A 3-day bug hunt on a 3-person team costs up to β¬7,200 in lost engineering time. This workshop teaches you to prevent that β unit tests, data tests, and integration tests for PySpark and Databricks Lakeflow, including Spark Declarative Pipelines.
This post begins a new series dedicated to Apache Spark 2.4.0 features. The first covered topic will be bucket pruning.
The series about Apache Spark 2.4.0 features continues. After last week's discovery of bucket pruning, it's time to switch to Structured Streaming module and see its major evolution.
The series about the features introduced in Apache Spark 2.4.0 continues. Today's post will cover higher-order functions that you may know from elsewhere.
Apache Avro became one of the serialization standards, among others because of its use in Apache Kafka's schema registry. Previously to work with Avro files with Apache Spark we needed Databrick's external package. But it's no longer the case starting from 2.4.0 release where Avro became first-class citizen data source.
Data-driven systems continuously change. We moved from static, batch-oriented daily processing jobs to real-time streaming-based pipelines running all the time. Nowadays, the workflows have more and more AI compontents. Apache Spark tries to stay in the movement and in the new release proposes the implementation of the barrier execution mode as a new way to schedule tasks.
When I first heard about the foreachBatch feature, I thought that it was the implementation of foreachPartition in the Structured Streaming module. However, after some analysis I saw how I was wrong because this new feature addresses other but also important problems. You will find more .
Apache Spark 2.4.0 brought a lot of internal changes but also some new features exposed to the end users, as already presented high-order functions. In this post, I will present another new feature, or rather 2 actually, because I will talk about 2 new SQL functions.
