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technologyreview.com·3h ago
Cloud & Infrastructure
The New Stack·Jun 16·5 min read
Databricks wants to merge the two databases every company runs
TL;DR
Many companies struggle with the separation between operational databases and analytical systems, which hinders effective data use. Databricks introduced Lake Transactional/Analytical Processing (LTAP) to merge these two types of databases, enabling AI agents to access and analyze data seamlessly.
✦ Why It Matters
Engineers can leverage LTAP to build more efficient data architectures that support AI-driven applications.
Key Takeaways
- Explore LTAP for unified data management in your organization.
- Utilize Lakehouse//RT for faster real-time analytics.
- Implement branching features for agile data experimentation.
- Consider the benefits of open formats for data storage.
How It Works
LTAP combines transactional and analytical data into a single storage layer, allowing AI agents to access both live and historical data seamlessly. It uses a mirroring technique to ensure that changes in Postgres databases are reflected in real-time within the lakehouse, enabling immediate access to fresh data for analytics.