Is there a prerequisite for enrolling in the Data Engineer Training?

Having a basic understanding of programming (like Python or SQL) can help, but it’s not mandatory. The Data Engineering Course in Gurgaon is designed for beginners.

How long is the Data Engineer Training program?

The program usually lasts from 3 to 6 months, depending on the institute and course format.

Can I expect to get placed right after completing the course?

The placement depends on your skills and the job market, but the course offers placement assistance, and most students find opportunities within a few months.

What tools and technologies are covered in the training?

You will learn Python, SQL, Hadoop, Apache Spark, AWS, and other cloud technologies used in data engineering.

Will the certification be useful for my career?

Yes, the certification from the training program, combined with official certifications from platforms like AWS or Azure, will help make your resume stand out to employers.

Is there a prerequisite for enrolling in the Data Engineer Training?

Having a basic understanding of programming (like Python or SQL) can help, but it’s not mandatory. The Data Engineering Course in Gurgaon is designed for beginners.

How long is the Data Engineer Training program?

The program usually lasts from 3 to 6 months, depending on the institute and course format.

Can I expect to get placed right after completing the course?

The placement depends on your skills and the job market, but the course offers placement assistance, and most students find opportunities within a few months.

What tools and technologies are covered in the training?

You will learn Python, SQL, Hadoop, Apache Spark, AWS, and other cloud technologies used in data engineering.

Will the certification be useful for my career?

Yes, the certification from the training program, combined with official certifications from platforms like AWS or Azure, will help make your resume stand out to employers.

Data Engineer Course in Gurgaon [Updated 2025] ✅

Name: Data Engineer- Professional Training Program
Item: Data Engineer- Professional Training Program
Rating: 4.90

Home /Data Engineering/ Data Engineer Course in Gurgaon

Master the Fundamental Concepts of Data Engineering. Enroll today and become a skilled Data Engineering expert.

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The Data Engineer Training in Gurgaon is a practical and detailed course for those who want to learn how to manage and process large amounts of data. The training covers key areas like building data pipelines, using tools like SQL, Python, Hadoop, and working with cloud platforms like AWS. Students will gain hands-on experience in handling real-time data challenges and will be prepared for a career as a data engineer.

The Data Engineer Classes in Gurgaon aims to give students the skills needed to work as data engineers. By the end of the training, you'll understand how to build data systems, manage data, and use the latest technologies to handle large datasets.

Learn how to build and manage data pipelines using tools like Apache Spark and Hadoop.

Get hands-on experience with cloud platforms (AWS, Azure) to handle big data.

Understand the ETL (Extract, Transform, Load) process to clean and organize data.

Learn about data storage methods like data lakes and data warehouses.

Become skilled in using programming languages like Python and SQL to manipulate data.

After completing the Data Engineering Training in Gurgaon, freshers can expect a decent starting salary due to the high demand for data engineers. On average, a fresher can expect to earn between 5 LPA and 8 LPA, depending on the skills learned during the course.

Entry-level salary: 5 LPA to 8 LPA.

Salaries increase with experience and knowledge of big data tools and cloud platforms.

Senior-level data engineers can earn 15 LPA or more as they gain more expertise.

Once you complete the Data Engineer Training in Gurgaon, you'll find multiple career opportunities with clear growth paths. You can start as a junior data engineer and grow into senior or management roles in data engineering.

Junior Data Engineer Senior Data Engineer Data Architect.

With experience, you can take on leadership roles, such as Data Engineering Manager.

You can also explore roles like Data Scientist or Lead Data Engineer.

The Data Engineer Training in Gurgaon is popular because there is a growing need for data engineers. As Gurgaon is a major hub for tech and business companies, it provides students with many job opportunities in the data field.

Gurgaon is home to many tech companies looking for skilled data engineers.

Data engineering is a rapidly growing field with high job demand.

The course is designed to teach the latest data tools and technologies.

The training includes practical experience that prepares students for real-world challenges.

After completing the Data Engineering Classes in Gurgaon, You will be ready to take on various data engineering roles. Your primary job will be to create and manage systems that store and process large amounts of data.

Develop and maintain data pipelines to collect and process data.

Manage data storage systems like data lakes and data warehouses.

Ensure the data is of high quality, secure, and available for analysis.

Work closely with data scientists to ensure they have clean, well-organized data.

Optimize how data is stored and processed to improve speed and efficiency.

Many industries need data engineers, and after completing the Data Engineer Training in Gurgaon, You will have many job opportunities. Industries that heavily rely on data need skilled professionals to manage and process this data.

The Data Engineer Training in Gurgaon includes strong placement assistance to help students find jobs after completing the course. The training institute works with top companies to help you get placed in the best organizations.

Dedicated placement support to assist with job applications and interview preparation.

Opportunities for internships or live projects to enhance your skills.

Mock interviews and resume-building sessions to improve your chances of getting hired.

You May Also Read:

Data Engineering Course in Noida

Data Engineering Course in Gurgaon

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Why Should You Choose Data Engineering Course?

Data Engineering helps out in creating better real-life solutions.

The concept of Data Engineering is all-pervasive.

Helps out in getting understanding of Maths, Statistics, Programming & others.

By 2026 there will 11.5 million jobs gets available in Data Engineering Course.

Data Engineering helps out in getting top position in organizations.

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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)

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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

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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

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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

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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

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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

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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

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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)

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Course Design By

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Course Offered By

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Course Duration

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