Master the Fundamental Concepts of Data Engineering. Enroll today and become a skilled Data Engineering expert.
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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.
Why Should You Choose Data Engineering Course?
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Course Duration
40 Hrs.
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05-May-2025*
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30-Apr-2025*
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CURRICULUM & PROJECTS
Data Engineering Certification Course
- Understand the purpose of data modeling.
- Identify the strengths and weaknesses of different types of databases and data storage techniques.
- Create a table in Apache Cassandra.
- Understand when to use a relational database.
- Understand the difference between OLAP and OLTP databases.
- Create normalized data tables.
- Implement denormalized schemas (e.g. STAR, Snowflake).
- Understand when to use NoSQL databases and how they differ from relational databases.
- Select the appropriate primary key and clustering columns for a given use case.
- Create a NoSQL database in Apache Cassandra.
- Explain how OLAP may support certain business users better than OLTP.
- Implement ETL for OLAP Transformations with SQL.
- Describe Data Warehouse Architecture.
- Describe OLAP cube from facts and dimensions to slice, dice, roll-up, and drill down operations.
- Implement OLAP cubes from facts and dimensions to slice, dice, roll-up, and drill down.
- Compare columnar vs. row-oriented approaches.
- Implement columnar vs. row-oriented approaches.
- Explain the differences between ETL and ELT.
- Differentiate scenarios where ELT is preferred over ETL.
- Implement ETL for OLAP Transformations with SQL.
- Select appropriate cloud data storage solutions.
- Select appropriate cloud pipeline solutions.
- Select appropriate cloud data warehouse solutions.
- Describe AWS data warehouse services and technologies.
- Create and configure AWS Storage Resources.
- Create and configure Amazon Redshift resources.
- Implement infrastructure as code for Redshift on AWS.
- Describe Redshift data warehouse architecture.
- Run ETL process to extract data from AWS S3 into Redshift.
- Design optimized tables by selecting appropriate distribution styles and sorting keys.
- Identify what constitutes the big data ecosystem for data engineering.
- Explain the purpose and evolution of data lakes in the big data ecosystem.
- Compare the Spark framework with Hadoop framework.
- Identify when to use Spark and when not to use it.
- Describe the features of lakehouse architecture.
- Wrangle data with Spark and functional programming to scale across distributed systems.
- Process data with Spark DataFrames and Spark SQL.
- Process data in common formats such as CSV and JSON.
- Use the Spark RDDs API to wrangle data.
- Transform and filter data with Spark.
- Use distributed data storage with Amazon S3.
- Identify properties of AWS S3 data lakes.
- Identify service options for using Spark in AWS.
- Configure AWS Glue.
- Create and run Spark Jobs with AWS Glue.
- Use Spark with AWS Glue to run ELT processes on data of diverse sources, structures, and vintages in lakehouse architecture.
- Create a Glue Data Catalog and Glue Tables.
- Use AWS Athena for ad-hoc queries in a lakehouse.
- Leverage Glue for SQL AWS S3 queries and ELT.
- Ingest data into lakehouse zones.
- Transform and filter data into curated lakehouse zones with Spark and AWS Glue.
- Join and process data into lakehouse zones with Spark and AWS Glue.
- Define and describe a data pipeline and its usage.
- Explain the relationship between DAGs, S3, and Redshift within a given example.
- Employ tasks as instantiated operators.
- Organize task dependencies based on logic flow.
- Apply templating in codebase with kwargs parameter to set runtime variables.
- Create Airflow Connection to AWS using AWS credentials.
- Create Postgres/Redshift Airflow Connections.
- Leverage hooks to use Connections in DAGs.
- Connect S3 to a Redshift DAG programmatically.
- Utilize the logic flow of task dependencies to investigate potential errors within data lineage.
- Leverage Airflow catchup to backfill data.
- Extract data from a specific time range by employing the kwargs parameters.
- Create a task to ensure data quality within select tables.
- Consolidate repeated code into operator plugins.
- Refactor a complex task into multiple tasks with separate SQL statements.
- Convert an Airflow 1 DAG into an Airflow 2 DAG.
- Construct a DAG and custom operator end-to-end.
Mock Interviews
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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.
- - Build an Impressive Resume
- - Get Tips from Trainer to Clear Interviews
- - Attend Mock-Up Interviews with Experts
- - Get Interviews & Get Hired
If yes, Register today and get impeccable Learning Solutions!
Training Features
Instructor-led Sessions
The most traditional way to learn with increased visibility,monitoring and control over learners with ease to learn at any time from internet-connected devices.
Real-life Case Studies
Case studies based on top industry frameworks help you to relate your learning with real-time based industry solutions.
Assignment
Adding the scope of improvement and fostering the analytical abilities and skills through the perfect piece of academic work.
Lifetime Access
Get Unlimited access of the course throughout the life providing the freedom to learn at your own pace.
24 x 7 Expert Support
With no limits to learn and in-depth vision from all-time available support to resolve all your queries related to the course.
Certification
Each certification associated with the program is affiliated with the top universities providing edge to gain epitome in the course.
Showcase your Course Completion Certificate to Recruiters
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Training Certificate is Govern By 12 Global Associations.
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Training Certificate is Powered by “Wipro DICE ID”
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