Databricks’ Lakehouse supports advanced analytics and artificial intelligence

Databricks’ Lakehouse supports advanced analytics and artificial intelligence

Databricks’ Lakehouse platform enables organizations to run complex analytics queries on massive amounts of data. This means we can leverage Databricks’ analytics and AI across a wide range of industries and use cases.

Etlians are familiar faces in the Databricks events and are essential part of their network. We have been keenly following up the Databricks roadmap and development for a couple of years now. During which, we have attended many Databricks events, such as Lakehouse day at Stockholm last spring and Databricks keynote session meetup in Helsinki during summer.

These events were a great chance to meet Databricks professionals and other interesting people around the field. While making new connections, we also got to hear the latest insights which we of course want to report to you.

The potential of Lakehouse platform

At the Stockholm Lakehouse day, we got to see a glance of the bigger picture of Databricks Lakehouse. Databricks executives presented their positioning and prospects. Also, we got to examine many examples of Databricks implementations by Databricks sponsors. You could say we were impressed by the insights shared and decided to share a few key takeaways here.

Open data Lakehouses are increasingly being adopted as a standard for leveraging data and AI. Essentially, they provide a unified and scalable infrastructure for handling large volumes of data.

In addition to this, open data Lakehouses are designed to support advanced analytics and AI, hence enable organizations to run complex queries on huge datasets. They also provide necessary tools for building and deploying ML models and other AI applications. Databricks analytics and AI can be leveraged by a wide range of industries and use cases.

From Etlia’s experience, we see that lot of the shift from traditional warehouses to lakehouse happens due to benefits Databricks Lakehouse platform offers:

  1. It is possible with Lakehouse to handle both relational sources and files/non-relational sources in one platform.
  2. Ability to build scoring models and other advanced data science use cases in one area, since Databricks provides the capacity and capability to work with those models. 
  3. By following guidelines and best practices of solution, it is even possible to get considerable savings in platform usage cost and time. This enables a shorter time to market.

We will be glad to share more about Databricks’ upcoming features which were discussed at the keynote session. We have already tried a couple of those firsthand and are eagerly waiting to try the rest of those soon. We’ll report the findings to you then!

Best Regards, Jaakko and Raaju from Etlia

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