San Francisco-based Databricks, a company that combines the capabilities of a data warehouse and data lake in a single “lakehouse” architecture, today announced a new industry-specific offering: Lakehouse for Financial Services.

Available generally starting today, the fully integrated platform follows the launch of Lakehouse for Retail a month ago. It has been tailor-made using a multi-cloud approach to meet the unique technical, business, and regulatory requirements of companies operating in banking, insurance, and capital markets and help them drive maximum value from their data assets. 

A number of financial services players have already signed up for the product, including TD Bank and Gemini.

“Databricks Lakehouse for Financial Services enables Gemini to bring together data ingestion, machine learning, and analytical engineering onto a single platform,” Sri Rajappa, head of data at Gemini, said. 

“That means various personas on our team, from data engineers, ML engineers to analytical engineers, can do everything from solving complex data engineering problems to building efficient AI models to providing easy access to the underlying datasets using SQL, Python, and Scala. This significantly accelerates the time it takes for us to solve our most pressing business problems,” he added.

Lakehouse for Financial Services: What’s special?

In addition to the capabilities that Databricks’ lakehouse is known for, meaning real-time analytics, business intelligence (BI), and AI, the industry-specific offering provides enterprises with vetted data model frameworks, partner solutions, and 14 pre-built accelerators and open-source libraries.

The accelerators and libraries jumpstart the analytics process for critical industry use cases, including post-trade analysis, market surveillance, transaction enrichment, fraud detection and prevention, and regulatory reporting. Meanwhile, the partner solutions include offerings from Deloitte and Avanade. The former offers a cloud-based, curated data platform to help financial institutions intelligently organize data domains and approved provisioning points, and the latter provides a risk management platform that enables firms to rapidly deploy data into value-at-risk models to keep up with emerging risks and threats. 

Notably, the vertical-specific lakehouse also comes integrated with FINOS’ Legend platform to facilitate the processing and exchange of financial data throughout the entire banking ecosystem and help develop next-generation industry standards. Plus, it uses the Delta Sharing protocol with leading financial data providers like Nasdaq, Factset, and Intercontinental Exchange to make it easier for enterprises to consume, share, and monetize data.

“For Financial Service Institutions around the world looking to modernize and innovate, the two most important assets are no longer its capital or sheer scale, but its data and its people,” said Junta Nakai, the global head for financial services & sustainability at Databricks. 

“The Databricks Lakehouse for Financial Services brings these two critical resources together on a secure, collaborative, and open source-based data platform that allows FSIs to leverage data across clouds and drive innovation with AI,” he added.

Competition

The launch of lakehouse for financial services further strengthens Databricks’ offering for enterprises. The company, which was valued at $38 billion following its last fund-raise in August 2021, goes against the likes of players such as Snowflake, Dremio, and Google BigQuery. 

Snowflake, in particular, has been a major rival for Databricks. The Montana-based company, a data warehouse provider in the beginning, already offers a product for financial services and has lately been adding data lake-specific features with the expansion to AI and ML use-cases and unstructured data among other things. The company also challenged Databricks’ recent performance claims.