Hadoop 101 Training

Live Online & Classroom Enterprise Training

Hadoop 101 introduces the fundamentals of Hadoop, including its distributed storage (HDFS) and processing framework (MapReduce). It covers the Hadoop ecosystem, setup, and real-world applications for big data analytics.

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What is Hadoop 101 Training about?

course provides an introductory overview of Apache Hadoop, a widely used framework for distributed storage and processing of large datasets. Participants will learn the core components of Hadoop, such as HDFS (Hadoop Distributed File System) and MapReduce, along with the ecosystem tools like Hive and Pig. This course is designed to build a solid foundation for working with big data using Hadoop.

What are the objectives of Hadoop 101 Training ?

Foundational Knowledge of Hadoop: Understand the architecture, components, and working of Apache Hadoop.
Data Storage with HDFS: Learn how Hadoop stores and manages large datasets across distributed systems.
Data Processing with MapReduce: Gain insights into the programming model for processing data in Hadoop.
Introduction to Hadoop Ecosystem Tools: Explore additional tools like Hive, Pig, and HBase that complement Hadoop.
Setting Up Hadoop Environment: Learn how to install and configure Hadoop on a local or cloud-based cluster.

Who is Hadoop 101 Training for?

Data Engineers: Professionals seeking to understand and work with distributed data systems.
Big Data Enthusiasts: Beginners who want to explore big data technologies.
IT Professionals: System administrators and database professionals looking to transition into big data roles.
Developers: Software engineers interested in processing and analyzing large datasets.
Students: Individuals pursuing a career in data science or analytics

Available Training Modes

Live Online Training

2 Days

Self-Paced Training

10 Hours

Course Outline Expand All

Expand All

Module 1: Introduction to Hadoop

The evolution of big data and the need for Hadoop

Overview of Hadoop architecture

Key components: HDFS and MapReduce

Module 2: Hadoop Distributed File System (HDFS)

Understanding HDFS and its role in Hadoop

Key features of HDFS (scalability, fault tolerance)

Working with files in HDFS

Module 3: MapReduce Programming Model

Basics of MapReduce and its workflow

Writing and executing MapReduce jobs

Practical examples of MapReduce applications

Module 4: Introduction to Hadoop Ecosystem

Overview of tools like Hive, Pig, HBase, and Sqoop

Introduction to data querying with Hive

Data transformation with Pig

Module 5: Setting Up Hadoop Environment

Installing and configuring Hadoop on a single-node cluster

Introduction to multi-node cluster setup

Cloud-based Hadoop options (AWS EMR, Azure HDInsight)

Module 6: Real-World Use Cases

Hadoop applications in industries like finance, healthcare, and retail

Use cases of Hadoop for data analytics and ETL processes

Module 7: Best Practices and Future Trends

Best practices for working with Hadoop

Challenges and limitations of Hadoop

Emerging technologies in the big data space

Who is the instructor for this training?

The trainer for this Hadoop 101 Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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