📈 Your Market Value After This Course
What you'll achieve and how much you can earn after completing Big Data
Fresher / Entry Level
₹6 - 9 LPA
0-2 years experience
- Junior Big Data Engineer
- Hadoop Developer Trainee
⭐ Most Common
Mid Level
₹10 - 20 LPA
2-5 years experience
- Big Data Engineer
- Spark Developer
Senior / Expert Level
₹22 - 50+ LPA
5+ years experience
- Lead Big Data Architect
- Data Engineering Manager
🎯 Job Roles You Can Apply For
Big Data Engineer
Hadoop Developer
Spark Developer
Data Engineer
Data Architect
Big Data Analyst
⚡ Skills You'll Master
Hadoop
HDFS
MapReduce
Apache Spark
Apache Kafka
Apache Hive
HBase
PySpark
Scala
Linux
📚 Complete Course Syllabus
Master every aspect with our comprehensive curriculum
Module 1: Introduction to Big Data
- What is Big Data - 5 Vs (Volume, Velocity, Variety, Veracity, Value)
- Traditional vs Big Data Technologies
- Big Data Use Cases & Applications
- Big Data Architecture Overview
- Data Processing - Batch vs Real-time
- Introduction to Hadoop Ecosystem
Module 2: Linux & Python for Big Data
- Linux Basics - Commands, File System, Permissions
- Shell Scripting for Big Data
- Python Basics - Variables, Loops, Functions
- Python Data Structures - Lists, Dictionaries, Sets
- File Handling in Python
- Working with CSV, JSON Data
- Python for Data Processing
Module 3: Hadoop Ecosystem
- Hadoop Architecture Overview
- Hadoop Cluster Setup - Single Node & Multi-node
- Hadoop Configuration Files
- Hadoop Daemons - NameNode, DataNode, ResourceManager
- Hadoop Installation & Setup
- Hadoop Ecosystem Components Overview
Module 4: Hadoop Distributed File System (HDFS)
- HDFS Architecture - NameNode, DataNode, Secondary NameNode
- HDFS Commands - ls, put, get, copy, mv
- HDFS Block Replication & Fault Tolerance
- Data Ingestion into HDFS
- HDFS Web UI Monitoring
- HDFS Permissions & Security
Module 5: MapReduce Framework
- Introduction to MapReduce Framework
- MapReduce Phases - Map, Shuffle, Reduce
- Writing MapReduce Programs in Java
- WordCount Example
- InputFormats & OutputFormats
- Combiner & Partitioner
- MapReduce Optimization Techniques
- YARN Architecture - ResourceManager, NodeManager
Module 6: Apache Hive
- Introduction to Hive - Data Warehouse
- Hive Architecture & Metastore
- HiveQL - Creating Tables, Loading Data
- Partitioning & Bucketing
- Hive Queries - SELECT, JOIN, GROUP BY
- User Defined Functions (UDF)
- Hive vs Traditional Databases
Module 7: Apache HBase
- Introduction to HBase - NoSQL Database
- HBase Architecture - HMaster, RegionServer
- HBase Data Model - RowKey, Column Family
- HBase Shell Commands
- Java API for HBase
- HBase Schema Design
- HBase vs Hive
Module 8: Apache Spark
- Introduction to Apache Spark - Speed & Ease of Use
- Spark Architecture - Driver, Executors, Cluster Manager
- RDD (Resilient Distributed Datasets)
- Spark Transformations & Actions
- Spark DataFrames & Spark SQL
- Spark Streaming - Real-time Data Processing
- Spark MLlib - Machine Learning Library
- PySpark - Python API for Spark
- Spark Optimization Techniques
Module 9: Apache Kafka
- Introduction to Apache Kafka - Distributed Streaming
- Kafka Architecture - Producers, Consumers, Brokers
- Topics, Partitions, Offsets
- Kafka Producer & Consumer APIs
- Kafka Consumer Groups
- Kafka Connect & Kafka Streams
- Integrating Kafka with Spark Streaming
Module 10: Big Data Projects & Capstone
- Project 1: Log Analysis using Hadoop MapReduce
- Project 2: Data Warehousing with Hive
- Project 3: Real-time Data Streaming with Kafka & Spark
- Project 4: Real-time Dashboard with Spark Streaming
- Capstone Project - End-to-End Big Data Pipeline
⭐ Why Choose Tekksol Global?
We provide the best learning experience with industry experts
Expert Trainers
Learn from industry professionals with 12+ years of Big Data experience
Hands-on Projects
Work on 6+ real-time Big Data projects with production datasets
Industry Certification
Get globally recognized Big Data certification
100% Placement Support
Tie-ups with 260+ companies for Big Data roles
Resume Building
Professional resume & portfolio with Big Data projects
Mock Interviews
Regular mock interviews with detailed feedback
💻 Real-Time Projects
Build impressive portfolio with industry-relevant projects
Web Server Log Analysis
Analyze web server logs using Hadoop MapReduce to find most visited pages, user sessions, and error rates.
Hadoop
MapReduce
HDFS
Java
Real-time Twitter Streaming
Build a real-time Twitter data pipeline using Kafka and Spark Streaming for sentiment analysis.
Kafka
Spark Streaming
Python
Elasticsearch
E-Commerce Data Warehouse
Create a data warehouse solution using Hive for sales analysis, customer segmentation, and inventory management.
Hive
HDFS
Spark SQL
Presto
🚀 Placement Assistance
We're committed to your success beyond the course
Placement Support Includes:
- Resume & LinkedIn Profile Building
- Aptitude & Technical Training
- Mock Interviews with Industry Experts
- Soft Skills & Communication Training
Our Hiring Partners:
- 500+ Hiring Partners
- Unlimited Interview Opportunities
- Job Portal Access
- Life-long Placement Support
Our Top Hiring Partners
❓ Frequently Asked Questions
Got questions? We've got answers
What are the prerequisites for Big Data course?
Basic programming knowledge (Java/Python) and SQL are recommended. We cover Linux and Python basics in the course.
What is the duration of the course?
The course duration is 6 months (200 hours) with flexible batch timings.
Will I learn both Hadoop and Spark?
Yes, the course covers Hadoop ecosystem (HDFS, MapReduce, Hive, HBase) and Apache Spark in depth.
What projects will I build?
You will build 6+ projects including Log Analysis, Twitter Streaming, and E-Commerce Data Warehouse.
Is placement assistance provided?
Yes, we provide 100% placement assistance with 260+ hiring partners.
🚀 Ready to Start Your Big Data Journey?
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