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Microsoft Cisco AWS EC-Council View All Courses
Hadoop
Open-Source Big Data Processing Framework

Conquer

Big Data Hadoop 3.x

The industry-standard framework for processing petabyte-scale datasets used at Yahoo, Facebook, and every major data company. Master HDFS, MapReduce, YARN, the complete Hadoop ecosystem (Hive, Pig, HBase, Sqoop, Flume), and integrate with Apache Spark for real-time analytics.

HDFS MapReduce YARN Apache Hive Apache Pig HBase Sqoop Apache Flume Apache Spark Oozie ZooKeeper Ambari
Enroll Now Brochure
Hadoop Ecosystem Architecture
HDFS — Distributed Storage Layer
NameNode
metadata
→
DN1
64MB blocks
DN2
64MB blocks
DN3
64MB blocks
YARN — Resource Manager
ResourceManager NodeManager ApplicationMaster
Ecosystem Tools:
Hive Pig HBase Sqoop Spark Flume
Petabyte
Scale
Distributed storage
32 hrs
Duration
Hands-on training
9
Modules
Full ecosystem
Cert
Included
Industry recognised
24/7
Support
Expert guidance
Apache Hadoop 3.x
HDFS
MapReduce
YARN
Apache Hive
HiveQL
Apache Pig
Pig Latin
HBase
Apache Sqoop
Apache Flume
Apache Spark
Spark SQL
MLlib
Apache Oozie
ZooKeeper
Apache Ambari
Python
Java
Scala
AWS EMR
Azure HDInsight
Apache Hadoop 3.x
HDFS
MapReduce
YARN
Apache Hive
HiveQL
Apache Pig
Pig Latin
HBase
Apache Sqoop
Apache Flume
Apache Spark
Spark SQL
MLlib
Apache Oozie
ZooKeeper
Apache Ambari
Python
Java
Scala
AWS EMR
Azure HDInsight
What You Master

The Complete Big Data Skill Set

From HDFS internals to real-time Spark processing — cover the full Hadoop ecosystem in one course.

🐘
HDFS + MapReduce Core

Understand distributed file system internals: NameNode, DataNode, block replication, and rack awareness. Write MapReduce jobs in Java and Python to process billions of records across cluster nodes in parallel.

NameNode DataNode MapReduce Combiner Partitioner Rack Awareness
🐝
Apache Hive

SQL-like HiveQL queries on HDFS data — perfect for analysts who know SQL but not Java.

⚡
Apache Spark

100x faster than MapReduce — Spark RDDs, DataFrames, Spark SQL, and MLlib on YARN.

🔄
Full Ecosystem Integration

Connect Hadoop to RDBMS with Sqoop, ingest logs with Flume, store in HBase, and schedule workflows with Oozie.

Sqoop Flume Oozie
Syllabus

Course Curriculum

9 comprehensive modules covering the entire Hadoop ecosystem and Big Data processing pipeline.

  • What is Big Data?
  • Characteristics of Big Data (5 Vs: Volume, Velocity, Variety, Veracity, Value)
  • Traditional Data Processing vs. Big Data Processing
  • Challenges in Handling Big Data
  • Big Data Applications Across Industries
  • What is Hadoop?
  • Evolution of Hadoop & History
  • Components of Hadoop Ecosystem
  • Hadoop 1.0 vs. 2.0 vs. 3.0
  • Overview of HDFS
  • Hadoop Architecture & Working
  • HDFS Features & Architecture
  • Blocks, NameNode, DataNode, Secondary NameNode
  • File Read/Write Operations in HDFS
  • Data Replication & Fault Tolerance
  • Rack Awareness & High Availability
  • Hands-on: HDFS Commands & File Operations
  • What is MapReduce?
  • MapReduce Workflow (Mapper, Reducer, Combiner, Partitioner)
  • Input Splits & Output Formats
  • Data Processing Flow in MapReduce
  • Writing and Executing MapReduce Jobs in Java
  • Hands-on: Word Count Program & Other Use Cases
  • Introduction to YARN
  • YARN Architecture (ResourceManager, NodeManager, ApplicationMaster)
  • How YARN Manages Resources
  • YARN vs. Traditional MapReduce
  • Hands-on: Running Jobs on YARN
  • Apache Hive: HiveQL, Managed vs External Tables, Partitioning & Bucketing
  • Apache Pig: Pig Latin Scripting, Data Transformation
  • Apache HBase: NoSQL on Hadoop, CRUD Operations, Region Servers
  • Apache Sqoop: Import/Export between RDBMS & Hadoop
  • Apache Flume: Log & Streaming Data Ingestion
  • Introduction to Apache Spark
  • Spark vs MapReduce
  • Spark Components: RDDs, DataFrames, Spark SQL, Streaming, MLlib
  • Hands-on: Running Spark Jobs on Hadoop YARN
  • PySpark for Data Engineering
  • Setting up Single-Node & Multi-Node Clusters
  • Hadoop Configuration Files (core-site.xml, hdfs-site.xml, yarn-site.xml)
  • Managing Hadoop Services & Daemons
  • Cluster Monitoring & Troubleshooting
  • Apache Ambari for cluster management
  • Real-time Use Cases in Banking, Retail, Healthcare
  • End-to-End Big Data Project Implementation
  • Cloud Deployments: AWS EMR, Azure HDInsight, Google Dataproc
  • Best Practices for Hadoop Development & Deployment
Career Outcomes

Big Data Roles in 2026

Big Data engineers remain among the highest-paid professionals in the tech industry. Hadoop expertise combined with Spark skills opens doors at every major data-driven company globally.

₹10–30L
India Avg LPA
$80K–$145K
USA Avg
50K+
Open Roles
Top 3
Data Skills
Big Data Engineer
Build and maintain HDFS clusters and Hadoop-based ETL pipelines for enterprise data lakes
₹10–22 LPA · $80K–$130K USA
Data Engineer (Hadoop/Spark)
Develop PySpark and Hive workflows for large-scale data transformation and analytics
₹12–24 LPA · $85K–$140K USA
Data Scientist
Use Hadoop + Spark MLlib to train machine learning models on petabyte-scale datasets
₹14–30 LPA · $95K–$155K USA
Hadoop Cluster Admin
Manage multi-node Hadoop clusters including capacity planning, patching, and DR
₹10–18 LPA · $70K–$115K USA
New Batch Starting Soon — Limited Seats

Ready to Master Big Data Hadoop?

Process petabyte-scale datasets and build world-class data engineering pipelines used at top tech companies.

Enroll Now Call Us WhatsApp
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