AI agents can make incorrect decisions without any errors when their data synchronization pipelines break, leading to silent failures.
AI applications consist of a user communication layer, an LLM for generation, and an operating system with an orchestration engine and shared memory.
AI systems rely on two data types: large, complex analytical data for training (OLAP) and small, fast operational data for real-time decisions (OLTP).
A clear definition of key database concepts including Online Analytical Processing (OLAP), Online Transactional Processing (OLTP), and Hybrid (HTAP) systems.
The feedback loop between operational and analytical data is broken by fragile synchronization pipelines, complex governance, and difficult development lifecycles.
LakeBase is a fully managed Postgres database within the Databricks platform that separates storage and compute to solve data synchronization issues.
Separating storage and compute enables autoscaling compute nodes up or down to zero and provides Git-like branching for development and testing.
A practical demo shows how to instantly create a new database branch, perform destructive operations safely, and keep the production environment intact.
Beyond AI agents, LakeBase is useful for reverse ETL to push golden datasets out and for serving data to applications and machine learning models.
AI agents require both operational and analytical data at once, and LakeBase bridges this historical gap by providing a unified Postgres inside the lakehouse.
Jobs that call for the skills explored in this talk.