Databricks Performance Optimization
In this course, you’ll learn how to optimize workloads and physical layout with Spark and Delta Lake and analyze the Spark UI to assess performance and debug applications. We’ll cover topics like streaming, liquid clustering, data skipping, caching, photons, and more.
Note: This course is part of the 'Advanced Data Engineering with Databricks' course series.
The content was developed for participants with these skills/knowledge/abilities:
- Ability to perform basic code development tasks using Databricks (create clusters, run code in notebooks, use basic notebook operations, import repos from git, etc.)
- Intermediate programming experience with PySpark
- Extract data from a variety of file formats and data sources
- Apply a number of common transformations to clean data
- Reshape and manipulate complex data using advanced built-in functions
- Intermediate programming experience with Delta Lake (create tables, perform complete and incremental updates, compact files, restore previous versions, etc.)
Self-Paced
Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
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Upcoming Public Classes
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Career Workshop/
March 20