Hadoop & Big Data Training — Online & Classroom

Course Overview

Master Big Data processing with the Hadoop ecosystem and handle massive datasets at scale. Learn HDFS, MapReduce, Hive, Pig, and Spark to process petabytes of data for enterprises and tech giants.

Hadoop training at SourceKode covers the complete Big Data stack from data storage to processing and analysis. Learn technologies used by Facebook, Yahoo, LinkedIn, and thousands of enterprises for big data analytics.

Why Learn Hadoop?

• Big Data Era: World generates 2.5 quintillion bytes daily
• High Demand: Big Data engineers earn ₹7-22 LPA
• Enterprise Need: All large companies need big data processing
• Future-Proof: Data volumes growing exponentially
• Scalability: Process terabytes to petabytes of data
• Open Source: Apache Hadoop - free and widely adopted

What You’ll Learn

• HDFS: Distributed file system for big data storage
• MapReduce: Distributed data processing framework
• Hive: SQL queries on big data
• Pig: Data flow scripting language
• Spark: In-memory fast data processing
• HBase: NoSQL database on Hadoop
• Sqoop: Data transfer between Hadoop and RDBMS

Course Syllabus

Module 1: Big Data Fundamentals

• What is Big Data (3Vs: Volume, Velocity, Variety)
• Traditional vs Big Data approaches
• Hadoop ecosystem overview
• Linux basics for Hadoop

Module 2: HDFS

• HDFS architecture
• NameNode and DataNode
• Block storage and replication
• HDFS commands
• File operations

Module 3: MapReduce

• MapReduce programming model
• Mappers and Reducers
• Combiner and Partitioner
• Java MapReduce programs
• WordCount and other examples

Module 4: Hive

• Hive architecture
• HiveQL syntax
• Creating tables and partitions
• Joins and aggregations
• User-defined functions (UDF)

Module 5: Pig

• Pig Latin scripting
• Data flow operations
• Pig vs Hive
• ETL with Pig

Module 6: Apache Spark

• Spark architecture
• RDDs (Resilient Distributed Datasets)
• Transformations and actions
• Spark SQL
• DataFrame API
• Spark vs MapReduce

Module 7: HBase & NoSQL

• HBase architecture
• CRUD operations
• Column-family design

Module 8: Data Ingestion

• Sqoop for data import/export
• Flume for log data
• Kafka basics

Projects

1. Log Analysis with Hadoop
2. E-commerce Data Analysis with Hive
3. Real-time Processing with Spark
4. Data Pipeline with full ecosystem

Career Opportunities

• Big Data Engineer - Average: ₹7-18 LPA
• Hadoop Developer - Average: ₹6-15 LPA
• Data Engineer - Average: ₹8-20 LPA
• Spark Developer - Average: ₹9-22 LPA

Companies

• Tech Giants: Facebook, Yahoo, LinkedIn, Twitter
• E-commerce: Amazon, Flipkart, eBay
• Finance: Banks, financial services
• Telecom: Airtel, Jio, Vodafone
• Analytics: All analytics companies