databricks/LearningSparkV2

This repository provides a comprehensive set of runnable examples for Apache Spark, covering Python, Scala, and Java implementations across multiple chapters. It includes sample datasets, build scripts, and configuration files designed to demonstrate core Spark features such as DataFrames, SQL, machine learning with MLflow, and performance optimization techniques like caching and partitioning. The system serves as a practical reference for learning and testing Spark applications in various programming environments.

This period focused on expanding the project's educational content by adding comprehensive examples and sample datasets for Chapters 2, 3, 6, and 7 across Python, Scala, and Java. The updates included new source files, data files, and build scripts to support standalone Spark applications, while also aligning the project with Apache Spark 3.0.0-preview2.

9 changes

This period focused on expanding the repository's educational resources by adding sample datasets and comprehensive Scala examples for Apache Spark operations such as caching, partitioning, and joins. Additionally, the project introduced an MLflow project example for training a Random Forest model, alongside updated documentation for Databricks Runtime compatibility.

5 changes

databricks/LearningSparkV2 was measured the same way every project in this corpus was: the same rubric, at a pinned commit, with the result published in full. Point the surveyor at a repository you know and see whether you agree with it.