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Data pipelines, databases, ETL/ELT, data processing, warehousing, and scalable data infrastructure.
Learner rating
4.8/5
Course duration
6 Months
Learning modules
13 modules
Learning mode
Offline
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Data Engineering is a 6 Months Artificial Intelligence program built around hands-on practice, guided assignments, interview preparation, and placement support. Start with SQL, practise with SQL, Python, Apache Spark, and connect your learning through applied business scenarios. Build a portfolio that explains your approach and prepare for roles such as Data Engineer and ETL Developer.
6 Months structured training program
Hands-on practice with SQL, Python, Apache Spark
Live projects and portfolio-ready assignments
Certification guidance and placement assistance
A structured learning journey
Explore the curriculum, build your understanding one module at a time, and put each new skill into practice.
Find out how this curriculum fits your career goals.
Tools & technologies
Explore industry-relevant tools through hands-on learning to master practical, in-demand skills.
Build practical skills with industry-relevant learning, expert guidance, and hands-on projects designed for modern teams.
Get the complete curriculum, tools, projects, and career support details in one brochure.
Build something that matters
Every student completes 6 real-world projects — one per month — building a professional portfolio demonstrated during placement interviews.
Purpose
uild an automated ETL pipeline that extracts data from multiple sources, cleans and transforms it, and loads it into a relational database.
Business Output
A complete ETL pipeline that automates data extraction, transformation and loading.
Purpose
Process and analyze a large dataset using Apache Spark and PySpark.
Business Output
A scalable data processing workflow that transforms large datasets efficiently.
Purpose
Develop an automated data pipeline using Apache Airflow to schedule and manage ETL workflows.
Business Output
An automated ETL workflow that executes scheduled data processing tasks with monitoring and error handling
Purpose
Build a real-time data pipeline that collects, processes and stores continuously generated data.
Business Output
A real-time data pipeline that continuously collects, processes and stores streaming data.
Portfolio tip: document the problem, your approach, and the result so you can walk an interviewer through your decisions.
Learn more about how we have been impacting thousands of careers.
Recognition that travels with you
Certification pathway
Build a credible portfolio of skills with structured preparation, practical projects, and certification guidance from experienced mentors.
Beyond the classroom
Soft skill session
Resume building
Aptitude training
LinkedIn profile building
Mock interview
Job assistance
Dedicated placement support with industry connections and recruiter interactions.
Your next opportunity starts here
Industry-ready courseFilling fast
CAREER OUTCOME
Offer letter preview
Congratulations!
Your practical skills, projects, and career preparation can help you approach interviews with clarity and confidence.
Role-ready preparation
Portfolio, interview practice, and mentor feedback.
Career counsellor
A little more clarity
Answers to common questions about learning, projects, and getting started.
Data Engineering is the process of designing, building, and maintaining systems that collect, process, transform, and store large volumes of data. It helps organizations make reliable data available for analytics, reporting, and business intelligence.
You will learn Python programming, SQL, database management, data warehousing, ETL and ELT processes, Apache Spark, PySpark, Apache Airflow, Apache Kafka, cloud services, and data pipeline development through practical projects.
Students, graduates, working professionals, software developers, data analysts, and IT professionals interested in building a career in data engineering can join this course.
Basic programming knowledge is helpful but not mandatory. The course covers Python and SQL fundamentals before moving to advanced data engineering concepts.
Yes. Freshers with an interest in programming, databases, and data technologies can start learning Data Engineering. Practical assignments and projects help develop the skills required for entry-level roles.
Python and SQL are the primary languages used in this course. They are widely used for data processing, database operations, ETL workflows, and pipeline development.
The course covers Python, SQL, MySQL, PostgreSQL, Apache Spark, PySpark, Hadoop, Apache Airflow, Apache Kafka, AWS S3, AWS Glue, Amazon Redshift, Amazon Athena, Docker, and Git/GitHub.
ETL stands for Extract, Transform, and Load. It is a process of collecting data from different sources, transforming it into a usable format, and loading it into a target database or data warehouse.
Data Engineering focuses on building data pipelines, managing databases, and preparing reliable data for analysis. Data Analytics focuses on examining data, identifying trends, creating reports, and generating business insights.
The course is designed around practical learning, hands-on exercises, and industry-oriented assignments. Students work on projects involving ETL pipelines, big data processing, workflow automation, and real-time data streaming.
Students work on projects such as: End-to-End ETL Data Pipeline Big Data Processing Using PySpark Automated Data Pipeline Using Apache Airflow Real-Time Data Streaming Using Kafka and Spark
After developing the required technical skills, you can explore roles such as Data Engineer, Junior Data Engineer, ETL Developer, Big Data Engineer, Data Pipeline Developer, and Cloud Data Engineer.
Yes. The course introduces cloud-based data engineering services, including AWS S3, AWS Glue, Amazon Redshift, and Amazon Athena, for data storage, processing, and analytics workflows.
Students who successfully complete the course requirements receive a course completion certificate from Raystech Academy
Raystech Academy provides career guidance and placement assistance to help students prepare for job opportunities. Support may include resume preparation, interview preparation, and guidance on relevant job openings.
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