Case study
Investment Management Data Lake
How SquareShift built a centralized Investment Management Data Lake on Azure and Databricks for an American multinational investment bank.
A data lake is only as useful as the last mile — if analysts still have to email someone for access, you haven't actually solved the real-time problem.
Data & Analytics Practice Lead, SquareShift
An American multinational investment bank and financial-services holding company.
It needed a single Investment Management Data Lake to unify financial, economic, and research data across every asset class it covers.
- Centralized data lake — consolidates financial, economic, and research data across all asset classes.
- Real-time analyst access — replaces fragmented, delayed data pulls with self-serve exploration.
- Multi-source ingestion — Bloomberg, Reuters, FactSet, S&P, and internal data unified in one place.
How SquareShift delivered it.
The challenge
Financial, economic, and research data for every asset class the bank covers lived in scattered systems, with no single, scalable home. Investment decisions needed real-time access to that data, but the firm had no centralized data lake to support it — historical or incremental.
What we delivered
SquareShift designed and implemented an Investment Management Data Lake on Azure, built on Databricks and H2O Driverless AI. Data from Bloomberg, Reuters, FactSet, S&P, and Sustainalytics was ingested, cleansed, and transformed into one centrally managed store spanning every asset class.
Analysts and data scientists now work from that same governed data lake instead of pulling from fragmented sources one request at a time.
The payoff
The bank now runs on a single, centrally managed data lake instead of scattered financial and research feeds. Analysts get real-time access to build new use cases directly on Azure and Databricks, with data from every major provider already ingested, cleansed, and ready to use.
Where this work sits