Apache Spark Essential Training: Big Data Engineering

Learn how to make Apache Spark work with other Big Data technologies and put together an end-to-end project that can solve a real-world business problem.

Introduction

• Driving big data engineering with Apache Spark
• Course prerequisites
• Setting up the exercise files

1. Data Engineering Concepts

• What is data engineering?
• Data engineering vs. data analytics vs. data science
• Data engineering functions
• Batch vs. real-time processing
• Data engineering with Spark

2. Spark Capabilities for ETL

• Spark architecture review
• Parallel processing with Spark
• Spark execution plan
• Stateful stream processing
• Spark analytics and ML

3. Batch Processing Pipelines

• Batch processing use case: Problem statement
• Batch processing use case: Design
• Setting up the local DB
• Uploading stock to a central store
• Aggregating stock across warehouses

4. Real-Time Processing Pipelines

• Real-time use case: Problem
• Real-time use case: Design
• Generating a visits data stream
• Building a website analytics job
• Executing the real-time pipeline

5. Data Engineering with Spark: Best Practices

• Batch vs. real-time options
• Scaling extraction and loading operations
• Scaling processing operations
• Building resiliency

6. End-to-End Exercise Project

• Project exercise requirements
• Solution design
• Extracting long last actions
• Building a scorecard

Conclusion

• More about Apache Spark