Build your ultimate AI agent
Course Description
Ready to handle real-world data at scale? This course teaches you to transform large datasets using Spark SQL and PySpark in Databricks. Learn to shape and clean data, run aggregations with optimized joins, and apply window functions for advanced analytics. You'll also set up file-based streaming with fault-tolerant checkpoints and persist results as Delta tables. By the end, you'll be orchestrating multi-step production pipelines with Databricks Workflows and Lakeflow Declarative Pipelines.
Prerequisites
Curriculum
Course outline
1
Loading and Shaping Data
In this chapter, you'll learn how to work with Databricks notebooks, load CSV data into Spark DataFrames, and shape data using PySpark and SQL.
2
Data Cleaning and Optimization
Learn how to define explicit schemas, build a data cleaning pipeline, and optimize query performance with broadcast joins.
3
Analytics and Production Pipelines
Learn how to calculate running totals and rankings with window functions, build streaming pipelines, and deploy production workflows.
Data Transformation with Spark SQL in Databricks
Course
Complete
