Data Engineering Online Courses & Certifications

Looking to master the essential skills of data engineering? Our curated list of top data engineering courses is designed to help you excel in this rapidly growing field. Whether you're just starting out or looking to advance your career, these courses offer in-depth knowledge and hands-on experience to propel you forward. 

Click below to discover the best courses that will take your data engineering skills to the next level!

What is Data Engineering?

Data engineering involves designing, building, and maintaining systems and infrastructure that allow for the collection, storage, processing, and analysis of large amounts of data. The primary goal is to ensure that data is accessible, reliable, and in a usable format for analysts and data scientists.

Key Components of Data Engineering

1. Data Collection: This is the process of gathering data from various sources, such as databases, APIs, and streaming services. Data engineers create pipelines to automate this process.
2. Data Storage: Once collected, data needs to be stored securely and efficiently. This involves using databases and data warehouses that can handle large volumes of data and support quick access and retrieval.
3. Data Processing: Raw data often needs to be cleaned and transformed before it can be useful. Data engineers develop processes and use tools to clean, organize, and transform data into a usable format. This includes tasks like removing duplicates, filling in missing values, and converting data types.
4. Data Pipelines: A data pipeline is a series of steps that data goes through from collection to storage to processing. Data engineers design and build these pipelines to ensure data flows smoothly and reliably.
5. Data Integration: Often, data comes from multiple sources and needs to be combined in a meaningful way. Data engineers ensure that data from different sources can be integrated and used together.
6. Data Quality and Governance: Ensuring data accuracy, consistency, and security is crucial. Data engineers implement measures to maintain high data quality and adhere to data governance policies.

Online Data Engineering Course Highlight

Aspect	Details
Type of Course	Online, Self-paced, Distance learning
Duration	1 week - 6 months
Course Pedagogy	Video lectures, interactive simulations, hands-on labs, coding exercises, data engineering projects, quizzes
Specializations	Data Pipelines, Data Warehousing, Big Data Processing, ETL, Cloud Data Engineering, Machine Learning Engineering
Eligibility Criteria	Often no formal prerequisites; some advanced courses may require basic IT or programming knowledge
Top Providers	Coursera, edX, Udacity, Udemy, DataCamp, Pluralsight
Top Recruiters	IT companies, tech startups, financial institutions, healthcare organizations, e-commerce companies, consulting firms
Certifications	Google Cloud Professional Data Engineer, AWS Certified Data Analytics, Microsoft Certified: Azure Data Engineer Associate, IBM Data Engineering Professional Certificate, Cloudera Certified Professional Data Engineer

Who Can Take Data Engineering Courses?

1. IT Professionals
1. Description: Individuals currently working in the IT industry who want to enhance their skills and specialize in data management, data processing, and data infrastructure.
2. Why: Data engineering is a crucial aspect of modern IT operations, and professionals can advance their careers by gaining expertise in this area.
2. Aspiring Data Engineers
1. Description: People who are specifically looking to start a career in data engineering.
2. Why: These courses provide the necessary knowledge and skills to design, build, and maintain data infrastructure, which is essential for a data engineering role.
3. Software Developers
1. Description: Developers who wish to transition into data-related roles by gaining knowledge in data engineering.
2. Why: Understanding data pipelines, big data processing, and ETL (Extract, Transform, Load) processes can broaden a developer's skill set and open up new career opportunities.
4. Database Administrators (DBAs)
1. Description: DBAs looking to expand their expertise to include modern data engineering tools and techniques.
2. Why: With the rise of big data and cloud technologies, traditional database management skills need to be supplemented with knowledge of scalable data processing and storage solutions.
5. Data Scientists and Data Analysts
1. Description: Professionals in data science and analysis roles who want to understand the infrastructure and processes behind the data they work with.
2. Why: Gaining insight into data engineering can help these professionals improve their data analysis workflows and ensure the data they use is properly managed and processed.
6. Students and Graduates
1. Description: Individuals with a background in computer science, information technology, or related fields who are looking to enter the data engineering field.
2. Why: These courses provide foundational knowledge and practical skills necessary to start a career in data engineering.
7. Professionals in Related Fields
1. Description: Individuals working in data warehousing, business intelligence, and ETL development who want to deepen their knowledge of data engineering.
2. Why: Understanding the latest data engineering practices can enhance their current roles and provide opportunities for career growth.
8. Tech Enthusiasts
1. Description: People with a keen interest in learning about data engineering for personal growth or to keep up with industry trends.
2. Why: Staying informed about data engineering can provide a competitive edge in the tech industry and open up new opportunities for innovation and career development.
9. Managers and Team Leads
1. Description: Leaders in tech fields who want to better understand data engineering processes to manage teams more effectively.
2. Why: Knowledge of data engineering allows managers to make informed decisions, effectively lead technical teams, and ensure projects are aligned with industry best practices.

How to Choose the Best Data Engineering Courses?

1. What are your learning goals?

1. Answer: Identify whether you are a beginner or have some experience in data engineering.
1. For Beginners:
1. Look for introductory courses covering the basics of data processing, databases, and data pipelines.
2. Ensure the course includes fundamental concepts such as SQL, basic ETL processes, and introductory data warehousing.
2. For Intermediate/Advanced Learners:
1. Seek courses that dive deeper into specific technologies like Hadoop, Spark, Kafka, or cloud platforms (AWS, Google Cloud, Azure).
2. Ensure the course covers advanced topics such as big data processing, real-time data streaming, and complex ETL processes.

2. What should you look for in the course content and curriculum?

1. Answer: Ensure the course covers essential topics and provides comprehensive knowledge.
1. Key Topics:
1. ETL (Extract, Transform, Load) processes
2. Data warehousing
3. Big data technologies (Hadoop, Spark)
4. Cloud data engineering (AWS, Google Cloud, Azure)
5. Data governance and quality
6. Data integration and pipelines
2. Updated Content:
1. Verify that the course content is current and reflects the latest industry practices and tools.
2. Look for courses that include emerging technologies and trends in data engineering.

3. What type of learning format suits you best?

1. Answer: Decide if you prefer a self-paced or scheduled learning format.
1. Self-paced:
1. Learn at your own speed, which is ideal for those with irregular schedules.
2. Scheduled:
1. Fixed deadlines and live sessions, which can provide a more structured learning experience.
3. Hands-on Labs and Projects:
1. Ensure the course includes practical experience through labs, coding exercises, and real-world projects.
2. Practical application helps reinforce theoretical knowledge and develop real-world skills.

4. How do you assess the instructors?

1. Answer: Evaluate the qualifications and experience of the instructors.
1. Expertise and Experience:
1. Check instructors’ backgrounds to ensure they have real-world experience in data engineering.
2. Look for instructors who have worked on significant data engineering projects or have industry recognition.
2. Teaching Style:
1. Review sample lectures or videos to see if the teaching style matches your learning preferences.
2. Effective communication and clear explanations are crucial for understanding complex concepts.

5. Why are course reviews and ratings important?

1. Answer: Reviews and ratings provide insights into the course quality and student satisfaction.
1. Student Feedback:
1. Look for detailed reviews that discuss the course content, instructor effectiveness, and overall experience.
2. Positive reviews from students who have successfully transitioned into data engineering roles are a good indicator.
2. Success Stories:
1. Check if the course has testimonials or success stories from graduates who have achieved their career goals.

6. What should you consider regarding accreditation and certification?

1. Answer: Ensure the course offers recognized certification and comes from reputable sources.
1. Recognized Certificates:
1. The certificate should be recognized by employers and relevant in the industry.
2. Certificates from well-known platforms like Coursera, edX, Udacity, and industry-recognized certifications (Google Cloud Professional Data Engineer, AWS Certified Data Analytics, etc.) are valuable.
2. Accredited Institutions:
1. Courses from accredited institutions or reputable online platforms typically have higher credibility.

7. How do you evaluate the cost and financial aid options?

1. Answer: Compare the costs and explore available financial aid options.
1. Course Fees:
1. Consider what is included in the price, such as access to resources, support services, and additional materials.
2. Financial Aid and Scholarships:
1. Check if the course provider offers financial aid, scholarships, or payment plans to make the course more affordable.

8. What support and resources are available?

1. Answer: Ensure the course provides adequate support and additional learning resources.
1. Instructor Support:
1. Access to instructors for questions and clarifications is crucial for effective learning.
2. Community and Peer Support:
1. A supportive learning community or peer group can enhance your learning experience through collaboration and discussion.
3. Additional Resources:
1. Check if the course provides supplementary materials such as textbooks, reference guides, and exclusive content.

9. Is job placement assistance provided?

1. Answer: Some courses offer job placement assistance and career services.
1. Career Services:
1. Look for courses that provide job placement assistance, resume building, interview preparation, and networking opportunities.
2. Industry Partnerships:
1. Courses with connections to industry partners may offer better opportunities for internships or job placements.

10. How flexible and accessible is the course?

1. Answer: Choose a course that fits your schedule and is easily accessible.
1. Course Duration:
1. Ensure the course duration is manageable and allows you to balance other commitments.
2. Accessibility:
1. Verify that the course platform is user-friendly and accessible on various devices, such as desktops and mobile devices.

How to Become a Data Engineering Professional?

To become a data engineering professional, you need a solid foundation in computer science, information technology, or a related field, coupled with hands-on experience in dealing with real-world data challenges. 

Here are the steps to guide you through different levels of expertise:

No Prior Knowledge

1. Study Data Engineering Fundamentals
1. Learn about data architectures, ETL processes, and data warehousing.
2. Understand key programming languages: Python, Java, and SQL.
2. Take Up Data Engineering Certification Courses
1. Enroll in beginner courses on platforms like Coursera, Udemy, and edX.
2. Courses to consider:
1. Data Engineering on Google Cloud Platform Specialization (Coursera)
2. Big Data Essentials (edX)
3. Introduction to Data Engineering (Udemy)
3. Understand Key Technologies
1. Learn about Hadoop, Spark, and cloud platforms like AWS, Azure, and Google Cloud.
2. Get familiar with relational (MySQL, PostgreSQL) and NoSQL (MongoDB, Cassandra) databases.

Basic Knowledge

1. Pursue Internships in Data Engineering
1. Gain practical experience by working on real-world data projects.
2. Apply for internships at tech companies and startups.
2. Develop Data Engineering Skills
1. Learn to build data pipelines and work with ETL tools (Apache NiFi, Talend).
2. Practice writing and optimizing SQL queries.
3. Take Entry-Level Roles
1. Apply for positions such as data engineering intern, junior data engineer, or data analyst.
2. Focus on gaining experience with data integration and processing.
4. Get Certified
1. Obtain entry-level certifications like:
1. Google Cloud Professional Data Engineer
2. AWS Certified Big Data – Specialty
3. Microsoft Certified: Azure Data Engineer Associate

Intermediate Knowledge

1. Gain Experience with Data Engineering Projects
1. Work as a data engineer, focusing on data pipeline development, data warehousing, and big data solutions.
2. Take on roles such as data analyst, data engineer, or data consultant.
2. Advance Your Certifications
1. Earn advanced certifications in data engineering and cloud technologies.
2. Consider certifications like:
1. Cloudera Certified Professional Data Engineer
2. IBM Certified Data Engineer
3. Specialize in Big Data and Cloud Platforms
1. Deepen your knowledge of Hadoop, Spark, and cloud data services.
2. Work on complex data integration and processing projects.
4. Consider Advanced Education
1. Pursue a master’s degree in data engineering, data science, or a related field.
2. Specialize in areas such as machine learning, artificial intelligence, and big data analytics.

Advanced Knowledge

1. Take Up Advanced Roles
1. Move into senior roles such as senior data engineer, data architect, or big data engineer.
2. Lead data engineering teams and manage large-scale data projects.
2. Get an MBA in IT or Data Management
1. Consider an MBA focusing on IT management or data engineering to gain leadership skills.
2. Learn about data strategy, data governance, and data security.
3. Enter Management Positions
1. Aim for roles such as Chief Data Officer (CDO) or Director of Data Engineering.
2. Oversee data strategy, infrastructure, and team management.
4. Develop and Implement Data Strategies
1. Design and deploy comprehensive data solutions for large organizations.
2. Improve existing data systems and ensure data quality and compliance.
5. Stay Updated with Industry Trends
1. Participate in conferences, workshops, and professional networks.
2. Keep up with the latest advancements in data engineering technologies and methodologies.

Popular Job Profiles in Data Engineering

Job Profile	Description	Average Salary (INR)
Data Engineer	Responsibilities: Designing, constructing, and maintaining large-scale processing systems and ensuring the smooth operation of data pipelines.  Skills Required: Proficiency in SQL, Python, Java, and ETL tools; knowledge of data warehousing solutions like Redshift or BigQuery.  Career Path: Entry-level roles may include junior data engineer or data engineering intern.	₹10.7 Lakhs per year
Senior Data Engineer	Responsibilities: Leading data engineering projects, mentoring junior engineers, designing advanced data architectures, and ensuring data quality and consistency.  Skills Required: Advanced knowledge of big data technologies (Hadoop, Spark), cloud platforms (AWS, Azure), and data modeling. Career Path: Progression from data engineer roles, moving towards lead or principal data engineer.	₹17 Lakhs per year
Big Data Engineer	Responsibilities: Specializing in big data technologies like Hadoop and Spark to handle and process large datasets efficiently.  Skills Required: Expertise in big data frameworks, data pipeline development, and real-time data processing.  Career Path: Typically follows from a background in data engineering or software development with a focus on big data.	₹14.3 Lakhs per year
Data Architect	Responsibilities: Designing and managing the data architecture, creating blueprints for data management systems, and ensuring efficient data flow and storage.  Skills Required: Extensive experience in database design, data modeling, and knowledge of various data storage solutions. Career Path: Progression from senior data engineering roles, moving towards enterprise architect positions.	₹21.0 Lakhs per year
Data Warehouse Engineer	Responsibilities: Designing, developing, and maintaining data warehouse solutions to support business intelligence activities. Skills Required: Proficiency in ETL processes, data warehousing tools, and SQL.  Career Path: Roles in business intelligence or data analysis, moving towards specialized data warehousing positions.	₹12.4 Lakhs per year
Machine Learning Engineer	Responsibilities: Implementing machine learning models and algorithms within data pipelines, developing predictive models, and optimizing machine learning workflows. Skills Required: Knowledge of machine learning frameworks (TensorFlow, PyTorch), programming skills (Python, R), and data preprocessing techniques. Career Path: Typically follows from roles in data science or software engineering with a focus on machine learning.	₹8.7 Lakhs per year
Cloud Data Engineer	Responsibilities: Managing data storage and processing in cloud environments, developing cloud-based data solutions, and ensuring data security and compliance.  Skills Required: Experience with cloud platforms (AWS, Azure, Google Cloud), knowledge of cloud data storage solutions, and proficiency in cloud-native data processing tools. Career Path: Progression from data engineering or cloud engineering roles, moving towards cloud solutions architect positions.	₹16.3 Lakhs per year
ETL Developer	Responsibilities: Developing ETL processes to extract, transform, and load data from various sources into data warehouses, ensuring data integrity and consistency. Skills Required: Proficiency in ETL tools (Informatica, Talend), strong SQL skills, and understanding of data integration techniques. Career Path: Entry-level roles in data engineering or data analysis, moving towards specialized ETL development positions.	₹7.2 Lakhs per year
Data Analyst	Responsibilities: Analyzing complex data sets to identify trends, patterns, and insights that can inform business decisions. Skills Required: Proficiency in data analysis tools (Excel, SQL, Tableau), strong analytical and problem-solving skills, and knowledge of statistical methods.  Career Path: Entry-level roles in business analysis or market research, moving towards senior data analyst or business intelligence analyst positions.	₹6.2 Lakhs per year
Business Intelligence (BI) Developer	Responsibilities: Developing BI solutions to support data-driven decision-making within organizations, creating dashboards, and generating reports.  Skills Required: Proficiency in BI tools (Power BI, Tableau), strong SQL skills, and understanding of data visualization techniques.  Career Path: Entry-level roles in data analysis or reporting, moving towards senior BI developer or BI architect positions.	₹12.1 Lakhs per year

Why Should You Choose Data Engineering as a Career Option?

Data engineering is one of the most dynamic and rapidly growing fields within the tech industry. Here are some compelling reasons why you should consider a career in data engineering:

1. High Demand for Data Engineers

1. Growing Data Volumes: With the exponential growth of data, companies need skilled professionals to manage and process this data efficiently.
2. Industry Demand: Data engineers are in high demand across various industries, including finance, healthcare, technology, and e-commerce.
3. Job Security: As data continues to play a crucial role in business decisions, the need for data engineers will only increase, ensuring job stability.

2. Attractive Salary Packages

1. Competitive Salaries: Data engineering roles offer competitive salaries, often higher than many other tech positions.
2. Salary Growth: As you gain experience and expertise, salary prospects significantly improve.
3. Global Opportunities: Data engineers are sought after globally, providing opportunities for international careers.

3. Diverse Career Opportunities

1. Various Roles: You can specialize in areas such as data architecture, big data engineering, machine learning engineering, or cloud data engineering.
2. Career Progression: There are clear paths for advancement, leading to roles like Senior Data Engineer, Data Architect, or Chief Data Officer (CDO).
3. Interdisciplinary Field: Data engineering intersects with fields like data science, machine learning, and cloud computing, allowing for diverse career options.

4. Impactful Work

1. Data-Driven Decisions: Your work enables organizations to make data-driven decisions that can significantly impact business strategies and outcomes.
2. Innovation: Data engineers are at the forefront of technological innovation, developing solutions that drive efficiency and new capabilities.
3. Problem Solving: The role involves solving complex problems and optimizing data processes, making it intellectually stimulating.

5. Continuous Learning and Growth

1. Evolving Field: The field of data engineering is continuously evolving with new technologies and methodologies.
2. Learning Opportunities: There are abundant opportunities for learning and professional development through courses, certifications, and conferences.
3. Cutting-Edge Technologies: You get to work with the latest technologies in big data, cloud computing, and data processing.

6. Flexibility and Remote Work

1. Remote Opportunities: Many data engineering roles offer the flexibility to work remotely, providing a better work-life balance.
2. Freelance and Consulting: Skilled data engineers have the option to work as freelancers or consultants, offering services to multiple clients.

7. Foundation for Advanced Roles

1. Path to Data Science: Experience in data engineering provides a strong foundation for transitioning into data science or machine learning roles.
2. Leadership Roles: With experience, data engineers can move into leadership positions, overseeing data strategy and infrastructure.

Best Data Engineering Courses Online

 

Here we have listed some of the best courses from the best course providers, hosted on top e-learning platforms. These courses can give you a competitive advantage in your career as a data engineer. Go through them and pick the one matching your career aspirations.

 

1. SRM University - M.Tech. in Data Engineering
2. IIIT Hyderabad - Post Graduate Certificate in Software Engineering for Data Science
3. Data Engineering Essentials Hands-on - SQL, Python and Spark by Udemy
4. IBM Professional Certificate in Data Engineering Fundamentals by edX
5. Microsoft Azure for Data Engineering by Coursera
6. Become a Data Engineer by Udacity
7. Cloud Data Engineering by Coursera
8. Preparing for the Google Cloud Professional Data Engineer Exam by Coursera
9. PG Program in Big Data Engineering by upGrad
10. Data Engineering Foundations Specialization by Coursera
11. Data Engineering in MagicLab: The Birth and Life of an Event by Pluralsight

 

Free Online Data Engineering Courses 

Below are some free data engineering courses from popular course providers. For some courses, you can start free and to receive the certificate, you may have to pay, while some courses are entirely free. Take your pick accordingly.

 

1. Data Engineering Basics for Everyone by edX
2. Python for Data Engineering Project by edX
3. Azure for the Data Engineer by Microsoft
4. Microsoft Certified: Azure Data Engineer Associate by Microsoft Azure
5. Data Engineering in MagicLab: The Birth and Life of an Event by Pluralsight
6. Data Engineering on Microsoft Azure by Microsoft Azure
7. SQL Concepts for Data Engineers by edX
8. Perform data engineering with Azure Synapse Apache Spark Pools by Microsoft

 

Top Data Engineering Courses in India

Institution	Course Name	Mode	Duration	Fees (INR)	Key Highlights
IIT Madras	Data Science and Engineering	Online	12 Months	₹3,50,000	Offered in partnership with Great Learning; covers Python, SQL, Hadoop, Spark, and cloud computing.
Simplilearn	Big Data Engineer Master's Program	Online	12 Months	₹1,70,000	Covers Hadoop, Spark, Kafka, and AWS; includes hands-on projects and capstone projects.
UpGrad	PG Certification in Data Science and Engineering	Online	12 Months	₹3,25,000	Collaboration with IIIT Bangalore; includes SQL, Python, Spark, Kafka, and real-time data processing.
edX	Professional Certificate in Data Engineering	Online	6 Months	₹90,000	Offered by Microsoft; covers Azure, SQL, NoSQL, ETL processes, and data pipelines.

Best Data Engineering Degree Programs in India

Institution	Program Name	Degree	Duration	Key Highlights
Indian Institute of Technology (IIT) Madras	B.Tech in Data Science and Engineering	Bachelor's	4 Years	Interdisciplinary program combining computer science, statistics, and data management.
Indian Institute of Technology (IIT) Hyderabad	B.Tech in Artificial Intelligence with Data Engineering focus	Bachelor's	4 Years	Focus on AI, machine learning, and data engineering; includes hands-on projects and industry internships.
Indian Institute of Technology (IIT) Jodhpur	M.Tech in Data and Computational Science	Master's	2 Years	Specialized courses in data engineering, big data analytics, and cloud computing.
Indian Institute of Technology (IIT) Kharagpur	M.Tech in Computer Science and Data Processing	Master's	2 Years	Focuses on data engineering, big data technologies, and data analytics.
National Institute of Technology (NIT) Trichy	M.Tech in Data Analytics	Master's	2 Years	Emphasizes data engineering, machine learning, and big data technologies.
International Institute of Information Technology (IIIT) Bangalore	M.Tech in Data Engineering	Master's	2 Years	Covers data wrangling, big data technologies, cloud platforms, and machine learning.
Birla Institute of Technology and Science (BITS) Pilani	M.Tech in Data Science and Engineering	Master's	2 Years	Combines data engineering, data science, and analytics; includes practical projects and industry exposure.
Amity University	B.Tech in Data Science and Engineering	Bachelor's	4 Years	Interdisciplinary approach with a strong focus on data engineering and big data technologies.
Vellore Institute of Technology (VIT)	M.Tech in Data Science	Master's	2 Years	Comprehensive program covering data engineering, big data, and machine learning.
Manipal Academy of Higher Education	M.Sc in Data Science	Master's	2 Years	Focus on data engineering, data analysis, and big data technologies.