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Dynatrace Uses AI to Modernize Data Handling and Database Management

Database Management

Business Honor
30 July, 2025

New platform upgrade helps businesses manage, process, and analyze data faster with intelligent automation.

Dynatrace has released important changes to its third-generation AI-powered observability platform, which is intended to assist companies in more effectively handling and managing massive amounts of data. By combining database administration, automation and analytics into a single, powerful solution, these modifications facilitate quicker, more intelligent management.

Grail, a scalable data lakehouse that combines the advantages of data lakes and data warehouses, is the platform's central component. Logs, metrics and business information are just a few of the vast volumes of organized and unstructured data that it enables enterprises to gather, store and analyze in one location. Data administration is updated as a result particularly in cloud contexts.

Dynatrace's combined AI tools allow businesses to do more than just store data. Its technology now supports improved data governance, querying, and reporting by assisting teams in automatically organizing, processing and analyzing data from many sources. The improved log management features are one highlight.

Petabytes of log data may now be automatically indexed and contextually linked to other system data every day by businesses. This speeds up data access and reduces database complexity by doing away with the need for manual indexing or separate tools. Developers and data teams may also utilize natural language queries to engage with data, create dashboards or automate responses all without extensive coding skills. This is a step toward data management that is easier to access and self-service.

With support for cloud-native systems like Kubernetes and continuing, flexible storage solutions (up to 10 years), Dynatrace helps companies in improving their data architecture and reorganization data handling. All things considered, this update proves how AI and contemporary database systems may cooperate to decrease human labor, boost productivity and transform natural data into useful business activities.