexes to ensure high-performance analytics. A schema is a fixed definition to organize data in certain fields.

Consider a log event in which the event itself has fields such as error code, severity, or time stamp. An index is a high-performing structure that improves the speed of data retrieval operations. Usually, the index is created on the fields defined in the schema at ingest time. This technique is called schema-on-write.

Schema-on-write worked well for some time, but it has reached its limits. The concept has major disadvantages that are significant when considering today’s ever-growing data complexity and quantity. Data is either not available at all, or you need to invest a lot of effort and wait for hours, or even days, until it is re-indexed.

Grail’s schema-on-read allows IT teams to store data in its native format. That means data preparation occurs when data is queried, not ingested. This provides greater flexibility, as teams can perform any query at any time—on data in its native structure—and are not limited to the fields defined in the schema when the ingest pipeline was set up.

In addition, Grail is also indexless. Whereas indexes promise high-query performance with indexed data, they’re limited in size due to high costs. As a result, data is generally stripped—or dropped—at ingest, to minimize the size of the index. Additionally, data is moved from index to cold storage once the data reaches a certain age. Consequently, teams can’t use cold data for analysis and need, instead, to re-index the data before adding it to a query. This process reduces flexibility as teams need to wait for indexing to finish before they can start a query.

Contrary to these concepts, Grail can query any data ingested and deliver answers in real time.

Thanks to its massively parallel processing (MPP) engine, you can perform any query and retrieve results instantly. Use the power of the Dynatrace Query Language (DQL) to search, parse, filter, sort and aggregate all data stored and available within Grail. Because of its familiar concepts it is easy to learn and master. Other than SQL you can understand the syntax and write your own complex queries, without being a data scientist. On-the-fly parsing is 5-10x times faster than similar approaches, like using regular expressions, and enable any query any time on any size of data. DQL is purpose-built for observability and security use cases. It gives teams maximum flexibility and control for any ad-hoc analysis while enabling AI-powered answers and automation.

What started in Boston finally comes to life in Grail

Dynatrace Grail truly enables modern enterprises. The continued exponential data growth is driven largely by the convergence of observability, security, and business data. Grail is built for such analytics, not storage. It’s your single source for all data, ready for any question, delivering precise and lightning-fast answers, empowering AI-driven decisions and intelligent automation in real time.

Start using Grail! You’ll love digging into your data in an ad-hoc manner while gaining new insights and value from your data. Grail for log management and analytics is available for customers using Dynatrace SaaS on AWS, and is expected to be available for Dynatrace SaaS on Azure or Azure Native Dynatrace customers in early 2024.

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