The Hallway Track
8 tracked signals on data-architecture.
Databricks unveils LTAP, combining Lake Base (managed Postgres) and a transactional/analytical engine for real-time agentic data workflows.
“So Lake Base is open source Postgres fully managed by Databricks with separated storage and compute and it's really easy to get started.”
Databricks introduces LTAP, a storage technology unifying OLTP and OLAP on one copy of data.
“We think Altab is H-Tab done right and accomplished the goals of H-Tab without actually having a single query engine for it, but we able to actually unify the storage which is by far the most important part.”
Databricks introduces LTAP architecture to unify OLTP and analytics without fragile CDC pipelines or failed HTAP systems.
“CDC doesn't stand for change data capture. It really stands for continuous data corruption.”
PostgreSQL is becoming the unified 'everything database', replacing fragmented specialized data stacks.
“50% of the developer time, you know, I repeat, 50%, which is pretty much half of the developer time, is wasted in maintaining the pipelines and not really building the new features.”
Neo4j proposes graph representations to give AI agents richer context from lakehouses than queries alone
“context coming in shapes and not necessarily queries”
Databricks LTAP unifies OLTP and OLAP into one open-format data stack for AI agents
“coding agents love simplicity. LTAP is that simplicity boost.”
New Zealand Rugby shifted to an athlete-centric data model so player data follows them across all 150 teams.
“If your data is organized around the business unit instead of the person you actually serve, you could be”
Databricks argues enterprises should build continuously-evolving data services instead of static data products.