Diffing two DataFrames — yesterday's snapshot against today's, or the output of a pipeline against an expected baseline — is one of the most useful patterns in Spark Scala. This tutorial walks through three approaches: exceptAll for full-row diffs, anti-joins for keyed adds and removes, and a full_outer join that classifies every row as ADDED, REMOVED, CHANGED, or UNCHANGED.

Once your team is sharing internal Spark libraries through a private Nexus, Artifactory, or CodeArtifact, you need a reliable way to publish new versions. Doing it from developer laptops causes version drift and credential sprawl. GitHub Actions can give you tagged, reproducible publishes — but wiring publishTo, credentials, and versioning into a workflow has a few sharp edges.

spark-submit --packages resolves dependencies through Ivy, not sbt. That means none of the credential and resolver setup in your build.sbt carries over — the cluster needs its own configuration. Here's how to wire up --repositories, supply credentials, and decide whether --packages is even the right tool for the job.

Most Spark teams keep shared utility libraries — schema definitions, custom UDFs, internal connectors — in a private Maven repository like Nexus or Artifactory. sbt resolves dependencies from Maven Central by default; pointing it at an internal repo and supplying credentials takes a handful of settings, but the order and the file locations matter.

Spark DataFrames have no built-in row order, so adding a row number isn't as simple as it is in SQL or pandas. When you don't have a column to sort by, you need a strategy to manufacture one. This tutorial walks through the options and their trade-offs.

Wide DataFrames — where each measure lives in its own column — are common in source data but awkward to aggregate, chart, or join. The stack generator function lets you unpivot those columns into rows without leaving the DataFrame API.

Spark dependencies belong in provided scope. The cluster already has them — bundling them into your fat jar wastes space, causes version conflicts, and can break your application at runtime. Here's how provided works in sbt and what to watch out for.

Building a fat jar with sbt-assembly for a Spark project almost always hits duplicate file errors. Spark pulls in hundreds of transitive dependencies, and many of them bundle overlapping META-INF files, service descriptors, and even classes. Merge strategies tell sbt-assembly how to resolve these conflicts.

Value mapping — translating one set of codes into another — comes up constantly in data pipelines. Spark gives you two clean ways to do it: chained when/otherwise expressions and Map lookups with typedLit. Each has strengths, and picking the right one depends on what you're mapping.

Spark spins up a full local mini-cluster inside your test JVM — drivers, executors, shuffle infrastructure — which needs far more heap than sbt's default. Two settings in build.sbt fix this: forking the test JVM and sizing it properly.