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Master the Fundamental Concepts of Data Engineering. Enroll today and become a skilled Data Engineering expert.
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4.5/5Course Duration
120 Hrs.Live Project
5 ProjectCertification Pass
GuaranteedTraining Format
Live Online /Self-Paced/ClassroomWatch Live Classes
Speciality
200+
Professionals Trained
3+
Batches every month
20+
Countries & Counting
100+
Corporate Served- 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
- 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.
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CURRICULUM & PROJECTS
Data Engineer- Professional Training Program
- 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
- Basics of Scala syntax and structure
- Functional programming (map, reduce, filter)
- Case classes and immutability
- Using Scala with Spark (RDDs, DataFrames)
- OOP principles: class, object, inheritance
- Exception handling and file I/O
- Working with Hadoop Java APIs (MapReduce basics)
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- 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)
- Blocks and replication
- NameNode and DataNode
- File storage commands and usage
- Integration with Spark, Hive, Sqoop
- YARN architecture
- ResourceManager and NodeManager
- Application lifecycle and scheduling
- Mesos vs YARN
- Resource allocation and scheduling
- Deploying Spark/Flink on Mesos
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- Database design concepts
- SQL queries (SELECT, JOIN, GROUP BY)
- Indexing, constraints, normalization
- MySQL, PostgreSQL / PostGIS, Oracle
- Concepts: CAP Theorem, BASE vs ACID
- Key-Value, Document, Columnar types
- Data model, keyspaces, replication, CQL
- Write-heavy optimizations
- BSON, collections, indexing, aggregation framework
- CRUD operations and performance tuning
- Column-family model, integration with Hadoop
- Use cases for wide-column storage
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- Hive architecture: Metastore, Driver, Executor
- HiveQL syntax: DDL, DML, Joins, UDFs
- Partitions and Bucketing
- Integration with HDFS and Spark
- 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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- Producer-consumer architecture
- Topics, partitions, brokers
- Kafka Streams basics
- Kafka + Spark/Flink integration
- Stream vs batch processing
- Windows, time semantics (event vs processing time)
- Stateful operators
- Fault-tolerant streaming with checkpoints
- Storm architecture: Spouts and Bolts
- Building topologies
- Real-time analytics pipelines
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- 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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- S3, IAM, EC2 basics
- Redshift for data warehousing
- AWS Glue for ETL
- EMR for big data
- Azure Data Factory (pipelines)
- Azure Blob Storage
- Azure Synapse for analytics
- BigQuery for analytics
- Google Cloud Storage
- Dataflow for stream and batch pipelines
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Get full course syllabus in your inbox
Get full course syllabus in your inbox
- Sqoop architecture and setup
- Import/export from MySQL, Oracle to HDFS/Hive
- Incremental loads (append, last-modified)
- Performance tuning using mappers
- 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 Duration
120 Hrs.
Weekday1 Hr/Day
Weekend2 Hr/Day
Training ModeClassroom/Online
Flexible Batches For You
12-Jul-2025*
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16-Jul-2025*
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12-Jul-2025*
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14-Jul-2025*
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- Mor | Aft | Eve - Slot
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16-Jul-2025*
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Course Price :
For Indian
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