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.
Serialization frameworks are intrinsic part of Big Data systems. Spark is not an exception for this rule and it offers some different possibilities to manage serialization.
Issues with not serializable objects are maybe the most painful when we start to work with Spark. But hopefully there are several solutions to them.
Some of previous posts (Serialization issues - part 1) presented some of solutions for serialization problems. This post is its continuation.
Since in distributed computing the data moves either locally (within single worker) or remotely (between several different workers), it must have a format understandable by the machine. And this format is guaranteed by the operation of serialization, also present in Apache Beam.
