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Big Data Analytics with Hadoop

Discover how to leverage Hadoop for handling and analyzing large datasets.

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. Discover how to leverage Hadoop for handling and analyzing large datasets.

  2. This course covers the essential components of the Hadoop ecosystem, such as HDFS, MapReduce, and YARN.

  3. Gain skills in managing and analyzing big data through Hadoop’s distributed storage and processing features.

  4. Explore additional tools like Hive, Pig, and HBase for effective data processing and analysis.

  5. Enhance your ability to work with big data at scale through practical applications and insights.

CERTIFICATION:

  1. Earn a Certified Big Data Analyst with Hadoop credential, demonstrating your expertise in handling and analyzing large datasets using the Hadoop ecosystem.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1. Comprehend the structure of Hadoop, focusing on HDFS, MapReduce, and YARN components.

  2. Establish and oversee a Hadoop cluster for efficient big data management.

  3. Develop and enhance MapReduce tasks for handling extensive datasets.

  4. Leverage Hive, Pig, and HBase for effective data querying, transformation, and storage solutions.

  5. Create data processing workflows for both batch and real-time analytical needs.

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Course Curriculum

Introduction to Big Data
  1. What is Big Data?
    • Key characteristics: Volume, Variety, Velocity, Veracity, and Value.
    • The impact of Big Data on industries and technology.
  2. Challenges of Big Data
    • Managing large data volumes, storage, real-time data processing, and data analysis.
  3. The Role of Hadoop in Big Data
    • Overview of Hadoop and its ecosystem.
    • Key components: HDFS (Hadoop Distributed File System), MapReduce, YARN, and Hadoop Ecosystem tools.
Hadoop Architecture
  1. Introduction to Hadoop Ecosystem
    • HDFS: Architecture, features, and working principles.
    • YARN: Resource management and job scheduling.
    • MapReduce: Distributed processing and job execution.
  2. Components of Hadoop
    • HDFS: Storing large datasets across multiple machines.
    • YARN: Managing resources and scheduling jobs.
    • MapReduce: Processing large datasets in a parallel, distributed manner.
Setting Up Hadoop
  1. Hadoop Installation
    • Installing Hadoop on a single machine (pseudo-distributed mode) and on a cluster.
    • Configuration of core-site.xml, hdfs-site.xml, mapred-site.xml, and yarn-site.xml files.
  2. Hadoop File System (HDFS) Commands
    • Managing HDFS directories and files using command-line interface (CLI).
    • Hadoop commands for data management: hadoop fs -put, hadoop fs -get, hadoop fs -ls, etc.
  3. Setting Up Hadoop Cluster
    • Configuration for multi-node cluster setup.
    • Configuring NameNode, DataNode, ResourceManager, and NodeManager.
MapReduce Programming Model
  1. Introduction to MapReduce
    • The concept of distributed data processing in MapReduce.
    • Key components: Mapper, Reducer, and Driver.
  2. MapReduce Jobs
    • Writing a basic MapReduce program (Word Count Example).
    • Understanding Mapper and Reducer classes.
    • Input and output formats in MapReduce.
  3. MapReduce Job Execution
    • How MapReduce jobs run on Hadoop clusters.
    • Task execution: Splits, Mappers, Reducers, and shuffle operations.
  4. Optimizing MapReduce Jobs
    • Best practices to optimize MapReduce jobs for performance.
    • Tuning memory usage, reducing I/O, and parallelizing jobs.
Data Storage and Management with HDFS
  1. Working with HDFS
    • Understanding how HDFS stores data in blocks across multiple machines.
    • How replication ensures data reliability and fault tolerance.
  2. Data Input and Output
    • Reading and writing data to HDFS using various formats: text, Avro, Parquet, ORC.
  3. HDFS Data Security
    • User authentication and authorization: Kerberos.
    • Managing permissions for data stored in HDFS.
Hadoop Ecosystem Tools
  1. Hive: SQL Interface for Hadoop
    • Introduction to Apache Hive for querying large datasets using SQL-like syntax.
    • Writing HiveQL queries and performing data analysis in Hadoop.
    • Managing schema, tables, and partitions in Hive.
  2. Pig: High-Level Data Flow Language
    • Introduction to Apache Pig and its language (Pig Latin).
    • Writing Pig scripts for data transformation and processing.
    • Using Pig for ETL (Extract, Transform, Load) operations.
  3. HBase: NoSQL Database for Hadoop
    • Introduction to Apache HBase for real-time read/write operations on large datasets.
    • Setting up and managing HBase clusters and tables.
    • Using HBase for key-value data storage and analysis.
  4. Sqoop: Data Import and Export
    • Using Apache Sqoop for importing and exporting data between Hadoop and relational databases.
    • Optimizing Sqoop jobs for data transfer.
  5. Flume: Data Ingestion
    • Introduction to Apache Flume for ingesting log data into Hadoop.
    • Setting up Flume agents to capture streaming data from different sources.
Data Processing and Analytics with Hadoop
  1. Batch Processing with MapReduce
    • Large-scale data processing using MapReduce for batch jobs.
    • Creating and managing batch jobs in Hadoop.
  2. Real-Time Data Processing
    • Introduction to Apache Kafka for stream processing in Hadoop.
    • Integrating Kafka with Hadoop for real-time data ingestion and processing.
  3. Data Transformation and Aggregation
    • Using MapReduce, Hive, and Pig for data cleaning, transformation, and aggregation.
    • Performing analytics on large datasets using SQL and custom functions.
Capstone Project
  1. End-to-End Big Data Analytics Project
    • Build a full-scale Big Data solution with Hadoop: data ingestion, storage, transformation, and analysis.
    • Example projects: Customer analytics, fraud detection, sentiment analysis, real-time log analytics, etc.

Training Features

Hands-on Projects

Work on real-world datasets for data processing, storage, and analysis using Hadoop ecosystem tools.

Comprehensive Understanding of Hadoop Ecosystem

Gain practical knowledge of key Hadoop tools such as HDFS, MapReduce, Hive, Pig, and HBase.

Real-Time Data Processing

Learn how to process real-time data streams using Kafka and Flume, and integrate them with Hadoop.

Machine Learning Integration

Apply machine learning models with Hadoop data using Apache Mahout.

Optimizing and Securing Hadoop Clusters

Master performance tuning and security best practices for Hadoop clusters.

Certification

Receive a certificate validating your skills in Big Data Analytics with Hadoop.

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