After launching industry-specific data lakes for the retail, financial services and healthcare sectors over the past three months, Databricks is launching a solution targeting the media and entertainment (M&E) sector .
Now generally available, the M&E Data Lake comes with features specific to industry use cases that the company calls accelerators, including real-time personalization, said Steve Sobel, global communications manager at company, in a blog post.
“The idea of these so-called accelerators is to provide pre-built analytics and use case functionality to accelerate deployment and time to value for customers,” said Doug Henschen, principal analyst at Constellation Research.
“You might think that the general-purpose version of Databricks Lakehouse gives the organization 80% of what it needs to get productive use of its data to generate enterprise-specific business insights and data science. The idea of the industry-specific version of the Lakehouse is to get customers in specific industries, say, 90% of the way to productive use of their data,” Henschen said.
The remaining 10% is the effort for initial deployment, data loading, configuration, and setting up customer-specific administrative and analytics tasks, Henschen said.
The data lakehouse is a relatively new data architecture concept, first championed by Cloudera, which offers both storage and analytics capabilities as part of the same solution, unlike data lake concepts and data warehouse which, respectively, store data in native format. , and structured data, often in SQL format.
Some of the targeted solutions that are part of Databricks’ new Lakehouse M&E include recommendation engines, customer lifetime value (CLV) module, streaming QoS module, and toxicity detection for games.
While recommendation engines help create more personalized experiences for consumers with AI-powered content recommendations that drive engagement and monetization opportunities, the CLV module identifies valuable customers with models that focus on spending habits to help businesses retain users and make better marketing investments, the company said. The recommendations also include suggested product development choices.
“The most effective recommendation engines are very industry and use-case specific. They require specific data inputs, patterns, algorithms, and they provide very specific recommendations. Providing accurate and reliable recommendations is not not an easy task, so accelerators can provide useful starting points for companies,” Henschen said.
The new data lakehouse features for streaming QoS and gaming toxicity detection are very case-specific services. While the streaming quality of service, as the name suggests, analyzes both streaming and batch data to ensure optimal and personalized content for users, the game-specific service uses natural language processing for the real-time detection of toxic language to ensure optimal dissemination. gaming experience for users.
Partner solutions to boost functionality, adoption
As with other data lake and data warehouse providers – such as Snowflake, which has also launched a series of industry-focused solutions – Databricks also wants to offer more functionality to its customers by partnering with other companies, which in turn should drive adoption of its new Lakehouse solution.
“Partnerships can save customers time as long as they introduce pre-built, time-saving integrations between platforms and partner solutions. It is typical for such partnerships to start with the most popular solutions in a given industry or with deep integrations with already established partners in a given industry. The more partnerships, the better for the solution provider,” Henschen said.
Some of the partnerships under the M&E Lakehouse solution include the company’s strategic ties with AWS, Cognizant, Lovelytics, Labelbox and Fivetran.
While the partnership with AWS focuses on providing more data and analytics capabilities for the M&E industry, the Cognizant partnership focuses on maintaining video quality for customers.
Cognizant’s solution combines telemetry data with artificial intelligence and machine learning to quickly identify and resolve video quality issues in real time to resolve issues such as failed playback, the first frame or a rejection issue, the company said.
The company’s collaboration with Lovelytics focuses on baseball. As part of the solution, baseball team managers can optimize a game’s strategy by using predictive analytics via artificial intelligence to forecast performance.
The solution also leverages biomechanical indicators to flag and prevent potential player injuries, the company said.
The joint solution with Labelbox is aimed at media companies and should help companies derive more value from unstructured data.
Databricks has partnered with Fivtran to offer a data integration service that it says can ingest data from more than 180 sources, including operational technology, advertising and marketing solutions.