--- title: Introducing intelligent database observability: from... description: Dynatrace has launched an enhanced Database App featuring an analysis-first observability approach that goes beyond traditional alerting. The platform... canonical: https://daily.dev/posts/introducing-intelligent-database-observability-from-alerts-to-answers-at-scale-bom3uzhsq twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Introducing intelligent database observability: from alerts to answers at scale | daily.dev og:description: Dynatrace has launched an enhanced Database App featuring an analysis-first observability approach that goes beyond traditional alerting. The platform... og:url: https://daily.dev/posts/introducing-intelligent-database-observability-from-alerts-to-answers-at-scale-bom3uzhsq og:image: https://api.daily.dev/og/posts/BOm3UzhSq.png og:image:alt: Introducing intelligent database observability: from alerts to answers at scale og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Introducing intelligent database observability: from alerts to answers at scale **[Dynatrace](https://daily.dev/sources/dynatrace)** · 8 min read · 0 upvotes · 0 comments ## Summary Dynatrace has launched an enhanced Database App featuring an analysis-first observability approach that goes beyond traditional alerting. The platform introduces a multi-layered analysis engine covering configuration, schema, query, execution plan analysis, and health scoring. Key new features include a Health Score (0–100) for each database instance, interactive execution plan visualization normalized across PostgreSQL, MySQL, and SQL Server, query and execution plan insights, configuration insights, and schema warning signals. A walkthrough scenario demonstrates how an on-call engineer with no prior schema knowledge can resolve a critical query performance incident in under 10 minutes using AI-powered analysis and automated index recommendations. The product targets modern engineering teams managing large fleets of databases without dedicated DBAs, including ephemeral instances spun up by AI agents. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What database engines does the Dynatrace Database App analysis engine currently support? The Dynatrace Database App analysis engine is available for PostgreSQL and MySQL, with support for additional database technologies planned. The execution plan visualization also normalizes plans from PostgreSQL, MySQL, and SQL Server into a common tree-diagram format so engineers can review query plans consistently across engines. _Comparing database observability tool coverage across PostgreSQL, MySQL, and SQL Server is easier with sources tracked on daily.dev._ ### How can I fix a slow PostgreSQL query caused by a sequential scan on a large table? Add an index on the columns used in the query's filter conditions so the planner can switch from a sequential scan to an index scan. In one example, a full sequential scan on a 62-million-row table filtered by merchant_id and status was resolved by creating a concurrent composite index on those two columns, cutting average execution time from 4.5ms to 0.08ms. _Developers debugging PostgreSQL execution plans can follow query-tuning fixes like this one on daily.dev._ ## Similar posts on daily.dev - [Database monitoring made easy with: Dynatrace’s AI-powered solution](https://daily.dev/posts/database-monitoring-made-easy-with-dynatrace-s-ai-powered-solution-5tgidwbei) · Dynatrace · 1 upvotes · 0 comments - [Understand, diagnose, and optimize SQL queries: Introducing Grafana Cloud Database Observability](https://daily.dev/posts/understand-diagnose-and-optimize-sql-queries-introducing-grafana-cloud-database-observability-pi0z4qiv4) · Grafana Labs · 51 upvotes · 0 comments - [Using CloudWatch Database Insights to troubleshoot query performance from calling services](https://daily.dev/posts/using-cloudwatch-database-insights-to-troubleshoot-query-performance-from-calling-services-7te5whq9n) · AWS Database Blog · 1 upvotes · 0 comments - [Diagnose and resolve database performance issues faster with Database Investigator](https://daily.dev/posts/diagnose-and-resolve-database-performance-issues-faster-with-database-investigator-luoerjnc2) · Datadog · 1 upvotes · 0 comments - [How AI Transformed Database Debugging at Databricks](https://daily.dev/posts/how-ai-transformed-database-debugging-at-databricks-xdhmgnzkw) · ByteByteGo · 8 upvotes · 0 comments --- Tags: [#devops](https://daily.dev/tags/devops), [#postgresql](https://daily.dev/tags/postgresql), [#mysql](https://daily.dev/tags/mysql) [View this post on daily.dev](https://daily.dev/posts/introducing-intelligent-database-observability-from-alerts-to-answers-at-scale-bom3uzhsq) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Introducing intelligent database observability: from alerts to answers at scale","url":"https://daily.dev/posts/introducing-intelligent-database-observability-from-alerts-to-answers-at-scale-bom3uzhsq","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/introducing-intelligent-database-observability-from-alerts-to-answers-at-scale-bom3uzhsq"},"datePublished":"2026-08-04T20:41:58.402Z","dateModified":"2026-09-14T07:52:51.745Z","description":"Dynatrace has launched an enhanced Database App featuring an analysis-first observability approach that goes beyond traditional alerting. The platform...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/921d8f76c24b6fa95cb6f9125a7066cb?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/921d8f76c24b6fa95cb6f9125a7066cb?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Dynatrace","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Dynatrace","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/3ca5a9897e9748f4a39fa9736389609e","url":"https://daily.dev/sources/dynatrace"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/introducing-intelligent-database-observability-from-alerts-to-answers-at-scale-bom3uzhsq","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"devops,postgresql,mysql","timeRequired":"PT8M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Dynatrace","item":"https://daily.dev/sources/dynatrace"},{"@type":"ListItem","position":3,"name":"Introducing intelligent database observability: from alerts to answers at scale"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/introducing-intelligent-database-observability-from-alerts-to-answers-at-scale-bom3uzhsq#faq","mainEntity":[{"@type":"Question","name":"What database engines does the Dynatrace Database App analysis engine currently support?","acceptedAnswer":{"@type":"Answer","text":"The Dynatrace Database App analysis engine is available for PostgreSQL and MySQL, with support for additional database technologies planned. The execution plan visualization also normalizes plans from PostgreSQL, MySQL, and SQL Server into a common tree-diagram format so engineers can review query plans consistently across engines. Comparing database observability tool coverage across PostgreSQL, MySQL, and SQL Server is easier with sources tracked on daily.dev."}},{"@type":"Question","name":"How can I fix a slow PostgreSQL query caused by a sequential scan on a large table?","acceptedAnswer":{"@type":"Answer","text":"Add an index on the columns used in the query's filter conditions so the planner can switch from a sequential scan to an index scan. In one example, a full sequential scan on a 62-million-row table filtered by merchant_id and status was resolved by creating a concurrent composite index on those two columns, cutting average execution time from 4.5ms to 0.08ms. Developers debugging PostgreSQL execution plans can follow query-tuning fixes like this one on daily.dev."}}]} ```