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Engineering blog

The Ubiquity of Delta Standalone: Java, Scala, Hive, Presto, Trino, Power BI, and More!

The Delta Standalone library is a single-node Java library that can be used to read from and write to Delta tables. Specifically, this...
Engineering blog

Extending Delta Sharing for Azure

We are excited for the release of Delta Sharing 0.3.0, which introduces several key improvements and bug fixes, including the following features: Delta...
Engineering blog

Native Support of Session Window in Spark Structured Streaming

Apache Spark™ Structured Streaming allowed users to do aggregations on windows over event-time . Before Apache Spark 3.2™, Spark supported tumbling windows and...
Engineering blog

What’s New in Apache Spark™ 3.1 Release for Structured Streaming

Along with providing the ability for streaming processing based on Spark Core and SQL API, Structured Streaming is one of the most important...
Engineering blog

Natively Query Your Delta Lake With Scala, Java, and Python

Today, we’re happy to announce that you can natively query your Delta Lake with Scala and Java (via the Delta Standalone Reader )...
Engineering blog

A look at the new Structured Streaming UI in Apache Spark 3.0

This is a guest community post from Genmao Yu, a software engineer at Alibaba. Structured Streaming was initially introduced in Apache Spark 2.0...
Engineering blog

Introducing Low-latency Continuous Processing Mode in Structured Streaming in Apache Spark 2.3

Import this notebook on Databricks Structured Streaming in Apache Spark 2.0 decoupled micro-batch processing from its high-level APIs for a couple of reasons...
Engineering blog

Faster Stateful Stream Processing in Apache Spark Streaming

February 1, 2016 by Tathagata Das and Shixiong Zhu in Engineering Blog
Many complex stream processing pipelines must maintain state across a period of time. For example, if you are interested in understanding user behavior...
Engineering blog

New Visualizations for Understanding Apache Spark Streaming Applications

Earlier, we presented new visualizations introduced in Apache Spark 1.4.0 to understand the behavior of Spark applications. Continuing the theme, this blog highlights...