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...
Introducing Stream-Stream Joins in Apache Spark 2.3
Since we introduced Structured Streaming in Apache Spark 2.0, it has supported joins (inner join and some type of outer joins) between a streaming and a static DataFrame/Dataset. With the release of Apache Spark 2.3.0, now available in Databricks Runtime 4.0 as part of Databricks Unified Analytics Platform, we now support stream-stream joins. In this...
Do your Streaming ETL at Scale with Apache Spark’s Structured Streaming
At the Spark Summit in San Francisco in June, we announced that Apache Spark’s Structured Streaming is marked as production-ready and shared benchmarks to demonstrate its performance compared to other streaming engines. Structured Streaming is a novel way to process streams. Not only does this new way make it easy to build end-to-end streaming applications,...
Event-time Aggregation and Watermarking in Apache Spark’s Structured Streaming
This is the fourth post in a multi-part series about how you can perform complex streaming analytics using Apache Spark. Continuous applications often require near real-time decisions on real-time aggregated statistics—such as health of and readings from IoT devices or detecting anomalous behavior. In this blog, we will explore how easily streaming aggregations can be...
Working with Complex Data Formats with Structured Streaming in Apache Spark 2.1
In part 1 of this series on Structured Streaming blog posts, we demonstrated how easy it is to write an end-to-end streaming ETL pipeline using Structured Streaming that converts JSON CloudTrail logs into a Parquet table. The blog highlighted that one of the major challenges in building such pipelines is to read and transform data...
Real-time Streaming ETL with Structured Streaming in Apache Spark 2.1
We are well into the Big Data era, with organizations collecting massive amounts of data on a continual basis. Yet, the value of this data deluge hinges on the ability to extract actionable insights in a timely fashion. Hence, there is an increasing need for continuous applications that can derive real-time actionable insights from massive...
Spark Structured Streaming
Apache Spark 2.0 adds the first version of a new higher-level API, Structured Streaming, for building continuous applications. The main goal is to make it easier to build end-to-end streaming applications, which integrate with storage, serving systems, and batch jobs in a consistent and fault-tolerant way. In this post, we explain why this is hard...
