This post describes how to programatically compact Parquet files in a folder. Incremental updates frequently result in lots of small files that can be slow to read. It’s best to […]

Spark makes it easy to broadcast maps and perform hash lookups in a cluster computing environment. This post explains how to broadcast maps and how to use these broadcasted variables […]

Spark Structured Streaming and Trigger.Once make it easy to run incremental updates. Spark uses a checkpoint directory to identify the data that’s already been processed and only analyzes the new […]

Spark can use the disk partitioning of files to greatly speed up certain filtering operations. This post explains the difference between memory and disk partitioning, describes how to analyze physical […]

The Spark console is a great way to run Spark code on your local machine. You can easily create a DataFrame and play around with code in the Spark console […]

The SparkSession is used to create and read DataFrames. It’s used whenever you create a DataFrame in your test suite or whenever you read a Parquet / CSV data lake […]

Mill is a SBT alternative that can be used to build Spark projects. This post explains how to create a Spark project with Mill and why you might want to […]

Spark DataFrame columns support arrays, which are great for data sets that have an arbitrary length. This blog post will demonstrate Spark methods that return ArrayType columns, describe how to […]

SBT is an interactive build tool that is used to run tests and package your projects as JAR files. SBT lets you create a project in a text editor and […]

Spark DataFrames schemas are defined as a collection of typed columns. The entire schema is stored as a StructType and individual columns are stored as StructFields. This blog post explains […]