Pyspark ETL with PostgreSQL- Workshop

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Introduction

This workshop will delve into the powerful combination of Apache Spark and PostgreSQL for efficient Extract, Transform, and Load (ETL) operations. You'll learn how to leverage Pyspark's computing capabilities to process large datasets and integrate them seamlessly with PostgreSQL databases.We have 4hr -16hr workshops.

Workshop Objectives

• Understand the basics of Apache Spark and its architecture.
• Learn how to use Pyspark for data manipulation and transformations.
• Explore techniques for reading and writing data from/to PostgreSQL databases.
• Implement ETL pipelines using Pyspark and PostgreSQL.
• Optimize ETL performance and handle large datasets efficiently.

Workshop Outline

1. Introduction to Apache Spark
• What is Apache Spark?
• Key features and benefits
• Spark architecture and components
• Setting up a Spark environment
2. Pyspark Basics
• Creating Spark sessions
• Reading and writing data (text files, CSV, JSON)
• Data manipulation using DataFrames
• Common Pyspark transformations and actions
3. Working with PostgreSQL
• Introduction to PostgreSQL
• Connecting to PostgreSQL from Pyspark
• Reading data from PostgreSQL tables
• Writing data to PostgreSQL tables
• Using SQL queries within Pyspark
4. ETL Pipeline Development
• Defining ETL requirements
• Data extraction from source systems
• Data transformation and cleaning
• Data loading into PostgreSQL
• Handling errors and exceptions
5. Performance Optimization
• Understanding performance bottlenecks
• Optimizing data transformations
• Partitioning and caching data
• Using broadcast variables and accumulators

Throughout the workshop, you'll engage in hands-on exercises to reinforce your learning. Some potential exercises include:
Reading CSV data into a Spark DataFrame and performing basic transformations.
Writing data from a DataFrame to a PostgreSQL table.
Implementing an ETL pipeline to extract data from a CSV file, clean it, and load it into a PostgreSQL database.
Optimizing the performance of an ETL pipeline using techniques like partitioning and caching.
Prerequisites
Basic knowledge of Python programming
Familiarity with SQL concepts
A working knowledge of PostgreSQL (optional)
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