About This Course
This is India’s most comprehensive Big Data Engineering Training
covering the complete Hadoop ecosystem — HDFS, Hive, Sqoop, Spark,
Scala, Kafka, Oozie, Airflow, Spark SQL, Spark Streaming and AWS
Big Data services — all with maximum hands-on labs.
You will build real production-grade big data pipelines used by
top companies like Amazon, TCS, Infosys, Wipro and Accenture.
What You Will Learn
✅ Master Hadoop HDFS architecture & YARN resource management
✅ Write advanced HiveQL — partitioning, bucketing, optimization
✅ Ingest data from RDBMS to HDFS using Apache Sqoop
✅ Build batch ETL pipelines with PySpark & Spark SQL
✅ Write Spark jobs in Scala — type-safe big data processing
✅ Process real-time data with Spark Structured Streaming & Kafka
✅ Orchestrate pipelines with Apache Airflow and Oozie
✅ Optimize Spark jobs — AQE, skew handling, broadcast joins
✅ Deploy Big Data workloads on AWS EMR & S3
✅ Master file formats — Parquet, ORC, Avro, Delta
✅ Build 3 end-to-end big data projects for your resume
✅ Crack Big Data & Spark developer interviews at top MNCs
Course Modules at a Glance
Module 1 — Big Data Fundamentals & Hadoop Ecosystem
Module 2 — Hadoop HDFS & YARN
Module 3 — Hive — SQL on Hadoop
Module 4 — Sqoop — RDBMS to HDFS Ingestion
Module 5 — Apache Spark Core Fundamentals
Module 6 — PySpark DataFrame API
Module 7 — Spark SQL
Module 8 — Spark Performance Optimization
Module 9 — Scala for Spark
Module 10 — Spark Streaming — Real-Time Processing
Module 11 — Apache Kafka — Real-Time Messaging
Module 12 — Apache Oozie — Hadoop Workflow Scheduler
Module 13 — Apache Airflow — Modern Pipeline Orchestration
Module 14 — AWS for Big Data Engineers
Module 15 — Big Data Pipeline Architecture Patterns
Module 16 — Big Data File Formats & Compression
Module 17 — End-to-End Big Data Projects
Module 18 — Certification Preparation
Who Is This Course For
👨💻 Software developers moving into Big Data Engineering
🔄 SQL / ETL developers upgrading to Hadoop & Spark
☁️ Professionals wanting to migrate from Hadoop to Cloud
📊 Data Analysts transitioning into Big Data Engineering
🎓 Graduates targeting Big Data Engineer roles at MNCs
🏆 Professionals preparing for Spark or Cloudera certification
Prerequisites
– Basic Python or Java knowledge — helpful not mandatory
– Basic SQL — SELECT, JOIN, GROUP BY
– No prior Hadoop or Spark experience needed
– Linux command line basics — helpful
– Laptop with internet — labs on cloud VMs provided
Course Highlights
🕐 65+ Hours of live instructor-led training
🛠️ 35+ Hands-On Labs on real cluster
📁 3 Real-World Projects for your portfolio
📝 18 Modules — fundamentals to production level
⚡ Both PySpark & Scala Spark covered
🌊 Spark Streaming with Kafka — real-time pipelines
🎼 Airflow + Oozie orchestration — both covered
☁️ AWS EMR & Glue — cloud big data included
🏆 Interview prep + certification guidance
🎥 Lifetime Access to all recorded sessions
💬 WhatsApp Support + weekly doubt clearing
📄 Resume Review + mock interviews
🤝 Placement support — 200+ hiring partners
Technologies Covered
🐘 Hadoop — HDFS & YARN
🐝 Apache Hive — SQL on Hadoop
⚡ Apache Spark — PySpark & Scala
🔵 Spark SQL — Unified Analytics
🌊 Spark Structured Streaming
📨 Apache Kafka — Real-Time Messaging
🔄 Apache Sqoop — RDBMS Ingestion
🎼 Apache Airflow — Modern Orchestration
⚙️ Apache Oozie — Hadoop Scheduler
☕ Scala — Type-Safe Spark Programming
☁️ AWS EMR, S3, Glue, Athena
📦 File Formats — Parquet, ORC, Avro, Delta
🗜️ Compression — Snappy, Gzip, ZSTD
3 Projects You Will Build
Project 1 — Retail Sales Analytics Batch Pipeline
Sqoop → HDFS → PySpark → Spark SQL →
Hive Gold Tables → Airflow Orchestration
Project 2 — Real-Time Clickstream Streaming Pipeline
Kafka → Spark Structured Streaming →
HDFS → Hive → Real-Time Dashboard
Project 3 — Hadoop to AWS Cloud Migration
HDFS → S3 → Glue ETL → Athena →
Cost-Optimized Cloud Architecture
Career Opportunities After This Course
💼 Big Data Engineer — ₹8 LPA to ₹25 LPA
💼 Spark Developer — ₹10 LPA to ₹28 LPA
💼 Hadoop Developer — ₹8 LPA to ₹20 LPA
💼 Data Pipeline Engineer — ₹10 LPA to ₹28 LPA
💼 Senior Big Data Engineer — ₹18 LPA to ₹40 LPA
Alumni working at Amazon, TCS, Infosys, Wipro,
HCL, Accenture, Capgemini and 200+ companies.
Why Learn From Us
🏅 India’s #1 rated Big Data Engineering training
👨🏫 Trainer with 10+ years Hadoop & Spark production experience
🛠️ Both PySpark & Scala Spark — not just one language
🏢 5000+ students trained — 95% placement rate
📚 Course updated with latest Spark & Airflow features
🆓 Free demo class available
💰 Affordable fees with EMI options
Course Features
- Lectures 232
- Quiz 0
- Duration 10 weeks
- Skill level All levels
- Language English
- Students 0
- Assessments Yes
- 18 Sections
- 232 Lessons
- 10 Weeks
- Module 1: Big Data Fundamentals & Hadoop Ecosystem8
- 1.1What is Big Data — 5 Vs (Volume, Velocity, Variety, Veracity, Value)
- 1.2Big Data Use Cases — Real Industry Examples
- 1.3Big Data Architecture — Batch vs Real-Time Processing
- 1.4Hadoop Ecosystem Overview — HDFS, YARN, MapReduce, Hive, Spark
- 1.5Hadoop vs Traditional RDBMS — Key Differences
- 1.6Big Data Career Roadmap — Roles & Skills in 2025
- 1.7Modern Big Data Stack — Hadoop vs Cloud (AWS, Azure, GCP)
- 1.8Lab: Big Data Architecture Design — Real-World Use Case
- Module 2: Hadoop — HDFS & YARN13
- 2.1What is Hadoop & Hadoop Distributed File System (HDFS)
- 2.2HDFS Architecture — NameNode, DataNode, Secondary NameNode
- 2.3HDFS Data Blocks — Default Block Size & Replication
- 2.4HDFS Read & Write Pipeline — How Data Flows
- 2.5HDFS High Availability — Active vs Standby NameNode
- 2.6HDFS Federation — Multiple NameNodes
- 2.7HDFS Commands — Essential CLI Operations
- 2.8YARN Architecture — ResourceManager, NodeManager, AppMaster
- 2.9YARN Resource Scheduling — FIFO, Capacity, Fair Scheduler
- 2.10HDFS Storage Policies — Hot, Warm, Cold, Archive
- 2.11HDFS Balancer — Redistribute Data Across Nodes
- 2.12Lab: HDFS Commands — Upload, Download, Manage Files
- 2.13Lab: YARN — Submit Job, Monitor Resources
- Module 3: Hive — SQL on Hadoop24
- 3.1What is Apache Hive & When to Use It
- 3.2Hive Architecture — HiveServer2, Metastore, Driver
- 3.3Hive Metastore — Central Schema Repository
- 3.4HiveQL — SQL-Like Query Language
- 3.5Hive Data Types — Primitive & Complex (Array, Map, Struct)
- 3.6Managed Tables vs External Tables — Key Differences
- 3.7Hive File Formats — TextFile, ORC, Parquet, Avro
- 3.8ORC vs Parquet — When to Use What
- 3.9Hive Partitioning — Static & Dynamic Partitioning
- 3.10Hive Partitioning — Static & Dynamic Partitioning
- 3.11Hive Joins — Map Join, Reduce Join, Bucket Map Join
- 3.12Hive Aggregations — GROUP BY, HAVING, ROLLUP, CUBE
- 3.13Hive Window Functions — ROW_NUMBER, RANK, LEAD, LAG
- 3.14Hive Views & Materialized Views
- 3.15Hive Indexes — Compact & Bitmap
- 3.16Hive Transactions — ACID Tables (INSERT, UPDATE, DELETE)
- 3.17Hive SerDe — Serialize & Deserialize Custom Formats
- 3.18Hive Performance Optimization
- 3.19Hive with ORC & Snappy Compression
- 3.20HiveQL vs Spark SQL — When to Use What
- 3.21Lab: Create Partitioned & Bucketed Hive Tables on HDFS
- 3.22Lab: Hive Performance Optimization — Before & After
- 3.23Lab: Hive ACID Transactions — UPDATE & DELETE Operations
- 3.24Quiz: Hive Concepts — 20 Questions
- Module 4: Sqoop — Data Ingestion from RDBMS13
- 4.1What is Apache Sqoop & Use Cases
- 4.2Sqoop Architecture — Import & Export Flow
- 4.3Sqoop Import — RDBMS to HDFS & Hive
- 4.4Sqoop Export — HDFS to RDBMS
- 4.5Sqoop Job — Save & Rerun Import Commands
- 4.6Sqoop Metastore — Store Saved Jobs
- 4.7Sqoop Parallel Import — Controlling Mappers
- 4.8Sqoop Password Management — Secure Credentials
- 4.9Sqoop with MySQL, PostgreSQL & Oracle
- 4.10Sqoop Limitations & Modern Alternatives
- 4.11Lab: Sqoop Full & Incremental Import from MySQL to HDFS
- 4.12Lab: Sqoop Import to Hive with Partitioning
- 4.13Lab: Sqoop Export from HDFS to MySQL
- Module 5: Apache Spark — Core Fundamentals13
- 5.1What is Apache Spark & Why It Replaced MapReduce
- 5.2Spark Architecture — Driver, Executors, Cluster Manager
- 5.3Spark Cluster Managers — YARN, Standalone, Kubernetes, Mesos
- 5.4SparkContext vs SparkSession
- 5.5RDD — Resilient Distributed Dataset
- 5.6Lazy Evaluation & DAG — Directed Acyclic Graph
- 5.7Spark Execution Model — Jobs, Stages, Tasks
- 5.8Transformations vs Actions — Deep Dive
- 5.9Spark Deployment Modes — Client vs Cluster Mode
- 5.10spark-submit — Submit Jobs to Cluster
- 5.11Spark Configuration — spark-defaults.conf & SparkConf
- 5.12Lab: RDD Operations — WordCount & Log Analysis
- 5.13Lab: spark-submit Job on YARN Cluster
- Module 6: PySpark — DataFrame API20
- 6.1PySpark vs Scala Spark — When to Use What
- 6.2SparkSession — Entry Point for DataFrame API
- 6.3Creating DataFrames — From Files, RDD, Python Collections
- 6.4Reading Data — CSV, JSON, Parquet, ORC, Avro
- 6.5Writing Data — Overwrite, Append, Partitioned Writes
- 6.6DataFrame Transformations
- 6.7DataFrame Actions — show, collect, count, take
- 6.8Schema — StructType & StructField — Define Custom Schema
- 6.9InferSchema vs Explicit Schema — Best Practices
- 6.10Working with Nested JSON & Array Columns – explode(), flatten(), struct()
- 6.11Handling Null Values — dropna(), fillna(), coalesce()
- 6.12String Functions, Date Functions, Math Functions
- 6.13Conditional Expressions — when/otherwise
- 6.14UDFs — User Defined Functions
- 6.15Pandas UDFs — Vectorized UDFs
- 6.16Working with Multiple DataFrames — union, unionByName
- 6.17Reading & Writing to HDFS, S3, ADLS from PySpark
- 6.18Lab: PySpark ETL Pipeline — HDFS → Transform → Hive
- 6.19Lab: PySpark with Nested JSON — Flatten & Load to Parquet
- 6.20Quiz: PySpark DataFrame — 20 Questions
- Module 7: Spark SQL18
- 7.1What is Spark SQL & Unified Analytics Engine
- 7.2Spark SQL Architecture — Catalyst Optimizer & Tungsten Engine
- 7.3Creating Temporary Views & Global Temp Views
- 7.4Creating Temporary Views & Global Temp Views
- 7.5Spark SQL vs HiveQL — Key Differences
- 7.6Spark SQL with Hive Metastore Integration
- 7.7Joins in Spark SQL — Inner, Left, Right, Full, Cross
- 7.8Aggregations — GROUP BY, HAVING, ROLLUP, CUBE, GROUPING SETS
- 7.9Window Functions in Spark SQL
- 7.10Subqueries & CTEs — WITH Clause
- 7.11Spark SQL with ORC & Parquet Files
- 7.12Spark SQL on S3 — External Tables
- 7.13Spark Catalog API — Manage Tables & Databases
- 7.14Broadcast Variables & Accumulators
- 7.15Explain Plan — Read & Understand Query Execution
- 7.16Lab: Complex Spark SQL — Window Functions & CTEs
- 7.17Lab: Spark SQL on Hive Metastore — External Tables
- 7.18Lab: Spark SQL EXPLAIN Plan — Optimize Slow Queries
- Module 8: Spark Performance Optimization18
- 8.1Partitioning Strategy — How Many Partitions
- 8.2Repartition vs Coalesce — When to Use Each
- 8.3Broadcast Join — Avoid Shuffle for Small Tables
- 8.4Sort Merge Join vs Broadcast Join
- 8.5Data Skew — Causes & Salting Technique
- 8.6Adaptive Query Execution (AQE)
- 8.7Caching & Persistence — StorageLevel Options
- 8.8Kryo Serialization — Faster than Java Serialization
- 8.9Small File Problem — Causes & Solutions
- 8.10Predicate Pushdown & Column Pruning
- 8.11Reading Spark UI — Jobs, Stages, Tasks, Timeline
- 8.12Spill to Disk — Causes & How to Fix
- 8.13Memory Management — Executor Memory Tuning
- 8.14GC Tuning — Reduce Garbage Collection Pauses
- 8.15File Size Optimization — Compact Small Files
- 8.16Lab: Identify & Fix Data Skew with Salting
- 8.17Lab: Spark UI Analysis — Find Bottlenecks in Pipeline
- 8.18Lab: AQE — Before & After Performance Comparison
- Module 9: Scala for Spark8
- 9.1Why Learn Scala for Big Data Engineering
- 9.2Scala vs Python for Spark — Performance & Use Cases
- 9.3Scala Basics for Data Engineers
- 9.4Scala with Spark
- 9.5Building Spark JAR — sbt & Maven
- 9.6Submitting Scala Spark JAR to YARN Cluster
- 9.7Lab: PySpark ETL Rewritten in Scala — Side by Side
- 9.8Lab: Build & Submit Scala Spark JAR on YARN
- Module 10: Spark Streaming — Real-Time Processing20
- 10.1What is Spark Streaming & Real-Time Architecture
- 10.2DStream API — Legacy Streaming (Overview Only)
- 10.3Structured Streaming — Modern Streaming API
- 10.4Structured Streaming Architecture
- 10.5Reading Streaming Data from Kafka
- 10.6Reading Streaming Data from Files — Auto Loader Style
- 10.7Writing Streaming Output — Append, Update, Complete Mode
- 10.8Trigger Modes — ProcessingTime, Once, Continuous, AvailableNow
- 10.9Watermarking — Handle Late-Arriving Data
- 10.10Stateful Streaming — Running Aggregations
- 10.11Checkpointing & Fault Recovery
- 10.12Streaming Joins — Stream-Stream & Stream-Static
- 10.13Streaming Deduplication
- 10.14Structured Streaming with Kafka — End to End
- 10.15Kafka Architecture Recap — Topics, Partitions, Offsets
- 10.16Exactly-Once Semantics — Kafka + Structured Streaming
- 10.17Lab: Structured Streaming — Kafka → Spark → HDFS
- 10.18Lab: Watermarking — Late Data Handling in Streaming
- 10.19Lab: Real-Time Word Count with Windowed Aggregation
- 10.20Quiz: Spark Streaming — 20 Questions
- Module 11: Apache Kafka — Real-Time Messaging13
- 11.1What is Apache Kafka & Event-Driven Architecture
- 11.2Kafka Architecture — Brokers, Topics, Partitions
- 11.3Producers & Consumers
- 11.4Consumer Groups — Parallel Processing
- 11.5Kafka Offsets — Track Message Position
- 11.6Kafka Retention — Message Storage Policy
- 11.7Producing Messages from Python — kafka-python
- 11.8Consuming Messages from Python
- 11.9Kafka with Spark Structured Streaming
- 11.10Kafka Connect — Import/Export Data Without Code
- 11.11Kafka vs Pub/Sub vs Kinesis — Comparison
- 11.12Lab: Produce & Consume Messages with Python
- 11.13Lab: Produce & Consume Messages with Python
- Module 12: Apache Oozie — Hadoop Workflow Scheduler12
- 12.1What is Apache Oozie & Workflow Orchestration
- 12.2Oozie vs Airflow — When to Use What
- 12.3Oozie Workflow — Sequential & Parallel Actions
- 12.4Oozie Coordinator — Schedule Workflows on Time & Data
- 12.5Oozie Bundle — Group Multiple Coordinators
- 12.6Oozie Actions
- 12.7Oozie XML Workflow Definition
- 12.8Oozie Web Console — Monitor Workflow Runs
- 12.9Oozie with HDFS — Trigger on Data Arrival
- 12.10Oozie Limitations & Why Airflow Replaced It
- 12.11Lab: Oozie Workflow — Sqoop Import → Hive → Email Alert
- 12.12Lab: Oozie Coordinator — Daily Scheduled Pipeline
- Module 13: Apache Airflow — Modern Pipeline Orchestration19
- 13.1Lab: Oozie Coordinator — Daily Scheduled Pipeline
- 13.2Airflow Architecture — Webserver, Scheduler, Executor, Metadata DB
- 13.3DAG — Directed Acyclic Graph Concepts
- 13.4Writing DAGs in Python — Basics to Advanced
- 13.5Airflow Operators
- 13.6Task Dependencies — set_upstream, set_downstream, >> operator
- 13.7XComs — Pass Data Between Tasks
- 13.8Airflow Variables & Connections — Store Config & Credentials
- 13.9Airflow Hooks — Connect to Hive, HDFS, S3, MySQL
- 13.10Trigger Rules — all_success, one_failed, all_done
- 13.11Airflow Pools — Control Concurrency
- 13.12Airflow SLA — Set Time Limits on Tasks
- 13.13Airflow Backfill — Run Historical DAG Runs
- 13.14Airflow Monitoring — DAG Runs, Task Logs, Gantt Chart
- 13.15Airflow with LDAP — Authentication Setup
- 13.16Lab: Airflow DAG — Sqoop Import → Hive Transform → Email Alert
- 13.17Lab: Airflow SparkSubmitOperator — Submit PySpark to YARN
- 13.18Lab: Airflow Dynamic DAGs — Generate DAGs from Config
- 13.19Quiz: Airflow — 20 Questions
- Module 14: AWS for Big Data Engineers10
- 14.1AWS Overview for Big Data Engineers
- 14.2Amazon S3 — Object Storage for Big Data
- 14.3Amazon EMR — Managed Hadoop & Spark on AWS
- 14.4AWS Glue — Serverless ETL on AWS
- 14.5Amazon Athena — Serverless SQL on S3
- 14.6AWS IAM for Big Data — Roles & Policies
- 14.7Migrating On-Premise Hadoop to AWS EMR
- 14.8Lab: Submit PySpark Job on AWS EMR Cluster
- 14.9Lab: AWS Glue ETL — S3 → Transform → S3
- 14.10Lab: Athena — Query Parquet Data Lake on S3
- Module 15: Big Data Pipeline Architecture Patterns7
- 15.1Pattern 1 — Traditional Hadoop Batch Pipeline
- 15.2Pattern 2 — Modern Spark Batch Pipeline
- 15.3Pattern 3 — Real-Time Streaming Pipeline
- 15.4Pattern 4 — Lambda Architecture
- 15.5Pattern 5 — Cloud-Native Migration
- 15.6Pattern 6 — Airflow Orchestrated Pipeline
- 15.7Lab: Design Full End-to-End Architecture for Given Use Case
- Module 16: Big Data File Formats & Compression5
- Module 17: End-to-End Big Data Projects3
- Module 18: Big Data Certification Preparation8
- 18.1Cloudera CDP Data Engineer Certification — Overview
- 18.2Databricks Certified Associate Developer for Apache Spark
- 18.3AWS Certified Data Engineer Associate — Big Data Topics
- 18.4Top 60 Big Data Interview Questions & Answers
- 18.5Resume Building for Big Data Engineer Roles
- 18.6LinkedIn Profile Optimization for Big Data Jobs
- 18.7Salary Negotiation — Big Data Engineer Roles
- 18.8Expected Salary — ₹8 LPA to ₹35 LPA
