Hadoop Developer with Spark

Course Modules

1. Introduction to Hadoop and Spark

   • Overview of Hadoop and Apache Spark and their role in big data processing
   • Hadoop architecture, components, and its ecosystem (HDFS, MapReduce, YARN)
   • Spark architecture and its integration with Hadoop
   • Difference between Hadoop MapReduce and Spark processing models

2. Setting Up Hadoop and Spark Environments

   • Installing and configuring Hadoop and Spark
   • Understanding the Hadoop Distributed File System (HDFS)
   • Setting up and managing a Spark cluster on YARN or Mesos
   • Working with Spark shell and interactive analysis

3. Data Processing with Hadoop and Spark

   • Understanding data processing workflows in Hadoop (MapReduce) vs. Spark (RDDs, DataFrames, Datasets)
   • Writing Hadoop jobs with MapReduce and Spark applications
   • Leveraging Spark SQL and DataFrames for structured data processing
   • Working with Spark Streaming for real-time data processing

4. Spark Core Concepts

   • Understanding RDDs (Resilient Distributed Datasets) and Transformations
   • Using Actions, Caching, and Persisting for performance optimization
   • Implementing Spark SQL for querying data with Hive and Parquet
   • Advanced operations like Joins, Aggregations, and GroupBy

5. Integrating Hadoop with Spark

   • Connecting Hadoop ecosystem tools like Hive, HBase, and Sqoop with Spark
   • Using Spark with HDFS for efficient data storage and processing
   • Integrating with Apache Kafka for data streaming and ingestion
   • Best practices for data loading and data writing in Hadoop/Spark ecosystems

6. Performance Tuning and Optimization

   • Understanding Spark performance and tuning concepts
   • Optimizing RDDs, DataFrames, and Spark jobs for improved performance
   • Memory management and garbage collection strategies
   • Efficient use of Spark’s Catalyst optimizer and Tungsten execution engine

7. Real-time Data Processing with Spark Streaming

   • Introduction to Spark Streaming for real-time data processing
   • Working with DStreams and structured streaming in Spark
   • Implementing real-time data pipelines with Spark Streaming
   • Integrating with Kafka, Flume, and Kinesis for real-time data ingestion

8. Advanced Hadoop and Spark Topics

   • Implementing machine learning with MLlib in Spark
   • Using GraphX for graph processing with Spark
   • Data lineage, versioning, and audit tracking in big data applications
   • Security in the Hadoop ecosystem (Kerberos, HDFS encryption)

9. Deploying Big Data Applications on Hadoop and Spark

   • Deploying Spark jobs to the cluster using YARN
   • Running and monitoring Hadoop MapReduce and Spark applications on the cluster
   • Troubleshooting and debugging Spark and Hadoop applications
   • Managing data pipelines and automating workflows with Apache Airflow