About a-team Marketing Services
The knowledge platform for the financial technology industry
The knowledge platform for the financial technology industry

A-Team Insight Blogs

PolarLake Announce Support for Bitemporal Data

Subscribe to our newsletter

Today PolarLake announces support for Bitemporal Data in the PolarLake Data Management Platform. This will allow clients to make changes to Historic Data, view the changes to Historic Data over time and also look at the detailed history of the Data updates. This extends PolarLake’s capabilities in processing Financial Data such as Pricing, Trade and Position, and Reference Data (Issue, Issuer, Credit Rating, Corporate Actions etc.). PolarLake already has a number of implementations in production supporting Bitemporal behaviour.

Commenting on the release Warren Buckley, founder and CTO of PolarLake said, “The Investment Banking and Asset Management communities are facing new levels of transparency requirements from Regulators when it comes to Financial Data. Regulators will be keen to know what you knew and when you knew it about particular Data Entities. They will also want to know when your view of that Data Entity changed over time, with detailed history of the changes. PolarLake’s core architecture is a great fit for the demands of Bitemporal Data. Our unique use of semantic technologies means that the entire lifecycle of Financial Data Entities over long periods of time can be fully captured, understood and reported against in the PolarLake Platform with no special coding.”

Subscribe to our newsletter

Related content

WEBINAR

Upcoming Webinar: The ROI of Data Trust: Quantifying the Business Value of Data Observability

Date: 8 July 2026 Time: 10:00am ET / 3:00pm London / 4:00pm CET Duration: 50 minutes Data is the fuel that keeps modern financial institutions’ motors running but if that data can’t be trusted then the decisions made based upon it, or the uses to which its put, will be compromised. That’s especially important for...

BLOG

12 Leading Vendors Operationalising AI & ML with Robust Data Pipelines

The transition of artificial intelligence and machine learning (ML) models from experimental sandboxes to production environments remains a persistent operational friction point. While quantitative researchers and data scientists can often demonstrate alpha in isolated backtesting environments, the institutionalisation of these models requires a level of data pipeline robustness, latency control and regulatory auditability that research...

EVENT

Data Management Summit New York City

Now in its 15th year the Data Management Summit NYC brings together the North American data management community to explore how data strategy is evolving to drive business outcomes and speed to market in changing times.

GUIDE

AI in Capital Markets Handbook 2026

AI adoption in capital markets has moved into a more disciplined phase. The priority is now controlled deployment: where AI can be used safely, where it can deliver measurable value, and how outputs can be governed, monitored and evidenced. The 2026 edition of the AI in Capital Markets Handbook examines how AI is being applied...