![]() ![]() They receive, for example, datasets and libraries for applying data management, artificial intelligence and analytics in their sector. This year, Databricks released lakehouses for retail, financial service, healthcare and media companies. They focus on common use cases to save on discovery, design, testing and development time. For these sectors, Databricks makes Solution Accelerators available, a term for targeted notebooks and best practices. In this case, customized means that it’s built for specific sectors. If there is one thing that stands out in the platform’s development over the past few months, it’s that lakehouses are increasingly being customized for the market. Tip: Will Databricks lakehouses change the AI and data world? Lakehouse tailored to sectors Therefore, we looked at what’s new and how the platform’s developing. Although that’s a good thing, there are also tests in which competitors score better. The lakehouse scores well in terms of price and performance. Instead, Databricks is fully committed to bringing together the best aspects of data warehouses and data lakes in an architecture called the lakehouse.ĭuring the Data + AI Summit, Databricks shared some benchmarks with us. Unified analytics is still the goal, as lakehouses follow the idea of deploying an integrated tool, but the word is used a lot less these days. Whereas the company still described itself as a unified analytics provider three to four years ago, it now revolves around the lakehouse paradigm. Databricks announced that it’s expanding its Lakehouse Platform at its Data + AI Summit.ĭatabricks is positioning the lakehouse more and more aggressively.
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