AI-ready LSEG Data in Databricks

This partnership turns Databricks from a general purpose AI data platform into a finance specific starting point. Instead of a bank or asset manager first buying market data, normalizing identifiers, building pipelines, and checking entitlements before any model work begins, teams can pull LSEG data into the same environment where they already clean data, run notebooks, fine tune models, and ship internal AI agents.1 2 3 4

• The concrete gain is fewer handoffs. LSEG says datasets like Lipper Fund Data and Analytics, Cross Asset Analytics, pricing, fundamentals, and tick history are available natively through Databricks, which means analysts and data scientists can query live finance data without standing up separate ingestion stacks.1 3 4
• Delta Sharing matters because it lets the data provider share governed data without copying it into every customer system. Databricks positions this as secure, open sharing across clouds and tools, which makes financial data distribution look more like software delivery than old terminal or feed based licensing.3 5 6
• This also helps Databricks compete for regulated industry workloads against finance incumbents and rival data clouds. S&P Global has expanded a similar Delta Sharing collaboration with Databricks, showing that financial data vendors now see native distribution inside customer AI workflows as a key route to usage and retention.7 2

The next step is a fuller finance application layer on top of the data layer. As more providers package datasets, notebooks, models, and agent tools directly inside Databricks, the platform becomes harder to replace in capital markets, because the workflow moves from storing data to actually doing research, pricing, surveillance, and portfolio analysis in one place.2 4 6 7