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

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Course Duration

120 Hrs.

Live Project

5 Project

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Data Engineering

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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.

Data Engineer Course in Gurgaon

About-Us-Course

  • 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.

Why Should You Choose Data Engineering Course?

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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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120 Hrs.
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Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures.Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.
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FAQ's

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.

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

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.

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

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.

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  • - Get Tips from Trainer to Clear Interviews
  • - Attend Mock-Up Interviews with Experts
  • - Get Interviews & Get Hired

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