Data Engineering with Big Data Course with Certification [English]
This Data Engineering with Big Data course provides an extensive curriculum designed to build a solid foundation in handling large and complex datasets. Covering everything from data storage and management to processing and analysis, the course enables participants to master the essential aspects of Big Data.
What will you take home from this Data Engineering with Big Data Course?
- 40+ hrs live expert-led course
- 70+ hrs self-paced expert-led course
- 175+ hrs of comprehensive study material
- 90+ hrs of real-world practicals
- 30+ Interactive quizzes & assessments
- 840+ Interview questions for top MNCs
- 40+ Real-time projects with implementation
- 200+ Practical Code Examples
- 98% Positive reviews from learners
- 45+ Comprehensive assignments
- 30+ Real-time industry case-studies
- 590+ Big Data programming tutorials
- 1:1 Career counselling with expert
- Practical knowledge which industry needs
- Industry-renowned certification
Your Data Engineering with Big Data Journey Starts Here — Try before you Buy
Master Data Engineering from Scratch
Join our hands-on Data Engineering course crafted by industry veterans and build real-world skills. It’s not just a course, it’s a job-ready bootcamp.|
Start 📅 9-Aug-2025 |
Schedule 🕗 8.00 PM IST | 09.30 AM EST (Sat-Sun) |
Access Duration 🕗 Lifetime Access |
Price |
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| 📅 30-Aug-2025 | 🕗 5.30 PM IST | 08.00 AM EDT (Sat-Sun) | 🕗 Lifetime Access |
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Start Anytime, Learn Anywhere – Enroll in Self-paced Data Engineering Course
Self-Paced Mastery
Ideal for: Beginners looking to learn and grow with expert guidance.
₹13990 | $182 | €161 ₹6990 | $91 | €80
| Self-paced Expert-led Course |
| Industry-relevant Curriculum by Experts |
| Beginner to Advanced Topics Covered |
| Notes, Study material & Cheatsheets |
| Real-world Practicals |
| Assessments to Test Your Skills |
| Interview Questions of Top MNCs |
| Real-time Live Projects |
| 1:1 Career Counselling with Expert |
| Real-time Industry Case-studies |
| 3 years Access Duration |
| Industry-renowned Certification |
Career Launchpad
Ideal for: Dedicated & ambitious learners looking for jobs in top MNCs.
₹17990 | $234 | €207 ₹9990 | $130 | €115
| Everything in “Self-Paced Mastery” plus: |
| Job Assistance |
| Resume & Interview Prep |
| Mock Interview |
| Internship |
| Job/Placement Prep |
| LOR |
| Additional Real-time Projects |
| Lifetime Course Access |
| LinkedIn Profile Optimization |
| Learn with ChatGPT & AI tools |
| Quizzes for Each Module to Track Progress |
Pro Mentorship
Ideal for: Industry professionals looking for a job or switching careers.
₹23990 | $312 | €276 ₹11990 | $156 | €138
| Everything in “Career Launchpad” plus: |
| Live interaction with Instructor for 3 months |
| Live Mentoring over Weekends |
| Personal mentorship and 1:1 guidance from experts |
| 100% Placement Assistance |
| Lifetime Support |
| Lifetime Upgrades to latest version |
| Additional Real-time Projects |
| 24x7xLifetime Access |
| Dedicated Hiring Manager |
| Interview Questions of MAANG Companies |
| After Job Support |
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Data Engineering with Big Data Course Objectives
This Data Engineering with Big Data course aims to improve your ability to handle the vast amount of data generated in various forms and deal with it all. Explore distributed file systems, NoSQL databases, and the data warehousing technologies you need to manage the speed, volume, and variety of Big Data.
It is more about processing and analysis of the data. You will learn the methodologies and tools required to handle and examine substantial datasets. Through hands-on experience with frameworks like Hadoop and Spark, you will implement distributed data processing workflows. Understanding data visualization and storytelling is also a vital part of the course. Effective communication of complex findings is crucial for making informed, data-driven decisions.
Why should you learn Data Engineering with Big Data?
Here is why you must learn Big Data.
Learning Data Engineering with Big Data is instrumental as it will improve you professionally. There are many opportunities in almost every industry. We can think of eight reasons you should take up your pen.
- Rising Demand: This is a top-rated course because modern businesses adapt their plans based on data and want an edge over competitors.
- Diverse Career Opportunities: Proficiency in Big Data opens up various career paths, including roles like data analyst, data engineer, data scientist, and machine learning specialist.
- Industry Impact: Big Data is transforming sectors such as healthcare, finance, marketing, and more, making it a valuable skill set in today’s job market.
- Enhanced Problem-Solving Skills: Learning Big Data equips you with the tools to tackle complex problems, make data-driven decisions, and uncover meaningful insights.
- Customer Insights and Personalization: Understanding big data allows companies to analyze customer behavior and preferences, improving customer experiences and creating stronger relationships.
- Scientific Research Advancement: Big Data is foundational for scientific fields, enabling data-driven discoveries in genomics, climate, and social sciences.
- Segregation of a Competitive Advantage: Organisations that use Big Data more efficiently can achieve a strategic advantage through business efficiency, product, and visibility.
- Catching Up On Data Literacy Skills: With growing automation and AI technologies, data literacy will become more crucial for every organisation’s workforce and society. Big Data is at the core of essential skills today, and in the future, so you should invest your time in learning big data.
What is Big Data?
Big Data refers to the immense, complex volumes of structured and unstructured data that traditional data processing tools cannot efficiently handle. Defined by the three V’s — Volume, Velocity, and Variety — Big Data is characterized by:
- Volume: The massive amounts of data generated daily from social media, sensors, financial transactions, etc.
- Speed: The high speed of data creation, acquisition, and processing.
- Variety: the different types of data, such as text, images, video and others
Big Data has revolutionized several business domains, allowing organizations to gain insights into customer fundamentals, market behavioral patterns, and operational efficiencies. With the help of Big Data, companies can make data-driven decision-making processes, improve customer experience, and find opportunities for new revenue streams. For instance, in e-commerce platforms, Big data analytics is leveraged to provide product recommendations based on customers’ recent interactions with the website.
Big data has become essential for every modern business looking for a competitive edge in the digital age. Utilizing big data to drive analytics and machine learning helps organizations make decisions, increase customer satisfaction, and innovate the industry. However, we need a blend of technical expertise and strategic and ethical understanding to leverage big data at its best.
What to do before you begin?
While prerequisites may vary, having a basic understanding of programming and database concepts is beneficial. Familiarity with programming languages like Python or Java and knowledge of SQL can enhance your learning experience.
Who should go for this Data Engineering with Big Data course?
This online Big Data course is designed for individuals interested in data analytics and processing.
It is ideal for:
- Aspiring Data Scientists
- Data Analysts
- IT Professionals
- Business Professionals
- Entrepreneurs and Business Owners
By enrolling in our Data Engineering with Big Data course, you can expect the following benefits:
The Data Engineering with Big Data course offers numerous advantages.
- Skills Acquisition: This course helps the participant acquire relevant skills required to work with Big Data and provides an opportunity to learn to process and analyze a large volume of data. Participants learn about in-demand tools, from programming languages such as Python and R to data visualization methods.
- More Career Opportunities: Big Data skills have never been more in demand so this helps you stand apart from the rest and increases employability in roles like data scientist, data engineer, business intelligence analyst, big-data architect etc.
- Strong Need for Big Data Skills: The increase of data-led approaches in organisations makes Big Data skills very sought after, which subsequently means higher job opportunities and earning potential.
- Real-World Experience: Through practical assignments and projects, you gain hands-on experience working with Big Data technologies and actual datasets, which helps close the gap between theory and application.
- Mastery of Data Analysis: Participants gain skills to analyse data and identify significant trends and insights from large, complex datasets.
Jobs after Learning this Data Engineering with Big Data Course
- Data Scientist
Data Scientists are pivotal in leading the data-driven transformation across industries. They collect, analyze, and interpret large, complex datasets to uncover meaningful insights that drive strategic decisions. By utilizing advanced analytics and machine learning techniques, they develop predictive models that help organizations make informed decisions.
- Data Engineer
Data Engineers are essential in designing and managing the infrastructure for processing and storing Big Data. They build data pipelines, implement data warehousing solutions, and ensure data quality and security. This role involves working with distributed systems and large-scale data processing frameworks.
- Business Intelligence (BI) Analyst
Business Intelligence Analysts transform raw data into actionable insights that support strategic business decisions. Using big data tools, they create interactive dashboards, reports, and data visualizations. Their work helps organizations understand trends, identify opportunities, and address challenges effectively.
- Big Data Architect
Big Data Architects are responsible for designing and integrating an organisation’s overall architecture of Big Data systems. They ensure data solutions are scalable, reliable, and aligned with business objectives. This role involves working closely with Data Scientists, Engineers, and business stakeholders to implement effective data strategies.
Our students are working in leading organizations
Data Engineering with Big Data Course Curriculum
- Why Learn Python Programming?
- What is Python?
- Python Applications
- Platform Dependency
- Features of Python
- Limitations of Python
- History of Python
- Python Installation
- Installing PyCharm
- What is IDLE?
- Python Code Execution Flow
- Hello World Program
- Statements, Indentation, and Comments
- Print and Input Functions
- Identifiers, Variables, and Data Types
- Input/Output Functions
- Formatted Strings and Replacement Operators
- Using the format() Method
- Types of Operators
- Number System Conversion
- If-Else and Elif Statements
- Loops and Patterns
- While and For Loops
- Nested Loops
- String Basics
- String Built-in Functions
- Lists, Tuples, Sets, and Dictionaries
- Frozen Sets and Byte Arrays
- Creating Functions
- Arguments and Parameters
- Global Variables and eval() Function
- Recursive Programming
- Solve problems like factorial, Fibonacci series, and reversing numbers using recursion
- Anonymous Functions
- Using Lambda with Filter and Map
- Array Basics
- Using NumPy for Arrays
- Binary Search
- Bubble Sort
- Understanding OOP Concepts
- Special Methods and Overloading
- Static and Inner Classes
- Inheritance and Abstract Classes
- Try-Except-Finally Blocks
- User-Defined Exceptions
- Working with Files
- Using Pickle and CSV
- Advanced File Operations
- What is Big Data?
- Necessity of Big Data and Hadoop in the Industry
- Paradigm Shift to Big Data Tools
- Dimensions of Big Data
- Data Explosion in the Industry
- Big Data Implementations
- Technologies for Handling Big Data
- Limitations of Traditional Systems
- Future of Big Data
- Why Hadoop is Central to Big Data
- Introduction to Hadoop Framework
- Hadoop Architecture and Design Principles
- Components of the Hadoop Ecosystem
- Hadoop Flavors
- Single-Node Hadoop Cluster Setup
- Hadoop Environment Setup
- Pseudo-Distributed Mode
- Multi-Node Cluster Setup
- Cloud Setup
- Troubleshooting
- Introduction to HDFS
- HDFS Daemons and Architecture
- Data Flow and Storage Mechanism
- HDFS Features
- Adding and Commissioning Nodes
- HDFS APIs and Web UI
- What is MapReduce?
- MapReduce Execution Flow
- Components of MapReduce
- Word Count Example
- Optimizing MapReduce Jobs
- Fault-Tolerance and Data Locality
- Working with Combiners
- Apache Hive
- Introduction and architecture of Hadoop Hive
- Hive shell and running HQL queries
- Hive DDL and DML operations
- Hive execution flow
- Schema design and Hive operations
- Difference between Schema-on-Read and Schema-on-Write in Hive
- Need for RDBMS
- Limitations of the default meta-store
- Using SerDe to handle different types of data
- Optimization of performance using partitioning
- Different Hive applications
- Use cases of Hive
- Introduction to Apache Sqoop
- Need for Apache Sqoop
- Working of Sqoop
- Importing data from RDBMS to HDFS
- Exporting data to RDBMS from HDFS
- Conversion of data import/export queries into MapReduce jobs
- Introduction to Apache HBase
- Internals of the HBase architecture
- The HBase Master and Slave Model
- Column-oriented, 3-dimensional, schema-less datastores
- Data modeling in Hadoop HBase
- Storing multiple versions of data
- Data high-availability and reliability
- HBase vs HDFS
- HBase vs RDBMS
- Work with HBase using the shell
- Introduction to Apache Flume
- Flume Architecture and Components
- Reliable and Scalable Data Collection
- Multi-Tier Flume Flows
- Collecting Data with Flume
- Introduction to YARN
- YARN and its ecosystem
- Daemon architecture in YARN
- Master of YARN
- Slave of YARN
- Requesting resources from the application master
- Dynamic slots
- Application execution flow
- MapReduce version 2 application over Yarn
- Hadoop Federation and Namenode HA
A live Big Data Hadoop project based on industry use-cases using Hadoop components like Pig, HBase, MapReduce, and Hive.
- Development Environment Setup: Working with the command line
- Production Environment Setup & Best Practices
- Stages of the Data Pipeline (Generation, Ingestion, Storage, Transformation, and Serving)
- ETL Concepts: Extract, Transform, Load
- Real-time Data ingestion and transformation
- Data Engineering Pipelines with ELT Best Practices
- Batch Processing vs Streaming Ingestion
- Real-time Streaming vs Micro Batch vs Batch Flow
- Collect Data from Various Sources and Load it into Target Systems
- Transformation Techniques: Cleaning and Preprocessing Data after Extraction
- Advanced Data Cleaning: Strategies for Real-World Data Issues
- Error Handling & Logging in ETL
- Real-time Data Ingestion
- Designing a Streaming Pipeline
- Data Queuing for Improved Reliability
- Fan-out – Create Multiple Flows for Real-Time and Batch Processing
- Design and Implement Data Pipeline using Hadoop, Spark, Kafka, Flume, BI
- Enterprise Data Flow Best Practices
- Design and Architecture of a Complete Data Lake
- Best Practices of Data Mart
- Storage and Archival Policy
- Streaming and Batch Flow with Storage
- Advanced Data Engineering PracticesOLTP vs OLAP
- Configuring High-Availability and Fault-Tolerant Designs
- Scalability Strategies
- Future-Proof the Deployment for Reliability
- Live Architecture of E-commerce (Flipkart)
- Live Architecture of Telecom (Jio)
- Live Real-Time Architecture of Media, BFSI, and Other Domains
- Layered Architecture Practices for Scalability
- Data Collection and Acquisition
- Data Queuing for improved reliability
- Fan-out – create multiple flows for Real-time and batch processing
- Introduction to Spark Components and Architecture
- Spark Deployment Modes
- Spark Web UI
- Introduction to PySpark Shell
- Submitting PySpark Jobs
- Writing Your First PySpark Job Using Jupyter Notebook
- Introduction to Spark RDDs (Resilient Distributed Datasets)
- Challenges in Traditional Computing
- Creating RDDs
- RDD Persistence and Caching
- General Operations on RDDs
- Key-Value Pairs in RDDs
- RDD Lineage
- Partitioning in RDDs
- Passing Functions to Spark
- Introduction to Spark SQL
- Spark SQL Architecture
- User-Defined Functions (UDFs)
- DataFrames
- Loading Data from Different Sources
- Performance Tuning in Spark SQL
- Spark-Hive Integration
- Introduction to Spark Streaming
- Spark Streaming Workflow
- StreamingContext Initialization
- Working with DStreams
- Windowed Operators
- Stateful Operators
- Introduction to Machine Learning
- Introduction to MLlib
- Features and Tools of MLlib
- Types of Machine Learning Algorithms
- Supervised Learning: Classification, regression, and more.
- Unsupervised Learning: Clustering and dimensionality reduction techniques.
- MLlib Workflow Utilities
Big Data Projects
-
Retalix – Retail Data Analysis
Analyze retail data to find customer preferences, shopping patterns, and seasonal changes to help improve store performance.Web Log Analytics
Study server log files to understand website visitors, fix errors, and make the website run better.Log Data Analysis
Examine log data to find issues, improve system performance, and keep the system secure. -
Sentiment Analysis with Real-Time Data
Use live social media data to identify whether people feel positive, negative, or neutral about a topic.Weather Data Analysis
Look at past weather records to see trends in temperature and rainfall over time.Building a Data Lake
Design and build a scalable data lake to store structured and unstructured data, enabling advanced analytics for business intelligence. -
Satellite Image Processing
Analyze satellite images to extract valuable insights for agriculture monitoring, urban planning, or disaster management.Twitter Data Analysis
Extract and analyze Twitter data to track trending topics, user behavior, and public sentiment in real-time.Telecom Customer Analysis
Process telecom customer data to identify churn risks, usage patterns, and opportunities for personalized services. -
IVR Data Analysis
Analyze IVR call data to enhance customer experience by identifying frequent issues and optimizing call flows.End-to-End Big Data Ecosystem
Design and build a complete data pipeline to ingest, process, store, and visualize industry-specific datasets for actionable insights.Enterprise Data Warehouse
Develop a robust data warehouse solution for retail or healthcare organizations to streamline data storage and analytics.
Numbers That Speak Our Success
Big Data Case Studies
-
Big Data at Google
Google processes billions of queries daily using Bigtable and MapReduce, ensuring lightning-fast search results and relevant ad placements to maintain its dominance in the search industry.Big Data at Spotify
Spotify employs Hadoop and Spark MLlib to analyze user preferences and listening habits, delivering personalized playlists that keep users engaged and reduce churn.Big Data at Tesla
Tesla uses Big Data and AI-powered frameworks to process vast amounts of sensor and video data from its fleet, improving the performance and safety of its self-driving algorithms. -
Big Data at NASA
NASA employs Big Data frameworks to analyze data from space missions, enabling real-time insights that improve mission success rates and resource allocation.Big Data at PayPal
PayPal employs Big Data analytics with Hadoop and Spark to monitor and detect fraudulent transactions in real time, ensuring safe and secure payment processing.Big Data at Twitter
Twitter leverages Hadoop and Storm to process massive volumes of tweets in real time, identifying trending topics and enabling timely content moderation and targeted advertising. -
Big Data at Alibaba
Alibaba uses Big Data technologies like Hadoop and machine learning to analyze transaction patterns and detect fraudulent activities, ensuring a secure online shopping experience for its customers.Big Data at Cisco
Cisco processes terabytes of network data daily using Big Data technologies to predict and resolve network bottlenecks, improving connectivity and customer satisfaction.Big Data at Ford
Ford employs Big Data frameworks to analyze vehicle sensor data, enabling predictive maintenance that reduces breakdowns and enhances customer trust. -
Big Data at eBay
eBay uses Hadoop and machine learning to optimize search algorithms, ensuring customers find relevant products quickly, improving user satisfaction and sales.Big Data at Samsung
Samsung leverages Big Data tools to analyze customer feedback and device usage patterns, enabling targeted product development and marketing strategies.
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Big Data and Data Engineering Job Roles
Our learners are working in leading organizations
Top Reasons to Choose Data Engineering with Big Data as a Career
Features of Data Engineering with Big Data Course
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Data Engineering with Big Data Online Training FAQs
A Data Engineering with Big Data course is a structured program that teaches the principles, techniques, and technologies for handling and analyzing large and complex datasets. It is ideal for anyone interested in learning to work with Big Data, including aspiring data scientists, data analysts, IT professionals, business professionals, and entrepreneurs.
While prerequisites may vary, a basic understanding of programming and database concepts is often beneficial. Familiarity with programming languages like Python or Java and SQL skills is frequently recommended.
Topics commonly include data management, distributed computing, data processing frameworks (e.g., Hadoop, Spark), data analysis, machine learning, and data visualization. Many courses also provide hands-on experience with Big Data projects and technologies.
Most Data Engineering with Big Data courses balance theoretical concepts with practical applications. You’ll learn core principles while gaining hands-on experience with tools and real datasets.
You can expect to work on projects involving real-world datasets, such as setting up data pipelines, managing distributed data processing, building predictive models, and creating data visualizations.
Yes, you will receive a certificate on completion of the course, and yes, this certificate is recognized in the industry.
Completing a Big Data course can open doors to roles such as data analyst, data engineer, data scientist, and business intelligence analyst. Industries like technology, finance, healthcare, and marketing are actively hiring Big Data professionals.
Course length varies based on the provider; some courses span a few weeks, while others may last several months. Courses may be instructor-led with scheduled classes or self-paced, allowing students to study at their convenience.
