Big Data & Hadoop Ecosystem
Master the Hadoop ecosystem (HDFS, MapReduce, Spark, Hive, Pig) and process large-scale data efficiently using industry-standard tools and frameworks.
Modules
- HDFS Architecture
- YARN
- MapReduce Basics
- HiveQL
- Partitioning & Bucketing
- Pig Latin Scripts
- Spark Core RDDs
- DataFrames
- Spark SQL
- MLlib: Machine Learning
- Structured Streaming
- GraphX
- Sqoop, Flume, Kafka
- Workflow: Oozie & Airflow
- ETL Pipeline
- Real-time Dashboard
- Batch Analytics
Industry Insights
88%
Industry Relevance
High
Market Demand
7 LPA+
Avg. Salary
Ready to start learning?
Your Learning Roadmap
Follow this path to mastery. Our AI guide leads the way.
Hadoop Foundations
12 hrsLearn HDFS, YARN, and distributed computing basics.
MapReduce & Data Processing
10 hrsMaster MapReduce programming and job optimization.
Hive & Pig for ETL
12 hrsWrite HiveQL queries and Pig scripts for data pipelines.
Spark Core & SQL
14 hrsLearn RDDs, DataFrames, and Spark SQL operations.
Advanced Spark & Streaming
12 hrsImplement MLlib, GraphX, and Structured Streaming.
Data Ingestion Tools
10 hrsIntegrate Sqoop, Flume, and Kafka for data movement.
Workflow Automation
5 hrsOrchestrate pipelines using Oozie & Airflow.
Capstone Projects
5 hrsBuild ETL, batch, and real-time analytics projects.
Why study Big Data & Hadoop?
- Companies process petabytes of data daily using Hadoop and Spark.
- Skills in distributed computing and SQL-on-Hadoop are in rising demand.
- Learn ETL, streaming, and analytics pipelines at scale.
- Ideal for Big Data Engineer, Data Lake Architect, and Analytics roles.