Big Data Hadoop & Spark Developer Course

Course Overview

Become a high-performing big data software developer by mastering distributed data processing, ETL pipeline construction, and large-scale analytics engines. This course bridges foundational Hadoop ecosystem tools with modern, lightning-fast Apache Spark frameworks. You will learn to process petabytes of structured and unstructured data, design scalable data warehouses in Hive, ingest real-time streams, and write optimized distributed code in Scala and Python.

Key Learning Outcomes

• Architect and write high-performance distributed data processing pipelines using MapReduce and Apache Spark.
• Design optimized schema, partition strategies, and complex analytical queries using Apache Hive and Impala.
• Ingest structured, semi-structured, and unstructured data at scale using Apache Flume, Sqoop, and Kafka.
• Optimize Spark execution jobs using DataFrames, Spark SQL, RDD memory management, and Catalyst Optimizer.

Detailed Syllabus

• Module 1: Hadoop Ecosystem & Data Ingestion: Deep dive into HDFS architecture, MapReduce paradigms, Sqoop database extraction, and Flume log ingestion.
• Module 2: Data Warehousing with Apache Hive: Managed vs external tables, ORC/Parquet storage formats, partitioning, bucketing, and HiveQL optimization.
• Module 3: Apache Spark Fundamentals: Spark core architecture, RDD operations, transformations vs actions, lazy evaluation, and Spark SQL DataFrames.
• Module 4: Advanced Data Pipelines & Streaming: Spark Streaming integration, Kafka consumer/producer pipelines, performance tuning, and memory optimization.

Target Audience & Prerequisites

Ideal for software developers, data engineers, and backend programmers looking to transition into big data. Basic knowledge of Python, Java, or Scala and foundational SQL skills are recommended.