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Data Engineering Course

Data Engineering Course Online – Build Real Projects & Job Ready Skills

Our Data Engineering Course is created for students, freshers, and working professionals who want to understand how data actually moves, is stored, and is prepared for use in real companies. This Data Engineering Online Course starts from the basics of data handling.

Duration: 12 – 16 Weeks | Mode: Live + Recorded Sessions

Data Engineering Course Demo Class

Attend our Data Engineering Training demo session to understand how live classes work before enrolling.

Our Recently Placed Students in Data Engineering Course

Neha Kapoor

Placed at Cognizant

Siddharth Jain

Placed at EY (Ernst & Young)

Anjali Rao

Placed at Capgemini

Kunal Verma

Placed at Wipro

Pooja Nair

Placed at Infosys

Virat Sharma

Placed at TCS

Sneha Iyer

Placed at Deloitte

Arjun Mehta

Placed at Accenture

About the Data Engineer Course With Placement

This Data Engineering Online Course is designed to explain how data is collected, cleaned, stored, and delivered for reporting and analytics. The Best Online Course For Data Engineering focuses on real business data problems and how data engineers solve them using simple tools and structured methods. This Data Engineering Best Course matches current industry needs.

Training Highlights
  • Live classes by experienced data engineering trainers
  • Hands-on projects using real data
  • Industry-focused Data Engineering Training
  • Real-time business data scenarios
  • Job-oriented learning approach
  • Resume building and interview preparation
  • Dedicated Data Engineer Training And Placement support

What You Get

  • Live instructor-led Data Engineer Course sessions
  • Practical assignments and data projects
  • Real-time data pipeline experience
  • Resume and interview guidance

Course Design & Approved By

Nasscom & Wipro

What Will You Learn in Data Engineering Course

Our Data Engineer Course With Placement is taught slowly & clearly so learners never feel confused. Each topic is explained in simple words with real-life examples. Data Engineering Course focuses on how data flows inside real companies helping learners become confident.

Core Modules Covered

  • Introduction to data engineering
  • Understanding data sources and formats
  • Data ingestion basics
  • Data storage concepts
  • Writing simple data pipelines
  • Data quality and validation
  • Working with structured data

Advanced Topics & Projects

  • Batch and real-time data basics
  • Data processing concepts
  • Working with cloud data platforms
  • SQL for data engineers
  • Live projects using real datasets
  • Case studies from real companies

Download Curriculum

Get a peek through the entire curriculum designed that ensures Placement Guidance

Course Design By

nasco wp

Course Offered By

Why Choose Our Data Engineer Course With Placement?

  • Data Engineering Training Videos
  • Simple notes for each topic
  • Real data case examples
  • Practice assignments
  • Interview and certification question

Benefits of Joining Our Data Engineering Course

  • Learn from experienced trainers
  • Work on real data projects
  • Lifetime LMS access
  • Recorded sessions for revision
  • Live doubt-clearing sessions
  • Certification support
Learners Reviews

“The interview support after completing the Best Online Data Engineering Courses was very helpful.”

— Nidhi, Associate Data Engineer

“Recorded sessions from the Data Engineer Training Online helped me revise concepts easily.”

— Manoj Kumar, Operations Analyst

“The Data Engineer Course With Placement gave me confidence to face technical interviews.”

— Priyanka Singh, Data Engineer

“Live projects during the Data Engineering Training helped me clearly understand real data pipelines.”

— Vikas Patel, IT Professional

“The Data Engineering Online Course was explained in very simple language, which made learning easy for me.”

— Ritika, Data Engineering Trainee

“This Data Engineering Course helped me understand how raw data is collected and processed in real companies.”

— Amit Khanna, Junior Data Engineer
Data Engineering - Country-wise Job Profiles & Salary

Top Job Profiles:

  • Data Engineer
  • Junior Data Engineer
  • ETL Developer
  • Data Operations Engineer
  • Big Data Engineer

Average Salary Range:

  • INR 4 LPA - INR 7 LPA (Entry Level)
  • INR 8 LPA - INR 15 LPA (Mid Level)
  • INR 18 LPA - INR 22+ LPA (Senior Level)

Top Job Profiles:

  • Data Engineer
  • Cloud Data Engineer
  • ETL Developer
  • Senior Data Engineer

Average Salary Range:

  • $75,000 - $95,000 (Entry Level)
  • $100,000 - $130,000 (Mid Level)
  • $140,000 - $150,000+ (Senior / Lead)

Top Job Profiles:

  • Data Engineer
  • ETL Developer
  • Data Platform Engineer

Average Salary Range:

  • £40,000 - £55,000 (Entry Level)
  • £60,000 - £80,000 (Mid Level)
  • £80,000 - £95,000+ (Senior Consultant)

Top Job Profiles:

  • Data Engineer
  • ETL Developer
  • Data Platform Specialist

Average Salary Range:

  • EUR 60,000 - EUR 85,000 (Entry Level)
  • EUR 85,000 - EUR 115,000 (Mid Level)
  • EUR 115,000 - EUR 140,000+ (Senior)

Enroll Today

Start your career journey with our job-focused Data Engineer Course With Placement. Join our Data Engineering Training and gain real-world skills required by top companies.

About the Trainer

Learn through Data Engineer Online Training from a professional trainer with over 10 years of industry experience. The trainer has worked on real data engineering projects and trained thousands of students.

  • 10+ years of data engineering experience
  • Expert in data pipelines and storage
  • Conducted 100+ online batches
  • Practical, project-based Data Engineering Best Course
  • Interview and placement guidance
Frequently Asked Questions

The syllabus at Croma Campus covers topics such as data modeling, ETL processes, data warehousing, and big data technologies like Hadoop and Spark.

Yes, with the right education and training, a fresher can become a data engineer, especially through specialized courses and hands-on experience.

This Data Engineering Course covers data ingestion, storage, processing, SQL, and real projects.

Learning at Croma Campus offers comprehensive training with experienced instructors, practical hands-on projects, and placement assistance, ensuring a strong foundation for a career in data engineering.

Fundamentals of data engineering include understanding data types, structures, database concepts, and basic principles of data processing and analysis.

After completing the course, you can pursue roles such as data engineer, big data engineer, database engineer, ETL developer, or cloud data engineer.

Yes, Croma Campus offers placement assistance to its students, helping them secure job opportunities as data engineers or related roles in the industry.

The stages typically involve learning foundational concepts, advanced techniques, hands-on projects, and practical application of skills in real-world scenarios.

ETL stands for Extract, Transform, Load, which refers to the process of extracting data from various sources, transforming it into a usable format, and loading it into a target destination, such as a data warehouse or database.

Yes, the Data Engineer Training And Placement program starts from basics and is beginner-friendly.

Yes, the Data Engineering Online Course includes notes, recordings, assignments, and projects.

CURRICULUM & PROJECTS

Data Engineer- Professional Training Program

    Python for Data Engineering:

    • Data types, loops, and conditionals
    • File handling (CSV, JSON, TXT)
    • Functions, lambda expressions, error handling
    • Working with libraries: Pandas, NumPy
    • Data manipulation and cleaning
    • Working with APIs and JSON data
    • Python for automation and scripting

    Scala (for Apache Spark):

    • Basics of Scala syntax and structure
    • Functional programming (map, reduce, filter)
    • Case classes and immutability
    • Using Scala with Spark (RDDs, DataFrames)

    Java Basics:

    • OOP principles: class, object, inheritance
    • Exception handling and file I/O
    • Working with Hadoop Java APIs (MapReduce basics)
Get full course syllabus in your inbox

    Apache Spark:

    • Spark architecture and cluster modes
    • RDDs vs DataFrames vs Datasets
    • Spark SQL: working with structured data
    • Transformations and actions
    • Spark Streaming: real-time data processing
    • Machine learning pipelines with MLlib (intro level)

    HDFS (Hadoop Distributed File System):

    • Blocks and replication
    • NameNode and DataNode
    • File storage commands and usage
    • Integration with Spark, Hive, Sqoop

    YARN:

    • YARN architecture
    • ResourceManager and NodeManager
    • Application lifecycle and scheduling

    Apache Mesos:

    • Mesos vs YARN
    • Resource allocation and scheduling
    • Deploying Spark/Flink on Mesos
Get full course syllabus in your inbox

    Relational Databases:

    • Database design concepts
    • SQL queries (SELECT, JOIN, GROUP BY)
    • Indexing, constraints, normalization
    • MySQL, PostgreSQL / PostGIS, Oracle

    NoSQL Databases:

    • Concepts: CAP Theorem, BASE vs ACID
    • Key-Value, Document, Columnar types

    Cassandra:

    • Data model, keyspaces, replication, CQL
    • Write-heavy optimizations

    MongoDB:

    • BSON, collections, indexing, aggregation framework
    • CRUD operations and performance tuning

    HBase:

    • Column-family model, integration with Hadoop
    • Use cases for wide-column storage
Get full course syllabus in your inbox

    Apache Hive:

    • Hive architecture: Metastore, Driver, Executor
    • HiveQL syntax: DDL, DML, Joins, UDFs
    • Partitions and Bucketing
    • Integration with HDFS and Spark

    Snowflake:

    • Cloud-based architecture: compute vs storage
    • Warehouses, databases, and schemas
    • Time Travel, cloning, and zero-copy restore
    • Working with semi-structured data (JSON, XML)
    • Performance tuning and caching
Get full course syllabus in your inbox

    Apache Kafka:

    • Producer-consumer architecture
    • Topics, partitions, brokers
    • Kafka Streams basics
    • Kafka + Spark/Flink integration

    Apache Flink:

    • Stream vs batch processing
    • Windows, time semantics (event vs processing time)
    • Stateful operators
    • Fault-tolerant streaming with checkpoints

    Apache Storm:

    • Storm architecture: Spouts and Bolts
    • Building topologies
    • Real-time analytics pipelines
Get full course syllabus in your inbox

    Apache Airflow:

    • DAGs, Tasks, Operators (PythonOperator, BashOperator, etc.)
    • Scheduling and triggering jobs
    • XComs, task dependencies, retries
    • Airflow UI for monitoring and management
    • Integrating with DBs, APIs, Spark, and cloud services
Get full course syllabus in your inbox

    AWS:

    • S3, IAM, EC2 basics
    • Redshift for data warehousing
    • AWS Glue for ETL
    • EMR for big data

    Azure:

    • Azure Data Factory (pipelines)
    • Azure Blob Storage
    • Azure Synapse for analytics

    GCP:

    • BigQuery for analytics
    • Google Cloud Storage
    • Dataflow for stream and batch pipelines
Get full course syllabus in your inbox

    OLTP vs OLAP systems

    Entity-Relationship (ER) modeling

    Star Schema and Snowflake Schema

    Dimension and Fact tables

    Slowly Changing Dimensions (SCD Types 1, 2, 3)

    Data Vault modeling basics

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    Designing batch and stream pipelines

    Data lake vs data warehouse architectures

    Scalable ingestion pipelines

    Fault tolerance, latency, high availability

    Case studies: Uber, Netflix, Spotify pipelines

    Choosing tools for use cases

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    Apache Sqoop:

    • Sqoop architecture and setup
    • Import/export from MySQL, Oracle to HDFS/Hive
    • Incremental loads (append, last-modified)
    • Performance tuning using mappers

    Capstone Projects

    • Batch ETL Pipeline using Airflow + Hive + MySQL
    • Real-Time Streaming using Kafka + Spark + MongoDB
    • Cloud Data Pipeline using AWS S3 + Glue + Redshift
    • System Design Case Study (architecture presentation)
Get full course syllabus in your inbox

+ More Lessons

Course Design By

naswipro

Nasscom & Wipro

Course Offered By

croma-orange

Croma Campus

Our Students' Projects
1768983146.webp
HCL Tech - Dashboard Support Project

Scenario: Supporting business dashboards

Live Work:
  • Prepared data for dashboards
  • Updated data regularly
  • Worked with reporting teams
  • Checked numbers before sharing

Outcome: Dashboards always showed correct data

1768983075.webp
IBM – Data Quality Validation Project

Scenario: Making sure data stays correct

Live Work:
  • Checked data daily
  • Found errors in data
  • Fixed data issues
  • Updated quality checks

Outcome: Data stayed correct and trustworthy.

1768982986.webp
Wipro – Data Reporting & Analytics Project

Scenario: Preparing data for reports

Live Work:
  • Understood what reports were needed
  • Prepared clean data for reports
  • Reduced extra data to improve speed
  • Verified report data

Outcome: Reports became faster and more accurate.

1768982817.webp
Deloitte – Data Storage Optimization Project

Scenario: Storing data safely

Live Work:
  • Organized data into folders and tables
  • Stored large data properly
  • Controlled who could access the data
  • Checked storage regularly

Outcome: Data was safe and easy to find.

1768911113.webp
Capgemini – Advanced Data Pipeline Project

Scenario: Moving data automatically.

Live Work:
  • Created a step-by-step data flow
  • Moved data from source to storage
  • Scheduled data runs
  • Checked if the process ran properly

Outcome: Data started moving automatically.

1768911052.webp
Accenture - Data Transformation Project

Scenario: Changing raw data into useful data.

Live Work:
  • Converted raw data into simple tables
  • Applied basic business rules
  • Joined related data together
  • Checked if results were correct

Outcome: Data became ready for reports and analysis.

1768911003.webp
TCS - Data Cleaning Project (Efficient & Accurate)

Scenario: Fixing messy data

Live Work:
  • Removed wrong and repeated data
  • Filled missing values where needed
  • Corrected date and number formats
  • Checked data again after cleaning

Outcome: Data became clean and easy to work with

1768910941.webp
Infosys – Data Collection & Analysis Project

Scenario: Collecting data needed for work.

Live Work:
  • Found where the data was coming from
  • Collected data from files and systems
  • Checked if the data was complete
  • Saved the data in the right place

Outcome: Data was ready to be used for the next steps.

Recent Data Engineering Course Job Requirements
Associate Data Engineer

Company: Cognizant

Location: Pune

Experience: 0–2 Years

Required Skills: Python, SQL, ETL/ELT Fundamentals, BI Tool Intro.

Entry Level Data Engineer

Company: Infosys

Location: Hyderabad

Experience: 0–1 Years

Required Skills: SQL, Python, Basic Cloud (AWS/GCP), Data Warehousing.

Junior Data Engineer

Company: Deloitte

Location: Mumbai

Experience: 0–2 Years

Required Skills: SQL, Python, ETL Tools (Airflow/AWS Glue).

Who Can Join Data Engineering Course?
  • Why : A good way to start a career in data engineering from scratch.
  • Best Modules: Basics of data, how data is collected, simple data work.
  • Job Benefit: Start working as a junior data engineer
  • Why : Useful for people who want to move into data-related jobs.
  • Best Modules: SQL, data handling, data pipelines.
  • Job Benefit: Shift into data engineer or data support roles.
  • Why : Suitable for people from non-technical fields who want IT jobs.
  • Best Modules: Basic data understanding, simple tools, hands-on practice.
  • Job Benefit: Entry-level roles in data teams.
  • Why : Helps understand how data flows in systems.
  • Best Modules: Data storage, data movement, cloud basics.
  • Job Benefit: Work confidently with data and technical teams.
  • Why : Helps understand how business data is handled
  • Best Modules: Data flow basics, reporting support
  • Job Benefit: Make better decisions and guide teams clearly
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