A month after introducing its first industry-specific lakehouse, Databricks unveiled its second.
In January, the vendor, founded in 2013 and based in San Francisco, released its Lakehouse for Retail. On Tuesday, it launched its Lakehouse for Financial Services.
A lakehouse is a combination of a data warehouse and data lake, enabling users to query structured data using SQL as with a data warehouse and unstructured data within the flexibility of a data lake. Databricks, meanwhile, offers a cloud data platform that enables customers to query and analyze data within its lakehouse environment.
The vendor's industry-specific lakehouses are designed to add speed and ease to queries and analysis by providing capabilities it calls accelerators that are prebuilt tools to address the unique technical, business and regulatory requirements of different industries.
Lakehouse for Financial Services accelerators include Fraud Detection and Prevention with Predictive Analytics and Regulatory Reporting.
Unlike the Lakehouse for Retail, however, in addition to accelerators developed solely by Databricks, the Databricks Lakehouse for Financial Services includes tools built in concert with financial services-industry partners Deloitte and Avanade.
In partnership with Deloitte, Databricks developed an accelerator called Deloitte Data-Driven ESG that enables forecasting and predictions for environmental, social and governance (ESG) initiatives. Meanwhile, in partnership with Avanade, Databricks developed Avanade Risk Management to enable customers to quickly deploy data into models to keep up with emerging threats.
"It's all about accelerating the value our customers can get," said Junta Nakai, global head of financial services and sustainability at Databricks. "We've created these lakehouses for industries to ensure that each vertical can leverage the full power of the lakehouse and get started as quickly as possible with all the right controls, requirements, data sets and connectors."
He added that the Lakehouse for Financial Services -- as it was with the Lakehouse for Retail -- is essentially just an enhancement of the general Databricks platform to enable speed and ease of use for a specific set of customers.
"The base is the lakehouse platform," Nakai said. "What we've done is we've extended the platform by building new solutions, bringing in partnerships and bringing in data-sharing capabilities directly on top of that lakehouse platform to get our financial services customers started on data and [augmented intelligence] use cases as quickly as possible."
The accelerators address the highest-value data and AI applications, he continued.
Fraud Detection and Prevention with Predictive Analytics uses data and machine learning to preemptively detect fraud and quickly respond to malicious activity, including fraudulent securities trading and money laundering.
Regulatory Reporting, meanwhile, simplifies complex regulatory reporting and compliance while also streamlining the acquisition, processing and transmission of regulatory data according to open data standards and open data sharing protocols.
Additional accelerators include:
- Post-Trade Analysis and Market Surveillance, which is an easily scalable time-series processing engine for market data that joins core market data with disparate alternative data sources to back test investments and report on transaction costs; and
- Transaction Enrichment, which is a scalable geospatial analytics library for credit card transactions and open banking analytics that enables personalization in retail banking and fraud prevention.
None of the capabilities are new, but previously they had to be built by financial services organizations. The benefits of the Databricks Lakehouse for Financial Services, therefore, are increased speed and efficiency reaching insights, according to David Menninger, an analyst at Ventana Research.
David MenningerAnalyst, Ventana Research
In addition, the accelerators take advantage of the collective knowledge of the financial services industry as a whole rather than just one organization.
"It's not that customers couldn't do this before, but they had to do it themselves," Menninger said. "The new solutions allow them to get up and running faster and they get the benefit of leveraging the work and knowledge of others in the industry."
That speed and collective knowledge is significant, he continued.
"The new offering will give [organizations] a significant head start, saving them months of development effort," Menninger said. "Even for an organization that has already implemented all of these use cases, it may still offer value. They may be able to learn something from these offerings that they hadn't thought of before."
Beyond its Lakehouses for Retail and Financial Services, Databricks is planning more industry-specific lakehouses, according to Nakai.
While serving a broad base of users, Databricks is particularly focused on five major industries -- healthcare, manufacturing and media in addition to retail and financial services -- and Nakai said Databricks plans to release healthcare- and media industry-specific lakehouses later in 2022.
"We know the common pain points and use cases, so we're in a good position to package [capabilities] together so customers can get started with the platform," Nakai said.
Beyond adding new industry-specific lakehouses, Databricks' plans include further simplifying its platform to make it more accessible to users without backgrounds in data science and expanding its partner ecosystem.
"How many data scientists and data engineers are there versus the number of people who can use Tableau or SQL?" Nakai asked. "So one of our strategic priorities is to simplify the platform. Data and AI are not going to be just for the most advanced companies. They're for all companies, so simplification is very important."
Regarding the expansion of its partner ecosystem, he added that the expertise of partners will enable Databricks to not only safely and securely expand its capabilities but also do so in a more simplified way.
"We can't do this alone," Nakai said.