Automatically Evolve Your Nested Column Schema, Stream From a Delta Table Version, and Check Your Constraints
We recently announced the release of Delta Lake 0.8.0, which introduces schema evolution and performance improvements in merge and operational metrics in table history. The key features in this release are: Unlimited MATCHED and NOT MATCHED clauses for merge operations in Scala, Java, and Python. Merge operations now support any number of whenMatched and whenNotMatched...
Diving Into Delta Lake: DML Internals (Update, Delete, Merge)
In previous blogs Diving Into Delta Lake: Unpacking The Transaction Log and Diving Into Delta Lake: Schema Enforcement & Evolution, we described how the Delta Lake transaction log works and the internals of schema enforcement and evolution. Delta Lake supports DML (data manipulation language) commands including DELETE, UPDATE, and MERGE. These commands simplify change data...
Enabling Spark SQL DDL and DML in Delta Lake on Apache Spark 3.0
Last week, we had a fun Delta Lake 0.7.0 + Apache Spark 3.0 AMA where Burak Yavuz, Tathagata Das, and Denny Lee provided a recap of Delta Lake 0.7.0 and answered your Delta Lake questions. The theme for this AMA was the release of Delta Lake 0.7.0 coincided with the release of Apache Spark 3.0...
Schema Evolution in Merge Operations and Operational Metrics in Delta Lake
Try this notebook to reproduce the steps outlined below We recently announced the release of Delta Lake 0.6.0, which introduces schema evolution and performance improvements in merge and operational metrics in table history. The key features in this release are: Support for schema evolution in merge operations (#170) - You can now automatically evolve the...
Query Delta Lake Tables from Presto and Athena, Improved Operations Concurrency, and Merge performance
We are excited to announce the release of Delta Lake 0.5.0, which introduces Presto/Athena support and improved concurrency. The key features in this release are: Support for other processing engines using manifest files (#76) - You can now query Delta tables from Presto and Amazon Athena using manifest files, which you can generate using Scala,...
Simple, Reliable Upserts and Deletes on Delta Lake Tables using Python APIs
We are excited to announce the release of Delta Lake 0.4.0 which introduces Python APIs for manipulating and managing data in Delta tables. The key features in this release are: Python APIs for DML and utility operations (#89) - You can now use Python APIs to update/delete/merge data in Delta Lake tables and to run...
Announcing the Delta Lake 0.3.0 Release
We are excited to announce the release of Delta Lake 0.3.0 which introduces new programmatic APIs for manipulating and managing data in Delta tables. The key features in this release are: Scala/Java APIs for DML commands - You can now modify data in Delta tables using programmatic APIs for Delete (#44), Update (#43) and Merge...
Efficient Upserts into Data Lakes with Databricks Delta
Simplify building big data pipelines for change data capture (CDC) and GDPR use cases. Databricks Delta, the next-generation engine built on top of Apache Spark™, now supports the MERGE command, which allows you to efficiently upsert and delete records in your data lakes. MERGE dramatically simplifies how a number of common data pipelines can be...
Introducing Databricks Optimized Autoscaling on Apache Spark™
Databricks is thrilled to announce our new optimized autoscaling feature. The new Apache Spark™-aware resource manager leverages Spark shuffle and executor statistics to resize a cluster intelligently, improving resource utilization. When we tested long-running big data workloads, we observed cloud cost savings of up to 30%. What’s the problem with current state-of-the-art autoscaling approaches? Today,...
Introducing Low-latency Continuous Processing Mode in Structured Streaming in Apache Spark 2.3
Structured Streaming in Apache Spark 2.0 decoupled micro-batch processing from its high-level APIs for a couple of reasons. First, it made developer’s experience with the APIs simpler: the APIs did not have to account for micro-batches. Second, it allowed developers to treat a stream as an infinite table to which they could issue queries as...
