Data engineering courses can help you learn data modeling, ETL (extract, transform, load) processes, and data warehousing techniques. You can build skills in data pipeline construction, database management, and ensuring data quality and integrity. Many courses introduce tools like Apache Spark, Hadoop, and SQL, that support processing large datasets and optimizing data workflows. You’ll also explore cloud platforms such as AWS and Azure, which facilitate scalable data solutions and enhance your ability to manage data in various environments.

Skills you'll gain: Data Store, Data Architecture, Apache Hadoop, Extract, Transform, Load, Relational Databases, Big Data, Data Storage, Databases, Apache Spark, Data Storage Technologies, Data Lakes, Data Warehousing, Data Governance, Data Pipelines, Data Integration, Data Processing, SQL, NoSQL, Data Security, Data Science
★ 4.7 (3.7K) · Beginner · Course · 1 - 4 Weeks

Multiple educators
Skills you'll gain: Apache Airflow, Data Modeling, Data Pipelines, Data Storage, Data Storage Technologies, Data Architecture, Requirements Analysis, Data Processing, Data Warehousing, Query Languages, Data Preprocessing, Apache Hadoop, Requirements Elicitation, Vector Databases, Data Lakes, Amazon Web Services, Apache Spark, Infrastructure as Code (IaC), Terraform, Data Management
★ 4.7 (628) · Intermediate · Professional Certificate · 3 - 6 Months

Skills you'll gain: Data Engineering, Relational Databases, PostgreSQL, Database Design, Data Pipelines, Data Processing, SQL, Data Modeling, Data Validation, Data Quality, Data Architecture, Extract, Transform, Load, Data Structures, Data Management, Data Transformation, Data Science, Data Analysis, Git (Version Control System), Version Control, Python Programming
Beginner · Course · 1 - 4 Weeks

Skills you'll gain: NoSQL, Extract, Transform, Load, Database Administration, Apache Spark, Data Warehousing, Web Scraping, Bash (Scripting Language), Package and Software Management, Data Pipelines, Apache Hadoop, Database Architecture and Administration, Database Design, SQL, IBM Cognos Analytics, Data Store, Generative AI, Professional Networking, Python Programming, Data Analysis, Data Science
★ 4.6 (63K) · Beginner · Professional Certificate · 3 - 6 Months

Skills you'll gain: Extract, Transform, Load, Web Scraping, Database Design, SQL, IBM DB2, Database Management, Data Store, Data Architecture, Relational Databases, Apache Hadoop, Databases, Big Data, Unit Testing, Data Storage, Apache Spark, Stored Procedure, Data Storage Technologies, Python Programming, NumPy, Data Analysis
★ 4.6 (60K) · Beginner · Specialization · 3 - 6 Months

Snowflake
Skills you'll gain: Data Engineering, Data Pipelines, Database Management, Data Manipulation, Data Transformation, Devops Tools, Change Control, DevOps, SQL, CI/CD, Application Development, Artificial Intelligence and Machine Learning (AI/ML), Role-Based Access Control (RBAC), Data Import/Export, Stored Procedure, Generative AI, Data Sharing, Data Science, Command-Line Interface, Data Analysis
★ 4.7 (388) · Beginner · Professional Certificate · 1 - 3 Months

Microsoft
Skills you'll gain: Data Modeling, Data Governance, Data Pipelines, Data Lakes, Data Integration, Data Architecture, Power BI, Data Manipulation, Dataflow, Capacity Management, Role-Based Access Control (RBAC), Real Time Data, Technical Communication, Data Management, Data Warehousing, Data Transformation, Business Reporting, System Monitoring, Data Processing, Debugging
Intermediate · Professional Certificate · 3 - 6 Months

Skills you'll gain: Model Training, Data Pipelines, Feature Engineering, Data Governance, Extract, Transform, Load, Data Quality, Model Evaluation, AI Workflows, Data Processing, Data Integrity, Data Store, Release Management, Database Systems, Requirements Analysis, Data Management, Database Design, Taxonomy, Data Transformation, Personally Identifiable Information, Dependency Analysis
★ 3.8 (6) · Intermediate · Professional Certificate · 3 - 6 Months
Duke University
Skills you'll gain: Pandas (Python Package), Bash (Scripting Language), Version Control, Jupyter, Linux Commands, Git (Version Control System), Shell Script, Linux, Web Scraping, Data Manipulation, MySQL, Microservices, AWS SageMaker, SQL, JSON, Scripting, Command-Line Interface, Python Programming, Big Data, Data Science
★ 4.5 (487) · Beginner · Specialization · 3 - 6 Months

Skills you'll gain: Extract, Transform, Load, Web Scraping, Database Management, Databases, Unit Testing, Data Transformation, Package and Software Management, Application Programming Interface (API), Integrated Development Environments, Python Programming, Style Guides
★ 4.6 (863) · Intermediate · Course · 1 - 4 Weeks

Skills you'll gain: Dashboard Creation, Model Deployment, Feature Engineering, PySpark, Data Import/Export, Big Data, Apache Spark, Data Governance, Dashboard, Apache Kafka, Cloud Deployment, Data Access, Apache Hadoop, Cloud API, Data Architecture, Apache Hive, Data Quality, Data Cleansing, Applied Machine Learning, Cloud Services
★ 4.6 (4.4K) · Intermediate · Specialization · 3 - 6 Months

DeepLearning.AI
Skills you'll gain: Data Pipelines, Data Architecture, Requirements Analysis, Requirements Elicitation, Amazon Web Services, Data Processing, System Requirements, Performance Tuning, Data Transformation, Scalability
★ 4.7 (512) · Intermediate · Course · 1 - 4 Weeks
Top-rated Data Engineering courses offered by IBM on Coursera.
Earn a certificate in Data Engineering from top universities and companies.
Top-rated beginner-friendly Data Engineering courses with no prerequisites.
Data engineering is the practice of designing, building, and maintaining systems that collect, store, process, and move data for analysis and applications. It often involves creating data pipelines, working with databases, preparing data for analytics, and supporting AI or machine learning workflows. Courses such as Introduction to Data Engineering, IBM Data Engineering, and Data Engineering for AI and ML Pipelines reflect how the field connects software, cloud platforms, and data systems. On Coursera, you can explore data engineering courses that introduce core concepts and build toward hands-on pipeline projects.‎
Data engineering is used in roles that work with data pipelines, cloud data platforms, analytics systems, and AI-ready datasets. Common examples include data engineer, analytics engineer, data platform engineer, ETL developer, and machine learning pipeline engineer, along with data analyst or data scientist roles that require stronger technical data preparation skills. Course options like DeepLearning.AI Data Engineering, Snowflake Data Engineering, and Open source Data Engineering with Spark, dbt & Airflow align with tools and workflows used in these roles. Coursera can help you compare courses based on the systems, tools, and projects most relevant to your goals.‎
Before learning data engineering, it helps to understand basic programming, databases, SQL, and how data is structured. Python is especially useful because many data workflows use it for automation, transformation, and pipeline development, which is why a course like Python Project for Data Engineering can be a practical next step. Familiarity with spreadsheets, basic statistics, and command-line concepts can also make data engineering topics easier to follow. If you are new to the field, starting with foundational courses such as Introduction to Data Engineering or Data Engineering Foundations can help you build confidence before moving into specialized tools.‎
Skills that complement data engineering include cloud computing, Python, SQL, data warehousing, distributed processing, workflow orchestration, and data modeling. Tools and technologies such as Spark, dbt, Airflow, and Snowflake are often used to transform, schedule, and manage data across modern systems. AI and machine learning concepts can also be helpful when data pipelines are built to support model training or production workflows, as reflected in Data Engineering for AI and ML Pipelines. Coursera courses can help you combine these related skills into a learning path that fits analytics, cloud, or AI-focused goals.‎
A good way to start learning data engineering is to begin with core concepts, then practice building small data pipelines. You might start with Introduction to Data Engineering or Data Engineering Foundations to learn how data moves through systems, then add Python, SQL, and cloud or platform-specific skills. Project-based courses such as Python Project for Data Engineering can help you apply what you learn, while Open source Data Engineering with Spark, dbt & Airflow introduces common workflow tools. On Coursera, you can choose beginner-friendly courses first, then progress into specialized options like Snowflake Data Engineering or AI and ML pipeline courses.‎
Yes. You can start learning data engineering on Coursera for free in two ways:
If you want to keep learning, earn a certificate in data engineering, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎
The best beginner data engineering courses are usually those that explain the full data pipeline before focusing on advanced tools. Introduction to Data Engineering and Data Engineering Foundations from IBM are strong starting points because they cover core ideas such as data sources, pipelines, storage, and processing. Python Project for Data Engineering can be useful if you want hands-on practice applying Python to a data workflow. After building the basics, you can consider more specialized courses such as Snowflake Data Engineering, DeepLearning.AI Data Engineering, or Open source Data Engineering with Spark, dbt & Airflow.‎
Data engineering courses typically cover data pipelines, databases, SQL, Python, ETL processes, data warehouses, cloud platforms, and workflow automation. More advanced courses may include distributed processing with Spark, transformation with dbt, orchestration with Airflow, Snowflake-based engineering, or pipelines for AI and machine learning use cases. The EQP course selection includes examples across these areas, such as IBM Data Engineering, Snowflake Data Engineering, DeepLearning.AI Data Engineering, and Data Engineering for AI and ML Pipelines. Comparing course descriptions on Coursera can help you choose whether to focus first on fundamentals, open-source tools, cloud platforms, or AI-ready data workflows.‎