![]() Data scientists want to use Delta lake and Databricks for the strong support of advanced analytics and better lake technology.īoth Snowflake and Databricks have options to provide the whole range and trying hard to build these capabilities in future releases. The data warehouse guys want to use Snowflake for strong data warehouse and Business Intelligence. At the same time, there are massive IoT data volumes that would need a data lake and demand for advanced analytics, machine learning, etc. And we would need proper management, governance and lineage there. We have a demand to integrate a lot of business systems together, so the Business Intelligence part will be big. We have the luxury to start from scratch. The company also challenged Databricks’ recent performance claims.Hi! I would like to ask for your opinion on the best approach to combine data lake with the data warehouse to serve both Business Intelligence and Advanced Analytics needs. 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. Snowflake, in particular, has been a major rival for Databricks. 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. The launch of lakehouse for financial services further strengthens Databricks’ offering for enterprises. “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. “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. 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. 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. 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. Meanwhile, the partner solutions include offerings from Deloitte and Avanade. 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. 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. ![]() Lakehouse for Financial Services: What’s special? This significantly accelerates the time it takes for us to solve our most pressing business problems,” he added. “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. Learn the critical role of AI & ML in cybersecurity and industry specific case studies.
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