Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

LinkedIn Learning

Big Data Analytics with Hadoop and Apache Spark

via LinkedIn Learning


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


  • 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
  • Next steps

Taught by

Kumaran Ponnambalam


4.5 rating at LinkedIn Learning based on 169 ratings

Start your review of Big Data Analytics with Hadoop and Apache Spark

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.