Course Overview

The world generates 2.5 quintillion bytes of data every single day — and the enterprises that can extract value from their share of that deluge gain decisive competitive advantages. Big Data technologies built on the Hadoop ecosystem are what make this extraction possible at scale: processing terabytes of log data, indexing billions of e-commerce events, analysing years of IoT sensor readings, or correlating millions of financial transactions in near-real-time. CETPA Infotech's Best Big Data Hadoop Training in Noida equips you with the full Hadoop technology stack and the complementary skills to build, manage, and optimise big data pipelines in enterprise environments.

The course begins with a thorough grounding in the Hadoop architecture — HDFS for distributed storage, YARN for resource management, and MapReduce as the foundational processing paradigm — before progressing to the modern big data tools that practitioners actually use in production. Apache Spark is the centrepiece of the advanced modules, covering Spark Core for distributed computation, Spark SQL for structured data processing, Spark Streaming for real-time data pipelines, and MLlib for distributed machine learning at scale. Hive, Pig, HBase, Sqoop, and Flume round out the Hadoop ecosystem coverage.

A dedicated module on Apache Kafka teaches you how to design and operate the event-streaming backbones that power Netflix, LinkedIn, and Uber's real-time data infrastructure. Cloud big data platforms — AWS EMR, Azure HDInsight, and Google Dataproc — are covered in the final modules, ensuring that your skills are relevant whether your employer runs on-premise Hadoop clusters or has migrated to cloud-managed services. CETPA's placement team connects Hadoop-certified graduates with Big Data Engineer and Data Platform Engineer roles at telecom companies, BFSI organisations, retail chains, and technology consulting firms.

Course Curriculum

• Big Data Concepts: Volume, Velocity, Variety, Veracity
• Hadoop Architecture: HDFS, YARN, NameNode, DataNode
• MapReduce Programming Model
• Apache Hive: HQL, Partitioning, Bucketing, ORC/Parquet
• Apache Pig: Pig Latin Scripting
• HBase: NoSQL Column-Family Database on HDFS
• Sqoop: RDBMS ↔ HDFS Data Transfer
• Apache Flume: Log Ingestion Pipeline
• Apache Spark: RDDs, DataFrames, Datasets
• Spark SQL & Catalyst Optimiser
• Spark Streaming & Structured Streaming
• MLlib: Distributed Machine Learning
• Apache Kafka: Topics, Producers, Consumers, Streams
• Cloud Big Data: AWS EMR / GCP Dataproc
• Capstone: Real-Time Data Pipeline with Kafka + Spark