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.
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.
Defining the universal workload and associating corresponding resources is always difficult. Even if most of time expected resources will support the load, there always will be some interval in the year when data activity will grow (e.g. Black Friday). One of Spark's mechanisms helping to prevent processing failures in such situations is dynamic resource allocation.
