Connecting the dots on the emerging Databricks tech stack

The recent Databricks Data+AI Summit attracted a large audience and, like Snowflake Summit, featured a strong focus on large language models, unification and bringing AI to the data. While customers demand a unified platform to access all their data, Databricks and Snowflake are attacking the problem from different perspectives. In our view, the market size justifies the current enthusiasm seen around both platforms but it’s unlikely that either company has a knockout blow for the other. This is not a head on collision. Rather Snowflake is likely years ahead in terms of operationalizing data. Developers can build applications on one platform, like Oracle when it won the market, that perform analysis and take action. Databricks likely has a similar lead in terms of unifying all types of analytic data – e.g. BI, predictive analytics & generative AI. Developers can build analytic applications across heterogeneous data, like Palantir today. But they have to access external operational applications to take action. In this Breaking Analysis we follow up last week’s research by connecting the dots on the emerging tech stack we see forming from Databricks. With an emphasis on how the company is approaching generative AI, unification and governance…and what it means for customers.  To do so we tap the knowledge of three experts who attended the event, CUBE analysts Rob Strechay and George Gilbert and AI market maven Andy Thurai of Constellation Research. 

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