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Big Data Development

Master Large-Scale Data Processing

Learn to process and analyze massive datasets using Hadoop, Spark, and modern big data technologies. Build scalable data pipelines and become a Big Data expert.

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Duration

8 Months

Minimum Training Period

Certification

Industry

Recognized Certificate

Batch Size

20-25

Students per Batch

Placement

95%

Success Rate

Course Overview

Big Data is revolutionizing how businesses make decisions. This comprehensive program covers the entire big data ecosystem including Hadoop, Spark, NoSQL databases, and real-time data processing. Learn to handle petabytes of data and extract valuable insights from massive datasets.

With hands-on experience in industry-standard tools and frameworks, you'll be equipped to design and implement scalable data solutions. This course combines theoretical knowledge with practical projects to prepare you for high-demand big data roles.

What You'll Learn

  • Hadoop & MapReduce Framework

    Master distributed storage and processing with Hadoop ecosystem

  • Apache Spark & Scala

    Build high-performance data processing applications

  • NoSQL Databases

    Work with MongoDB, Cassandra, and HBase for scalable data storage

  • Data Processing & Analytics

    Process and analyze large-scale datasets efficiently

  • Real-time Data Streaming

    Apache Kafka, Storm, and real-time data pipelines

  • Data Warehousing

    Hive, Pig, and data warehousing solutions

  • Cloud Big Data

    AWS EMR, Azure HDInsight, and Google Cloud Dataproc

  • Data Pipeline Architecture

    Design and implement end-to-end data pipelines

Course Highlights

  • Live interactive classes
  • 12+ Big Data projects
  • Cluster environment access
  • Industry expert instructors
  • Lifetime access to materials
  • Placement assistance
  • Resume building support
  • 24/7 doubt resolution
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Course Curriculum

Detailed Syllabus

Comprehensive module-wise breakdown of the course content

01

Big Data Fundamentals

  • • Introduction to Big Data
  • • Big Data Characteristics (5 V's)
  • • Big Data Architecture
  • • Distributed Systems Concepts
  • • CAP Theorem
  • • Data Storage Solutions
  • • Batch vs Real-time Processing
  • • Big Data Use Cases
02

Hadoop Ecosystem

  • • Hadoop Architecture
  • • HDFS (Hadoop Distributed File System)
  • • MapReduce Framework
  • • YARN Resource Manager
  • • Hadoop Cluster Setup
  • • Data Ingestion & Storage
  • • Hadoop Commands & Operations
  • • Hadoop Security
03

Apache Spark

  • • Spark Architecture & RDD
  • • Spark Core & Transformations
  • • Spark SQL & DataFrames
  • • Spark Streaming
  • • MLlib (Machine Learning)
  • • GraphX for Graph Processing
  • • Scala Programming for Spark
  • • PySpark Development
04

NoSQL Databases

  • • NoSQL Database Types
  • • MongoDB (Document Store)
  • • Cassandra (Column-Family Store)
  • • HBase (Hadoop Database)
  • • Redis (Key-Value Store)
  • • Neo4j (Graph Database)
  • • Data Modeling in NoSQL
  • • Consistency & Availability
05

Data Warehousing

  • • Apache Hive Fundamentals
  • • HiveQL (Hive Query Language)
  • • Hive Tables & Partitioning
  • • Apache Pig & Pig Latin
  • • Data Warehousing Concepts
  • • ETL Processes
  • • Apache Sqoop for Data Transfer
  • • Apache Flume for Log Data
06

Real-time Processing

  • • Apache Kafka Architecture
  • • Kafka Producers & Consumers
  • • Kafka Streams API
  • • Apache Storm Topology
  • • Stream Processing Concepts
  • • Apache Flink
  • • Real-time Analytics
  • • Event-Driven Architecture
07

Cloud Big Data

  • • AWS Big Data Services (EMR, S3)
  • • Azure HDInsight
  • • Google Cloud Dataproc
  • • Cloud Data Warehouses
  • • Databricks Platform
  • • Cloud Data Migration
  • • Cost Optimization
  • • Cloud Security Best Practices
08

Capstone Projects

  • • E-commerce Analytics Platform
  • • Social Media Data Analysis
  • • IoT Data Processing Pipeline
  • • Real-time Fraud Detection
  • • Log Analysis System
  • • Recommendation Engine
  • • Data Lake Implementation
  • • Production Deployment

Prerequisites

To get the most out of this course:

  • Programming Knowledge

    Java or Python programming experience required

  • Database Basics

    Understanding of SQL and database concepts

  • Linux Commands

    Basic Linux/Unix command line knowledge

  • Dedication

    25-30 hours per week for 8 months

Career Opportunities

Big Data professionals are in high demand:

Big Data Engineer

₹6-14 LPA

Hadoop Developer

₹5-12 LPA

Spark Developer

₹7-15 LPA

Data Engineer

₹6-13 LPA

Start Your Journey

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