📋 Quick Enquiry:
Live Online Training — New Batches Starting
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.
No prior experience needed
7-day money-back guarantee
Placement support included
▶
Watch a free preview lecture
₹20,000
₹30,000
Save 10,000
✅ Demo Booked!
Trainer Venu's team will call you within 2 hours.
📋 Register for Free Demo
🎥 Live Online + Recorded Sessions
🐘 Real Hadoop Cluster Labs
📂 3 End-to-End Projects
📜 Certificate of Completion
🤝 Placement Support
♾️ Lifetime Recording Access
✅ Free Demo Before Enroll
Who Is This For
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
Career Outcomes
Big Data Professionals Earn Top Salaries
Big Data engineering skills form the foundation of all cloud data engineering careers. Companies across India hire thousands of big data engineers every year.
Entry Level
₹6–12 LPA
0–2 Years
Mid Level
₹12–22 LPA
2–5 Years
Senior Level
₹22–45+ LPA
5+ Years
Student Success Stories
1200+ Professionals Placed at Top Companies
★★★★★
"The PySpark and Kafka modules were very comprehensive. Trainer Venu made complex distributed computing concepts easy to understand. Got placed at TCS!"
SK
Suresh Kumar
Fresher → Big Data Engineer
✅ TCS · ₹8 LPA
★★★★★
"Great foundation for cloud data engineering. After this course I moved directly into Databricks training and got placed at HCL within 3 months!"
RD
Ramya Devi
SQL Dev → Data Engineer
✅ HCL · ₹14 LPA
★★★★★
"The Hive optimization and Spark Structured Streaming modules were exactly what enterprise companies look for. Excellent training!"
KR
Kishore Rao
ETL Dev → Big Data Engineer
✅ Infosys · ₹12 LPA
FAQs
Frequently Asked Questions
Is Big Data still relevant when companies are moving to cloud? ▾
Yes! Big Data skills (Spark, Kafka, Hive) are foundation skills used in ALL cloud platforms — AWS EMR, GCP Dataproc, Azure HDInsight, and Databricks all run Spark. These skills never expire.
Do I need Linux knowledge for this course? ▾
Basic Linux command-line knowledge is helpful. We include a quick Linux refresher in the first session covering everything you need for Hadoop and Spark labs.
Will this help me transition to Databricks/AWS/Azure? ▾
Absolutely. Big Data is the best foundation. Our students typically do Big Data training first, then move to Databricks or cloud-specific training for higher salaries.
Is there job placement support? ▾
Yes — we provide resume building, mock interviews, and placement assistance through our network of 150+ hiring partner companies.
What is the refund policy? ▾
7-day money-back guarantee. Attend the free demo — if not satisfied, full refund with no questions asked.