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Big Data with Hadoop Training Key Features

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Practical Hadoop Cluster Labs

Get hands-on experience with distributed file systems, data processing, and analysis on live Hadoop environments and ecosystem tools.

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Flexible Online and In-Person Classes

Learn at your convenience through our classroom sessions at Ameerpet or Kukatpally, or join live interactive online classes from anywhere in the world.

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Dedicated Big Data Mentorship

Receive personalized assistance for all your big data projects and complex distributed computing queries from our experienced instructors during and after your course.

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Robust Career & Placement Guidance

We help you prepare for big data engineering interviews with mock sessions, resume optimization, and direct connections to job opportunities in leading data-driven companies.

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Real-World Big Data Projects

Gain invaluable experience by developing end-to-end solutions for processing, storing, and analyzing massive volumes of data using various Hadoop ecosystem components.

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Engaging Learning Community

Collaborate with a supportive community of peers and instructors, fostering enhanced big data skills, knowledge sharing, and valuable networking opportunities.

about us

Big Data with Hadoop Training Overview

Value Learning offers comprehensive Big Data with Hadoop training courses at both Ameerpet and Kukatpally (KPHB), Hyderabad. Our programs are meticulously designed to equip you with the practical skills needed to manage, process, and analyze massive datasets effectively.

Apache Hadoop is a fundamental framework for distributed storage and processing of very large data sets across clusters of computers. It forms the backbone of many modern big data architectures, enabling organizations to handle vast volumes of structured and unstructured data. Our expert-led training covers core Hadoop components like HDFS (Hadoop Distributed File System), MapReduce for parallel processing, and ecosystem tools such as Hive and Pig, ensuring you are proficient in solving complex big data challenges.

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Successful Learners

68k

Training Hours Delivered

540

Enterprise Projects Covered

Big Data with Hadoop Training Objectives

The Big Data with Hadoop course at Value Learning, delivered at our Ameerpet and Kukatpally (KPHB) centers in Hyderabad, is designed to give learners a robust understanding of big data concepts and the comprehensive Hadoop ecosystem.

Through this training, you will gain hands-on experience with HDFS for distributed storage, MapReduce for parallel processing, and tools like Hive and Pig for large-scale data analysis. You'll learn to work effectively with both structured and unstructured data in a big data environment.

The primary goal of the training is to empower learners to confidently design and implement robust big data solutions for enterprise-level data processing and analytics, addressing the challenges of massive data volumes.

To equip learners with comprehensive, practical experience in setting up, configuring, and working with Hadoop clusters, and solving real-world big data problems, preparing them for specialized roles in big data engineering and data architecture.

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Course Curriculum -Big Data with Hadoop

Overview:
  • Understanding Big Data: 3 Vs (Volume, Velocity, Variety) and beyond
  • Challenges of Traditional Data Processing
  • Introduction to Hadoop: History, Core Components, and Philosophy
  • Hadoop Ecosystem Overview: HDFS, MapReduce, YARN, Hive, Pig, etc.
  • Use Cases and Benefits of Big Data with Hadoop

  • HDFS Architecture: NameNode, DataNode, Secondary NameNode
  • Data Replication, Fault Tolerance, and High Availability
  • HDFS Commands for File Operations (put, get, ls, mkdir, rm)
  • Understanding Blocks, Rack Awareness, and Data Locality
  • Setting up a Single-Node Hadoop Cluster (Hands-on)

  • Introduction to MapReduce: Concepts and Working Flow
  • Mapper, Reducer, Combiner, Partitioner Functions
  • Writing Basic MapReduce Programs in Java (Word Count Example)
  • Input Formats, Output Formats, and Custom Writable Comparators
  • Understanding MapReduce Job Execution and Monitoring

  • YARN Architecture: ResourceManager, NodeManager, ApplicationMaster
  • Resource Management and Scheduling in Hadoop 2.x/3.x
  • Understanding Containers and Resource Allocation
  • Benefits of YARN: Multi-tenancy, Scalability, Flexibility
  • Monitoring YARN Applications and Cluster Health

  • Introduction to Apache Hive: Architecture and Components (Metastore)
  • HiveQL: SQL-like Queries for HDFS Data
  • Creating and Managing Hive Tables (Managed vs. External Tables)
  • Partitioning and Bucketing for Performance Optimization
  • Loading Data into Hive and Querying Complex Data Types

  • Introduction to Apache Pig and Pig Latin Scripting
  • Comparing Pig with MapReduce and Hive
  • Data Types, Operators, and Functions in Pig Latin
  • Loading, Storing, Filtering, Grouping, and Joining Data in Pig
  • Executing Pig Scripts in Local and MapReduce Mode

  • Introduction to NoSQL Databases and their Types (Key-Value, Document, Column-Family)
  • Apache HBase: Architecture, Data Model, and Operations
  • Apache Cassandra: Distributed, Highly Available NoSQL Database
  • Comparing HBase and Cassandra for Different Use Cases
  • Integrating NoSQL databases with Hadoop Ecosystem

  • Apache Sqoop: Importing/Exporting Data between RDBMS and Hadoop
  • Sqoop Commands: Import, Export, Codegen
  • Apache Flume: Collecting Log Data and Streaming Data
  • Flume Architecture: Agents, Sources, Channels, Sinks
  • Real-time Data Ingestion Strategies

  • Introduction to Apache Spark: Advantages over MapReduce
  • Spark Architecture: Driver, Executors, Cluster Manager
  • Resilient Distributed Datasets (RDDs): Concepts and Operations
  • Spark SQL for Structured Data Processing
  • Introduction to Spark Streaming and Machine Learning Libraries (MLlib)

  • Introduction to Apache Kafka: Distributed Streaming Platform
  • Kafka Architecture: Producers, Consumers, Brokers, Topics, Partitions
  • Publishing and Consuming Messages
  • Kafka Connect and Kafka Streams (overview)
  • Real-time Analytics with Kafka and Spark Streaming

  • Concept of Data Lake vs. Data Warehouse
  • Building Data Lakes with HDFS and Object Storage
  • Data Governance and Security in Data Lakes
  • Tools for Data Lake Management (e.g., Apache Atlas, Ranger)
  • Modern Data Architecture Patterns

  • Hadoop Cluster Setup: Multi-node Installation and Configuration
  • Monitoring Hadoop Cluster Health (Ganglia, Nagios - overview)
  • Troubleshooting Common Hadoop Issues
  • Hadoop Security (Kerberos - overview)
  • Backup and Recovery Strategies for Hadoop Clusters

  • Overview of AWS EMR, Azure HDInsight, Google Cloud Dataproc
  • Advantages of Cloud Big Data Platforms
  • Deploying and Managing Hadoop/Spark Clusters in the Cloud
  • Cost Optimization Strategies in Cloud Big Data
  • Serverless Big Data Processing (e.g., AWS Lambda, Google Cloud Functions)

  • Big Data in E-commerce and Retail (Personalization, Recommendation Systems)
  • Financial Services: Fraud Detection, Risk Management
  • Healthcare: Patient Data Analysis, Drug Discovery
  • Telecommunications: Network Optimization, Churn Prediction
  • Government and Public Sector Applications

  • Roles in Big Data: Hadoop Developer, Data Engineer, Big Data Architect
  • Building a Strong Portfolio for Big Data Jobs
  • Certifications in Hadoop and Spark Ecosystem
  • Emerging Trends: Data Mesh, Lakehouse Architecture, Serverless Big Data
  • Job Market for Big Data Professionals in Hyderabad, Telangana, India
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