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.
One of methods generating new RDD consists on applying transformations on already existent RDDs. But transformations not only makes new RDDs but also gives a sense to all data processing.
Spark was developed to work on big amount of data. If big means millions of items. For every item one or several costly operations are done, it'll lead quick to performance problems. It's one of the reasons why Spark proposes operations executed once per partition.
As every library, Spark has methods than are used more often than the others. As often used methods we could certainly define map or filter. In the other side of less popular transformations we could place, among others, tree-like methods that will be presented in this post.
Transformation are intrinsic part of each data processing framework. Apache Beam is not an exception and it also provides some of build-in transformations that can be freely extended with appropriated structures.
Previous post introduced built-in transformations available in Apache Beam. Most of them were presented - except ParDo that will be described now.
