Path overview
In this path, you’ll learn how to use PySpark and Apache Spark to process data at scale. You’ll work with RDDs, DataFrames, and Spark SQL to build and optimize production ETL pipelines through hands-on, real-world data engineering scenarios.
Key skills
- Processing large-scale datasets with PySpark
- Building production ETL pipelines with Apache Spark
- Optimizing distributed data processing performance
Path outline
Distributed Data Processing with PySpark [2 courses]
Course 1: Analyzing Large Datasets in Spark 8h
Course Objectives- Set up and configure Spark applications using SparkSession
- Transform and analyze distributed datasets using RDDs and DataFrames
- Write SQL queries on large datasets using Spark SQL
- Understand Spark's architecture including Drivers, Executors, and lazy evaluation
Course 2: PySpark for Data Engineering 6h
Course Objectives- Build complete ETL pipelines with proper project structure including extract, transform, and load functions
- Handle real-world data quality issues including inconsistent formats, test data, and missing values
- Implement production-standard error handling, logging, and data quality validation
- Deploy PySpark jobs that run on schedules using spark-submit
- Diagnose performance bottlenecks using the Spark UI to identify slow operations and inefficient partitioning
- Apply systematic optimization techniques including caching, partition tuning, and reducing shuffles
- Understand different managed Spark platforms including Databricks, EMR, and Dataproc
- Integrate PySpark with cloud storage services like AWS S3 and data catalogs like AWS Glue
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