PySpark is the Python interface for Apache Spark, allowing developers to use Python for distributed data processing.

What is Apache Kafka used for?

Apache Kafka is a distributed event-streaming platform commonly used to handle high-volume real-time data streams.

What is the difference between Hadoop and Spark?

Hadoop provides a broader ecosystem for distributed storage and processing, while Spark is a fast distributed processing framework used for large-scale workloads.

Can beginners learn Big Data Engineering?

Yes. Learners with basic programming and SQL knowledge can progressively develop Big Data Engineering skills through structured training and practical projects.

Technology First

HomeAbout Us Payment Contact Us Corporate Training Blog Login

Technology First

All Courses

No categories available

Start typing to search courses...

Type in the search box to find courses

Chat with us

Call us now

+91-8882178896

+1 (343) 477-0926

Call

CoursesData Engineering

Big Data Engineering

Data Engineering

Big Data Engineering

Q: What is Big Data Engineering?

Big Data Engineering focuses on technologies and techniques used to store, process, and manage very large datasets across distributed computing environments.

Q: What does a Big Data Engineer do?

A Big Data Engineer works with technologies such as Hadoop, Spark, Kafka, Hive, and NoSQL to process and manage large-scale data workloads.

Q: What skills are required for Big Data Engineering?

Important skills include Python, SQL, Hadoop, Spark, PySpark, Kafka, distributed computing, NoSQL, and Big Data architecture concepts.

Q: Is Big Data Engineering difficult to learn?

The difficulty depends on your programming and data background. Learning the concepts progressively with hands-on practice can make the technology stack easier to understand.

Q: What is Hadoop used for?

Hadoop provides distributed storage and processing capabilities for large datasets through technologies such as HDFS, YARN, and MapReduce.

Q: What is Apache Spark?

Apache Spark is a distributed computing framework designed for fast processing and analysis of large datasets.

Build practical skills in Big Data Engineering with Hadoop, Spark, PySpark, Kafka, Python, and SQL. This Big Data Engineering Course covers distributed data processing, batch and real-time workloads, Big Data technologies, cloud concepts, performance optimization, and hands-on projects designed around large-scale data environments.

5/5(4,890 Reviews)

Level

Advanced

Duration

8 Weeks

About

Training Plan

Course Curriculum

New Batch

Projects

Certificate

Testimonials

FAQ

Interview FAQ

Placement Client

About Big Data Engineering

The Big Data Engineering Course at TechPratham is designed to build practical skills for working with large-scale data environments and distributed computing technologies. The program covers Big Data Engineering fundamentals, Hadoop, HDFS, YARN, MapReduce, Apache Spark, Spark SQL, and PySpark for distributed data processing.

You will strengthen Python and SQL skills for handling large datasets and learn how modern Big Data systems support both batch and real-time workloads. The course also covers Hive, NoSQL databases, Apache Kafka, and Spark Structured Streaming for large-scale data processing and event-driven applications.

The learning journey includes Big Data Engineering with Hadoop and Spark, cloud-based Big Data concepts, performance optimization, data quality, security, and governance. Practical projects and an end-to-end capstone help you apply these technologies to realistic Big Data scenarios.

This Big Data Engineering Training is suitable for learners and professionals looking to develop practical capabilities in modern Big Data technologies.

Training Plan

About trainer

Working professional who is carrying more than 10 years of industry experience.

Decks & Updated Content

Access to updated presentation decks shared during live training sessions.

e-Book

Assignments & MCQs

Module-wise assignments and MCQs provided for practice.

Video Recording

Daily Session would be recorded and shared to the candidate.

Projects

Live projects will be provided for hands-on practice.

Resume Building

Expert-guided resume building with industry-focused content support.

Interview Preparation

Comprehensive interview preparation with real-time scenario practice.

Big Data Engineering Curriculum

Module 1: Big Data Engineering Fundamentals

Understand the fundamentals of Big Data Engineering, large-scale datasets, distributed computing, and the technologies used to process high-volume and high-velocity data.

Introduction to Big Data

Big Data characteristics

Big Data Engineering fundamentals

Structured, semi-structured and unstructured data

Distributed computing concepts

Scalability and fault tolerance

Big Data architecture

Batch and real-time processing

Big Data technology ecosystem

Big Data Engineering use cases

Module 2: Python and SQL for Big Data

Build the programming and database skills required to work with large datasets and Big Data processing technologies.

Python fundamentals

Python data structures

Functions and modules

File handling

Exception handling

Working with large files

Data processing with Python

SQL fundamentals

Joins and aggregations

Subqueries and CTEs

Window functions

SQL performance concepts

Module 3: Distributed Computing and Hadoop

Learn how distributed computing enables large datasets to be stored and processed across multiple machines using the Hadoop ecosystem.

Distributed computing architecture

Hadoop overview

Hadoop ecosystem

Hadoop Distributed File System

Hadoop cluster architecture

Master and worker concepts

Resource distribution

Fault tolerance

Scalability

Hadoop use cases

Module 4: HDFS, YARN and MapReduce

Explore the core Hadoop technologies used for distributed storage, resource management, and large-scale data processing.

HDFS architecture

NameNode and DataNode

Blocks and replication

HDFS commands

File permissions

YARN architecture

ResourceManager

NodeManager

MapReduce architecture

Mapper and Reducer

Shuffle and sort

MapReduce execution flow

Module 5: Hive and Big Data Storage

Learn how Hive and related Hadoop ecosystem technologies support querying, organizing, and analyzing large datasets.

Apache Hive introduction

Hive architecture

Hive tables

Managed and external tables

HiveQL

Partitions

Bucketing

Hive joins

Hive aggregations

Query optimization concepts

Data storage formats

Hadoop ecosystem storage technologies

Module 6: Apache Spark and Spark SQL

Develop a strong understanding of Apache Spark for fast distributed data processing and large-scale analytics.

Apache Spark introduction

Spark architecture

Driver and executors

Spark applications

RDD fundamentals

Transformations and actions

Lazy evaluation

DataFrames

Spark SQL

Spark execution

Partitioning

Shuffle operations

Performance optimization

Module 7: PySpark for Distributed Data Processing

Use Python with Apache Spark to process, transform, and analyze large datasets efficiently.

PySpark introduction

SparkSession

Creating DataFrames

Reading large datasets

Data cleaning

Data transformation

Filtering and aggregation

Joins in PySpark

User-defined functions

Handling missing data

Partition management

PySpark performance practices

Module 8: NoSQL Databases for Big Data

Understand NoSQL databases and their role in handling high-volume, flexible, and distributed data workloads.

NoSQL fundamentals

Relational vs NoSQL databases

Key-value databases

Document databases

Column-family databases

MongoDB concepts

HBase fundamentals

Data modeling

Distributed storage

Scalability

Replication

NoSQL use cases

Module 9: Apache Kafka and Real-Time Streaming

Learn how Kafka and streaming technologies support real-time data ingestion, event processing, and high-volume message delivery.

Real-time data processing

Apache Kafka introduction

Kafka architecture

Producers and consumers

Topics and partitions

Brokers

Consumer groups

Message delivery

Kafka offsets

Fault tolerance

Streaming use cases

Spark Structured Streaming

Event-based processing

Module 10: Big Data Processing Workflows

Understand how different Big Data technologies work together to support large-scale processing workflows from data ingestion to analysis.

Big Data processing workflow

Batch processing architecture

Real-time processing architecture

Data ingestion concepts

Processing large datasets

Hadoop and Spark integration

Kafka and Spark integration

Data transformation

Data validation

Processing formats

Workflow monitoring concepts

Big Data system troubleshooting

Module 11: Cloud Big Data, Security and Performance

Explore cloud-based Big Data environments along with performance, security, governance, and reliability practices.

Cloud concepts for Big Data

Cloud storage fundamentals

AWS Big Data concepts

Azure Big Data concepts

Google Cloud Big Data concepts

Cloud-based distributed processing

Spark performance optimization

Partition optimization

Data quality

Data governance

Access control

Big Data security

Monitoring and reliability

Module 12: End-to-End Big Data Engineering Capstone

Apply the concepts learned throughout the course to design and implement an end-to-end Big Data Engineering solution.

Project requirement analysis

Big Data architecture design

Data ingestion

Distributed processing

Hadoop implementation

Spark processing

PySpark transformations

Kafka streaming

Data validation

Performance optimization

Security considerations

Project testing

Final implementation

Project presentation

Data Engineering Courses

No related courses found

Additional Program Highlights

Comprehensive study materials and resources

Professional resume building session

Master your interview skills

Real-world project demonstrations

Upcoming Batches

✓ Selected

Weekdays

📅 01 Oct 2026

⏰ Time

07:00 AM11:00 AM

02:00 PM09:00 PM

Weekends

📅 04 Oct 2026

⏰ Time

07:00 AM11:00 AM

02:00 PM09:00 PM

Weekdays

📅 06 Oct 2026

⏰ Time

07:00 AM11:00 AM

02:00 PM09:00 PM

Weekdays

📅 07 Oct 2026

⏰ Time

07:00 AM11:00 AM

02:00 PM09:00 PM

Can't find a batch you were looking for?

Who Should Take Big Data Engineering Course?

IT Professionals

Non-IT Career Switchers

Fresh Graduates

Working Professionals

Ops/Administrators/HR

Developers

BA/QA Engineers

Cloud / Infra

IT Professionals

Non-IT Career Switchers

Fresh Graduates

Career Opportunities After Learning Big Data Engineering

Big Data Engineer

Big Data Developer

Hadoop Developer

Spark Developer

PySpark Developer

Big Data Analyst

Data Processing Engineer

Big Data Platform Engineer

Big Data Engineer

Big Data Developer

Hadoop Developer

Key Projects

Big Data Engineering

Walmart – Retail Data Processing

Scenario: Process large-scale retail transaction data using Hadoop and Spark to identify trends, product patterns, and customer activity.

Live Work:

Process large datasets with Hadoop
Transform data using Spark and PySpark
Analyze retail patterns with Spark SQL

Outcome: Build a scalable retail data solution

WNS – Customer Event Streaming

Scenario: Build a simulated real-time streaming environment to process customer events using Kafka and Spark Structured Streaming.

Live Work:

Create Kafka topics for events
Process streams with Spark
Analyze incoming customer activity

Outcome: Develop real-time processing skills

Deloitte – Enterprise Data Analytics

Scenario: Design a Big Data solution for analyzing large enterprise datasets using Hadoop, Spark, Hive, and distributed processing concepts.

Live Work:

Store datasets using HDFS
Query datasets with Hive
Process analytics using Spark

Outcome: Build enterprise Big Data expertise

Accenture – Cloud Big Data Processing

Scenario: Create a simulated cloud-based Big Data environment for processing large datasets with distributed computing technologies.

Live Work:

Configure cloud storage concepts
Process data using Spark
Optimize distributed workloads

Outcome: Apply cloud Big Data concepts

Placement Process

Live Class with Industry Use Cases

Interactive learning
Real-world examples
Expert guidance
Doubt resolution

Daily Assignment & Weekly Assessments

Hands-on practice
Concept reinforcement
Weekly evaluation
Timely feedback

Project Testing & Deployment

Project development
Testing practice
Deployment exposure
Portfolio creation

Interview Preparation (Mock, PD, Alumni Session)

Mock interviews
Personal development
Alumni guidance
Confidence building

Resume Building

Resume crafting
Industry-focused format
Skills highlighting
Professional presentation

Placement

Job assistance
Interview support
Career guidance
Opportunity access

Latest Hiring

No hiring posts

Recently Placed Candidates

No placements available

Latest Hiring

No hiring posts

Our Success Mantra

Commitment

Ensuring quality training every day

Fulfillment

Meeting learning goals with confidence

Accomplishment

Students achieving industry-ready expertise

Our Learner Voice

Loading reviews...

Beyond Courses:

Additional Support We Provide

24/7 Support

LinkedIn Profile

Resume Writing

Alumni Sessions

Interview Preparation

Live Projects

What Makes TechPratham Training Different?

Feature

TechPratham Advantage

Industry Standard Institutes

Real-World Implementation

Live enterprise tenant access for 24/7 practice on real-world, enterprise-grade scenarios.

Restricted or simulation-based access that lacks real-world complexity.

Subject Matter Experts

Mentorship from certified professionals with 15+ years of global industry experience.

Generic instructors with no hands-on implementation background.

2026 Ready Curriculum

AI-driven curriculum with advanced platform integrations and real-world configurations.

Basic, outdated syllabus that misses key technical integrations.

Daily & Weekly assignments

Daily assignments and weekly assessments focused on concept reinforcement and continuous feedback.

Self-paced learning with no accountability or progress tracking.

End-to-End Lifecycle

Hands-on project testing and deployment with portfolio-ready real-world exposure.

Basic lab exercises without any deployment or testing practice.

Interview Readiness

Structured interview preparation including mock interviews, PD sessions, and alumni guidance.

Minimal support limited to a generic certificate of completion.

Placement Support

Guaranteed interview support through tie-ups with top MNCs and 1-on-1 mock interview sessions.

Basic career tips with no direct industry connections.

Training Support

24/7 technical doubt resolution and personalized mentoring support.

Limited mentor availability and no support after class hours.

Affordable Fees

Competitive fixed pricing with flexible payment options and transparent fee structure.

Inflated fees with hidden costs and low return on investment.

Real-World Implementation

Live enterprise tenant access for 24/7 practice on real-world, enterprise-grade scenarios.

Restricted or simulation-based access that lacks real-world complexity.

Subject Matter Experts

Mentorship from certified professionals with 15+ years of global industry experience.

Generic instructors with no hands-on implementation background.

2026 Ready Curriculum

AI-driven curriculum with advanced platform integrations and real-world configurations.

Basic, outdated syllabus that misses key technical integrations.

Daily & Weekly assignments

Daily assignments and weekly assessments focused on concept reinforcement and continuous feedback.

Self-paced learning with no accountability or progress tracking.

End-to-End Lifecycle

Hands-on project testing and deployment with portfolio-ready real-world exposure.

Basic lab exercises without any deployment or testing practice.

Interview Readiness

Structured interview preparation including mock interviews, PD sessions, and alumni guidance.

Minimal support limited to a generic certificate of completion.

Placement Support

Guaranteed interview support through tie-ups with top MNCs and 1-on-1 mock interview sessions.

Basic career tips with no direct industry connections.

Training Support

24/7 technical doubt resolution and personalized mentoring support.

Limited mentor availability and no support after class hours.

Affordable Fees

Competitive fixed pricing with flexible payment options and transparent fee structure.

Inflated fees with hidden costs and low return on investment.

What is Big Data Engineering?

What does a Big Data Engineer do?

What skills are required for Big Data Engineering?

Is Big Data Engineering difficult to learn?

What is Hadoop used for?

What is Apache Spark?

Industry-Recognized Certification

Big Data Engineering Certificate

News Highlights

TechPratham Introduces Hire-Train-Deploy Model to Transform HR & ERP Talent in the AI Era

TechPratham Empowering Future Professionals Through AI-Focused HR & ERP Training

Featured In

TechPratham Gains Recognition for Bridging the HR & ERP Skills Gap with Hire-Train-Deploy

TechPratham's Hire-Train-Deploy Approach Reshaping HR & ERP Careers in the AI-Driven Industry

About Us

TechPratham is an ISO 9001:2015 certified IT Training and Development, delivering industry-focused certification courses with expert guidance and placement support.

CIN:U62013UP2025PTC223378GST:09AALCT8794N1Z2

Courses

Company

Contact Us

Noida Office:B-24, Sector-1, Noida, Uttar Pradesh - 201301

Hyderabad Office:LVS Arcade, 71, Hitech, 6th floor, Madhapur Road, Jubilee Enclave, HITEC City, Hyderabad - 500081

+91-8882178896

+1 (343) 477-0926

info@techpratham.com

All Courses

Start typing to search courses...

Big Data Engineering

Enquire This Course

Placement Client

About Big Data Engineering

Training Plan

About trainer

Decks & Updated Content

e-Book

Assignments & MCQs

Video Recording

Projects

Resume Building

Interview Preparation

Big Data Engineering Curriculum

Module 1: Big Data Engineering Fundamentals

Module 2: Python and SQL for Big Data

Module 3: Distributed Computing and Hadoop

Module 4: HDFS, YARN and MapReduce

Module 5: Hive and Big Data Storage

Module 6: Apache Spark and Spark SQL

Module 7: PySpark for Distributed Data Processing

Module 8: NoSQL Databases for Big Data

Module 9: Apache Kafka and Real-Time Streaming

Module 10: Big Data Processing Workflows

Module 11: Cloud Big Data, Security and Performance

Module 12: End-to-End Big Data Engineering Capstone

Data Engineering Courses

Additional Program Highlights

Upcoming Batches

Who Should Take Big Data Engineering Course?

Career Opportunities After Learning Big Data Engineering

Key Projects

Big Data Engineering

Walmart – Retail Data Processing

Live Work:

WNS – Customer Event Streaming

Live Work:

Deloitte – Enterprise Data Analytics

Live Work:

Accenture – Cloud Big Data Processing

Live Work:

Placement Process

Live Class with Industry Use Cases

Daily Assignment & Weekly Assessments

Project Testing & Deployment

Interview Preparation (Mock, PD, Alumni Session)

Resume Building

Placement

Latest Hiring

Recently Placed Candidates

Latest Hiring

Our Success Mantra

Commitment

Fulfillment

Accomplishment

Our Learner Voice

Beyond Courses:

What Makes TechPratham Training Different?

What is Big Data Engineering?

What does a Big Data Engineer do?

What skills are required for Big Data Engineering?

Is Big Data Engineering difficult to learn?

What is Hadoop used for?

What is Apache Spark?

What is the difference between HDFS and a traditional file system?

What are NameNode and DataNode?

What is YARN?

What are transformations and actions in Spark?

What is lazy evaluation in Spark?

What is the difference between RDD and DataFrame?

Big Data Engineering Certification

Certification Details

Certification Coverage

TechPratham Course Completion Certificate

Industry-Recognized Certification

News Highlights

Featured In

About Us