Master Big Data Engineering — Hadoop, Spark, Kafka & Hive

Build a strong foundation in Big Data Engineering — Hadoop HDFS, Hive, PySpark, Kafka and HBase — with Trainer Venu. Essential skills for cloud data engineering careers at top MNCs.

⏱️ 60 Hours

📦 9 Modules

🔬 18+ Labs

🗂️ 3 Projects

🌐 Live Online

No prior experience needed

7-day money-back guarantee

Placement support included

Is This Course Right For You?

🎓

Freshers

Build foundational big data skills required by every data engineering role.

🗄️

SQL Developers

Move from SQL to distributed big data processing with Hive and Spark.

☁️

Aspiring Cloud Engineers

Big data is the foundation — then layer AWS/Azure/GCP on top.

📊

Data Analysts

Scale your analytics from single-machine to distributed big data platforms.

🔄

ETL Developers

Modernize legacy batch ETL to distributed Spark processing.

🏢

Enterprise Teams

Build on-premise or hybrid big data platforms for large organizations.

Tools Covered

🐘 Hadoop HDFS

⚡ Apache Spark

🐝 Hive

📨 Apache Kafka

🔌 HBase

🔄 Sqoop

🌊 Flume

📅 Oozie

🐖 Pig

🦒 ZooKeeper

🐍 PySpark

🔥 Databricks

☁️ AWS EMR

🌐 GCP Dataproc

Course Curriculum

9 Modules — Key Concepts

Here are the core topics you'll master. Each module includes hands-on labs with real Big Data access.

Module 01

Hadoop HDFS & MapReduce

HDFS — distributed storage, blocks, replication
NameNode, DataNode architecture
MapReduce — map, shuffle, reduce phases
YARN — resource management and job scheduling
Hadoop cluster setup and configuration

Module 02

Apache Hive

Hive architecture — Metastore, Driver, Compiler
HiveQL — SQL on HDFS data
Partitioned and bucketed tables
ORC and Parquet file formats in Hive
Hive optimization — vectorization, CBO, TEZ

Module 03

Apache Spark & PySpark

Spark architecture — Driver, Executors, DAG
RDDs vs DataFrames vs Datasets
PySpark transformations and actions
Spark SQL — HiveContext, SparkSession
Spark Streaming and Structured Streaming

Module 04

Apache Kafka

Kafka architecture — brokers, topics, partitions
Producers and consumers API
Consumer groups and offset management
Kafka Connect — source and sink connectors
Kafka Streams — real-time stream processing

Module 05

HBase & NoSQL

HBase architecture — HMaster, RegionServer
Row key design for HBase
HBase Shell and Java/Python API
HBase integration with Spark and Hive
When to use HBase vs relational databases

Module 06

Ingestion Tools — Sqoop & Flume

Sqoop — RDBMS to HDFS bulk import/export
Sqoop incremental imports and deltas
Flume — log streaming to HDFS/Kafka
Flume agents — source, channel, sink
Oozie — workflow scheduling for big data

M01

Hadoop HDFS — Distributed Storage

⏱️ 6 Hours● Beginner

▾

Hadoop ecosystem overview — what fits where

HDFS architecture — blocks, replication, rack-awareness

NameNode — metadata management, secondary NN

DataNode — block storage and heartbeats

HDFS commands — put, get, ls, mkdir, rm, chmod

HDFS Federation — scaling the namespace

High Availability NameNode — ZooKeeper-based HA

Hadoop cluster setup — single and multi-node

🔬 HDFS Cluster Setup Lab📝 Quiz: HDFS Architecture

M02

MapReduce & YARN

⏱️ 5 Hours● Beginner

▾

MapReduce programming model — map, combiner, reducer

YARN — Yet Another Resource Negotiator

ApplicationMaster, NodeManager, ResourceManager

MapReduce job execution lifecycle

Input formats and output formats

Counters and custom counters

MapReduce optimization — combiners, partitioners

🔬 Word Count MapReduce Job

M03

Apache Hive — SQL on Hadoop

⏱️ 7 Hours● Intermediate

▾

Hive Metastore — schema-on-read vs schema-on-write

HiveQL — DDL, DML, subqueries, window functions

Managed vs External tables

Partitioned tables — static and dynamic partitioning

Bucketed tables — sampling optimization

ORC and Parquet formats — columnar storage

Hive Tez execution engine

Cost-Based Optimizer (CBO)

🔬 Hive Analytics on HDFS🏗️ Project: Hive Data Warehouse

M04

Apache Spark Core

⏱️ 8 Hours● Intermediate

▾

Spark architecture — Driver, Executors, Cluster Manager

RDDs — create, transform, actions

DataFrames — structured data processing

SparkSession and SparkContext

Transformations — map, filter, flatMap, groupByKey

Actions — collect, count, take, saveAsTextFile

Caching and persistence levels

Broadcast variables and accumulators

🔬 Spark ETL Pipeline Lab

M05

PySpark — DataFrame API

⏱️ 8 Hours● Intermediate

▾

SparkSession setup and configuration

Read CSV, JSON, Parquet, ORC, Delta files

DataFrame transformations — select, filter, withColumn

Aggregations — groupBy, agg, pivot, rollup

Joins — inner, outer, cross, broadcast joins

Window functions — rank, lag, lead, running sums

Spark SQL — register DataFrames as temp views

Writing DataFrames — Parquet, Delta, JDBC

🔬 PySpark Analysis Lab📝 Quiz: PySpark

M06

Apache Kafka — Event Streaming

⏱️ 7 Hours● Intermediate

▾

Kafka use cases — event sourcing, log aggregation, CDC

Kafka architecture — brokers, topics, partitions, replicas

Producer API — keys, partitioning strategies

Consumer API — poll loop, commits, rebalancing

Consumer Groups — parallel consumption

Kafka Connect — source connectors (JDBC, S3, Debezium)

Kafka Connect — sink connectors (HDFS, BigQuery)

Kafka Streams — stateless and stateful processing

🔬 Kafka Producer-Consumer Lab🏗️ Project: Kafka→Spark Streaming

M07

HBase, Sqoop & Flume

⏱️ 6 Hours● Intermediate

▾

HBase architecture — regions, compaction, bloom filters

HBase Shell — create, put, get, scan, delete

Row key design patterns for HBase

HBase with Spark — Spark-HBase connector

Sqoop import — full and incremental from RDBMS

Sqoop export — from HDFS to RDBMS

Flume agents — Avro, Thrift, syslog sources

Flume HDFS sink with partitioning

🔬 HBase Design Lab

M08

Spark Streaming & Structured Streaming

⏱️ 7 Hours● Advanced

▾

DStream API — Spark Streaming basics

Structured Streaming — DataFrame-based streaming

Kafka → Spark Structured Streaming

Watermarks for late data handling

Output modes — append, update, complete

Streaming aggregations and joins

Checkpointing for fault tolerance

Kafka → Spark → HBase real-time pipeline

🔬 Real-time Streaming Pipeline🏗️ Project: End-to-End Big Data Pipeline

M09

Big Data to Cloud & Career Prep

⏱️ 6 Hours● Advanced

▾

Migration — Hadoop to AWS EMR / GCP Dataproc

AWS EMR — Spark and Hive on cloud

GCP Dataproc — managed Hadoop/Spark

Delta Lake — modernize Hive with ACID transactions

Databricks as the future of Spark

Big Data interview questions — Top 50

Resume writing for big data roles

📝 Big Data Interview Prep

Master Big Data Engineering — Hadoop, Spark, Kafka & Hive

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9 Modules — Key Concepts

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