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LinkedIn Learning

Big Data Analytics with Hadoop and Apache Spark

via LinkedIn Learning

Overview

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

Syllabus

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

Taught by

Kumaran Ponnambalam

Reviews

4.5 rating at LinkedIn Learning based on 413 ratings

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