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.
Data, especially in streaming applications, can very often arrive on late to the processing pipeline. Despite of that, Apache Beam is able to handle this case pretty easily thanks to watermark mechanism.
The idea of watermark was firstly presented in the occasion of discovering the Apache Beam project. However it's also implemented in Apache Spark to respond to the same problem - the problem of late data.
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.
