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If you know about or are already using Databricks, I’m excited to tell you about Databricks Delta. As most of you know, Apache Spark is the underlining technology for Databricks, so about 75-80% of all the code in Databricks is still Apache Spark. You get that super-fast, in-memory processing of both streaming and batch data types as some of the founders of Spark built Databricks.
The ability to offer Databricks Delta is one big difference between Spark and Databricks, aside from the workspaces and the collaboration options that come native to Databricks. Databricks Delta delivers a powerful transactional storage layer by harnessing the power of Spark and Databricks DBFS.
The core abstraction of Databricks Delta is an optimized Spark table that stores data as Parquet files in DBFS, as well as maintains a transaction log that efficiently tracks changes to the table. So, you can read and write data, stored in the Delta format using Spark SQL batch and streaming APIs that you use to work with HIVE tables and DBFS directories.
With the addition of the transaction log, as well as other enhancements, Databricks Delta offers some significant benefits:
Databricks Delta is another great feature of Azure Databricks that I wanted to point out. At Pragmatic Works we are getting a lot of momentum with this and are doing some interesting things with it for customers.
We’d like to help you too. If you have questions about Databricks, Azure or anything data platform related, you’re in the right place. Click the link below or contact us—we’re here to help.
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