Introduction

Delta Lake, built on top of Apache Spark, has revolutionized how we manage big data processing and storage by offering transactional consistency and scalability. It brings powerful capabilities such as ACID transactions, data versioning, and schema enforcement to Spark, enabling data engineers to work with large datasets reliably and efficiently. This course focuses on how to use Delta Lake with Apache Spark to build robust, scalable, and efficient data engineering pipelines.

In this course, you will learn how to integrate Delta Lake with Spark for both batch and streaming data processing, manage data lakes with optimized storage, and ensure data integrity and consistency across your pipelines. By mastering Delta Lake and Spark, you’ll be equipped to work with the latest big data solutions, improve data governance, and handle complex data processing scenarios.

Prerequisites

• Basic knowledge of Apache Spark and big data concepts.
• Familiarity with SQL and data processing.
• Understanding of data lakes and data engineering practices.
• Experience with Python or Scala for Spark programming (optional but helpful).

Table of Contents

1. Introduction to Delta Lake and Apache Spark
   1.1 What is Delta Lake?
   1.2 Key Features of Delta Lake: ACID Transactions, Data Versioning, and Schema Evolution
   1.3 Overview of Apache Spark and its Integration with Delta Lake
   1.4 Benefits of Using Delta Lake with Spark for Data Engineering
2. Setting Up Delta Lake with Apache Spark
   2.1 Installing and Configuring Apache Spark with Delta Lake
   2.2 Setting Up Delta Lake on Cloud Platforms (AWS, Azure, GCP)
   2.3 Understanding Delta Lake’s Architecture and Components
   2.4 Managing Delta Lake Tables with Spark
3. Working with Delta Lake Tables
   3.1 Creating and Managing Delta Tables
   3.2 Understanding Delta Lake’s File Format and Storage Mechanism
   3.3 Using Delta Lake’s ACID Transactions for Data Integrity
   3.4 Data Versioning and Time Travel with Delta Lake
   3.5 Optimizing Delta Tables for Performance (Z-Ordering, Data Skipping)
4. ETL with Delta Lake and Apache Spark
   4.1 Extracting Data from Different Sources: Databases, APIs, Files
   4.2 Transforming Data with Spark and Delta Lake
   4.3 Loading Data into Delta Lake for Efficient Storage and Querying
   4.4 Using Delta Lake for ETL Automation and Scheduling
5. Streaming Data with Delta Lake
   5.1 Introduction to Spark Structured Streaming
   5.2 Real-Time Data Processing with Delta Lake and Spark
   5.3 Writing Streaming Data to Delta Lake Tables
   5.4 Handling Late Data and Out-of-Order Data in Streaming Pipelines
   5.5 Managing Stream-Table Joins and Aggregations
6. Optimizing Data Processing with Delta Lake and Spark
   6.1 Delta Lake Optimizations: Caching and Compaction
   6.2 Performance Tuning with Spark and Delta Lake
   6.3 Delta Lake File Optimization Strategies (Partitioning, Compaction)
   6.4 Monitoring and Managing Job Performance in Spark
7. Data Governance and Compliance with Delta Lake
   7.1 Data Quality and Validation in Delta Lake
   7.2 Implementing Schema Enforcement and Evolution
   7.3 Managing Data Lineage with Delta Lake
   7.4 Ensuring Compliance and Security in Data Pipelines
8. Advanced Delta Lake Features and Use Cases
   8.1 Delta Lake’s MERGE and UPSERT Operations
   8.2 Handling Incremental Loads and Data Updates
   8.3 Delta Lake for Data Lakehouse Architecture
   8.4 Case Study: Building a Data Pipeline with Delta Lake for Real-Time Analytics
9. Integrating Delta Lake with Other Big Data Tools
   9.1 Delta Lake and Apache Kafka for Stream Processing
   9.2 Integrating Delta Lake with Apache Airflow for Workflow Orchestration
   9.3 Using Delta Lake with Apache Hive for Data Warehousing
   9.4 Delta Lake in the Cloud: AWS Glue, Azure Synapse Analytics, Google BigQuery
10. Real-World Projects and Best Practices
    10.1 Building a Scalable Data Pipeline with Delta Lake and Apache Spark
    10.2 Optimizing Data Lakes for Fast Analytics with Delta Lake
    10.3 Real-Time Data Processing Pipeline Example with Delta Lake
    10.4 Best Practices for Maintaining and Scaling Delta Lake-based Pipelines

Conclusion

By completing this course, you will acquire the necessary skills to work with Delta Lake and Apache Spark to build efficient, scalable, and reliable data pipelines. You will be able to leverage Delta Lake’s advanced features, such as ACID transactions, time travel, and schema evolution, to ensure data consistency and integrity across your data engineering workflows.

This knowledge will enable you to manage large-scale data lakes, optimize data processing performance, and design ETL pipelines that handle both batch and real-time data. Whether you are working in cloud-based environments or on-premises, mastering Delta Lake and Spark will make you proficient in modern data engineering techniques and tools.