Hadoop Online Training
Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Hadoop Online Training
Hadoop Online Training Course
Master HDFS, MapReduce, Hive, Sqoop & Oozie with project-driven learning.
Course Overview
Prerequisties
Why Choose us
Course Content
Course Overview
Comprehensive Overview of Hadoop Training
This course provides an end-to-end learning experience designed to help you become proficient in Hadoop and its ecosystem. Through structured modules, hands-on labs, and industry projects, you’ll develop practical skills for managing real-world Big Data workloads efficiently.Key Modules Include
- Big Data Fundamentals: Understand the 5 Vs and Hadoop’s role in large-scale data processing.
- Core Concepts: Master HDFS and MapReduce for data storage and processing.
- Ecosystem Tools: Hive (analytics), Sqoop (migration), Pig (scripting), Oozie (workflow).
- Advanced Skills: Cluster setup, optimization, and managing production environments.
Prerequisties
Prerequisites for Hadoop Online Training
- Basic programming knowledge (Java recommended, Python/SQL helpful).
- Understanding of Linux commands and environments.
- Familiarity with databases (RDBMS concepts, SQL queries).
- A keen interest in solving Big Data challenges.
For beginners, pre-course materials are provided to help you get started.
Outcome of This Hadoop Training
- Design and deploy end-to-end Hadoop solutions.
- Analyze and process large datasets using Hive, Pig, and MapReduce.
- Build data workflows with Oozie and migrate data using Sqoop.
- Become industry-ready for data engineering and analytics roles.
Why Choose us
Why Choose Us for Hadoop Training
- Experienced Trainers: Learn from certified Big Data professionals with deep Hadoop expertise.
- Hands-On Learning: Real projects using datasets from finance, healthcare, and retail domains.
- Industry-Relevant Curriculum: Includes Hadoop 3.x updates and modern data engineering tools.
- Flexible Learning: Choose self-paced or live online instructor-led sessions.
- Career Support: Placement guidance, resume workshops, and mock interviews.
- Lifetime Access: Continuous access to recordings, materials, and updates.
- Capstone Project: Work with Hive, Pig, and Sqoop to complete an end-to-end Big Data project.
Course Content
Hadoop Online Training Course
This comprehensive Hadoop training course covers everything from Big Data fundamentals to advanced Hadoop ecosystem tools. Through a hands-on approach, you’ll gain expertise in HDFS, MapReduce, Hive, Pig, Sqoop, and Oozie — mastering how to design, process, and manage data at scale. Perfect for developers, analysts, and data engineers seeking to excel in Big Data.
Course Content
Module 1: Introduction to Big Data and Hadoop
- Overview of Big Data and its characteristics (Volume, Velocity, Variety, Veracity, Value)
- Real-world use cases: Retail, Healthcare, Finance
- What is Hadoop? Its need and evolution
- Core Components: HDFS and MapReduce
- Hadoop Ecosystem: Hive, Pig, Sqoop, Spark, Oozie
- Comparison with traditional systems
- Applications of Hadoop in the industry
Module 2: Hadoop Ecosystem and Architecture
- Overview of HDFS, MapReduce, and YARN
- Supporting tools: Hive, Sqoop, Pig, HBase, Spark
- Master-Slave Architecture and core components
- HDFS: NameNode, DataNode, Secondary NameNode
- YARN: ResourceManager, NodeManager, ApplicationMaster
- Cluster setup modes: Single-node, pseudo-distributed, fully distributed
- Configuration files: core-site.xml, hdfs-site.xml, mapred-site.xml
- Rack awareness and block placement strategies
Module 3: HDFS (Hadoop Distributed File System)
- Design principles, replication, and fault tolerance
- Read/write operations in HDFS
- HDFS commands (CLI and API-based)
- Data ingestion and management using Java API
- HDFS Federation and High Availability
Module 4: MapReduce Programming
- Framework basics, key-value concepts, and data flow
- Writing MapReduce programs in Java
- Advanced MapReduce: Partitioners, sorting, shuffling, custom Writable
- Performance tuning, combiners, and secondary sorting
- Real-world use cases: Weather analysis, log file processing
Module 5: Advanced Hadoop
- Distributed cache and side data distribution
- Input formats: Text, Sequence, Avro, XML
- Compression techniques: Snappy, Gzip, Bzip2
- Monitoring, debugging, and testing with MRUnit
- Scheduling and performance optimization
Module 6: Hive (Data Warehousing on Hadoop)
- Introduction to Hive architecture and HiveQL
- Installation and configuration
- Working with tables: internal, external, partitioned, and bucketed
- Joins (inner, outer, map-side), UDFs, and query optimization
- Advanced features: Views, indexing, windowing, and analytical functions
- Integration with Java and Thrift Server
Module 7: Sqoop (Data Transfer between Hadoop and RDBMS)
- Introduction, installation, and configuration
- Importing and exporting structured data
- Data migration from relational databases to Hadoop and vice versa
- Using Sqoop with Hive and HBase
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