Ingest semi-structured data faster and more efficiently with Variant - Now Generally Available
8/3/2026
Introduces the 'Variant' data type for semi-structured data ingestion, improving performance and efficiency within the Lakebase system.
Blogs›Databricks›Lakebase
Lakebase
7
posts
2024–2026
This post details the integration of AI agent orchestration with Lakebase Postgres, enabling simplified management and execution of AI workflows. It outlines how leveraging Postgres within the Lakebase architecture enhances the capabilities for handling complex AI agent interactions and data management. This post specifically focuses on the critical role of R&D data residing in the Lakehouse for these agents, emphasizing the need for efficient data access and processing to improve agent performance and reduce costs.
2026
Backstage with Lakebase, part 3
8/1/2026
This post contributes to the ongoing technical narrative of the Lakebase system, likely detailing specific architectural decisions, implementation challenges, or operational improvements related to its development and scaling.
Convert proprietary code to open ANSI SQL with Genie Code
7/30/2026
Introduces Genie Code, a tool that converts proprietary code to open ANSI SQL, enhancing interoperability and data accessibility within Databricks Lakebase.
NBCUniversal’s Seamless Migration: Unlocking Scalable Analytics with Databricks
7/29/2026
This post describes NBCUniversal's migration of their analytics platform from an on-premises Hadoop cluster to Databricks Lakebase. It highlights the technical challenges and solutions involved in scaling their analytics capabilities, including the adoption of Databricks SQL for improved performance and cost-effectiveness. The migration process involved re-architecting data pipelines and leveraging Delta Lake for data reliability.
Simplify AI agent orchestration with Lakebase Postgres
7/23/2026
This post introduces the simplification of AI agent orchestration by integrating it with Lakebase Postgres. It details how using Postgres as a backing store for AI agent state and metadata within the Lakebase ecosystem streamlines the management and execution of AI agent workflows. The technical depth likely includes schema design for agent states, query optimization for retrieving agent information, and strategies for handling the volume and velocity of data generated by AI agents.
Why R&D Data Belongs in the Lakehouse - and Why Agents Need It There
7/21/2026
This post argues for the necessity of R&D data residing within the Lakehouse for AI agents. It highlights how this data is crucial for agents to perform complex tasks, learn, and optimize their operations, thereby improving performance and reducing costs. The post implicitly extends the Lakebase capability by emphasizing the data requirements for advanced agent functionalities.
2024
Granular Usage Attribution for dbt Pipelines with Query Tags - Cloned
4/21/2024
This post introduces the use of query tags in Databricks to achieve granular usage attribution for dbt pipelines. It explains how these tags are applied to queries executed by dbt, allowing for the association of compute and storage costs with specific dbt models or jobs. The technical details likely involve how Databricks' query engine processes and reports on these tags, and how this data is integrated into the Lakebase system for billing and analysis.