Over the past year, I’ve observed an exponential increase in the interest and adoption of Databricks for Advanced Analytics and Machine Learning applications, so it’s no wonder it’s being reported that Microsoft participated in Databricks’ recent $250M round of funding. Databricks is a powerful analytics solution from the founders of Apache Spark. The open-source data processing system is built around speed, ease of use, and offers an in-memory engine up to 100x faster than Hadoop.
This is where Azure Databricks comes in. Azure Databricks brings together the best of both worlds. It was designed in collaboration with the founders of Apache Spark to offer one-click setup and streamlined workflows, an interactive workspace for easy collaboration, and native integration with Azure services including SQL DW, Cosmos DB, Blob storage and Power BI. Of course, you also get enterprise-grade security and Active Directory support. Thanks to early integration, Azure Databricks offers a powerful and highly extensible system with support for Scala, Java, and Python alongside Spark SQL, Streaming and Machine Learning Library (Mllib). Paired with Azure Machine Learning, Databricks provides a powerful tool for grooming datasets for training up ML algorithms.
Interested to learn more and see a brief demo? Check out this introduction by Bryan Cafferky, a Technical Solutions Professional at Microsoft.