Big Data Analytics with Hadoop and Apache Spark

Discover how to build scalable and optimized data analytics pipelines by combining the powers of Apache Hadoop and Spark.

Introduction

• The combined power of Spark and Hadoop Distributed File System (HDFS)

1. Introduction and Setup

• Apache Hadoop overview
• Apache Spark overview
• Integrating Hadoop and Spark
• Setting up the environment
• Using exercise files

2. HDFS Data Modeling for Analytics

• Storage formats
• Compression
• Partitioning
• Bucketing
• Best practices for data storage

3. Data Ingestion with Spark

• Reading external files into Spark
• Writing to HDFS
• Parallel writes with partitioning
• Parallel writes with bucketing
• Best practices for ingestion

4. Data Extraction with Spark

• How Spark works
• Reading HDFS files with schema
• Reading partitioned data
• Reading bucketed data
• Best practices for data extraction

5. Optimizing Spark Processing

• Pushing down projections
• Pushing down filters
• Managing partitions
• Managing shuffling
• Improving joins
• Storing intermediate results
• Best practices for data processing

6. Use Case Project

• Problem definition
• Data loading
• Total score analytics
• Average score analytics
• Top student analytics

Conclusion

• Next steps