10 Best Data Engineering Courses Online in 2024

Collecting, storing, and analyzing massive datasets is key to industries like tech, healthcare, and finance. Data engineering is the backbone of this process, making it a growing field with a demand for professionals who can build and maintain the data analysis infrastructure.

Data engineers create the pipelines and systems that allow businesses to turn raw data into valuable insights. In recent years, numerous online learning data engineering opportunities have emerged, making course selection more overwhelming than ever.

This blog post guides you through the top-rated data engineering courses available online in 2024, helping you find the right fit. By the end, you'll be ready to make an informed choice and start your data engineering journey.

1. Google Cloud Platform Big Data and Machine Learning Fundamentals

This course, offered by Google Cloud Training (via Coursera), provides a comprehensive introduction to the core components of data engineering and machine learning within the Google Cloud Platform ecosystem.

Key Modules and Learning Outcomes:

• Understand the core components of data engineering and machine learning on GCP.
• Learn how to design data processing systems, build and operationalize machine learning models, and leverage unstructured data using GCP services.
• Get hands-on experience with GCP tools such as BigQuery, Dataflow, Dataproc, and AI Platform.

Format, Duration, and Prerequisites:

• Self-paced
• Approximately 1 week (9 hours) to complete
• No specific prerequisites, but a basic understanding of data concepts and cloud computing is recommended

Pros:

• Directly from Google, ensuring high-quality and up-to-date content.
• Focuses on a leading cloud platform, GCP, which is in high demand.
• Provides practical experience with essential GCP tools and services.

Cons:

• Assumes some familiarity with cloud computing and data concepts.
• May not provide the depth required for advanced data engineering roles.

Specific Benefits:

• Prepares learners for the Google Cloud Professional Data Engineer certification.
• Helps build practical skills using real-world examples and case studies.

At a glance:

• Duration: Approximately 9 hours
• Cost: Free
• Format: Online, self-paced
• Prerequisites: None

2. IBM Data Engineering Professional Certificate

This professional certificate program, offered on Coursera in partnership with IBM, covers the essential skills and tools required for a career in data engineering.

Key Modules and Learning Outcomes:

• Learn the fundamentals of data engineering, including data pipelines, data warehouses, and ETL processes.
• Gain proficiency in using tools such as Apache Spark, Hadoop, and relational databases.
• Develop practical skills through hands-on projects and labs.

Format, Duration, and Prerequisites:

• Self-paced
• Approximately 6 months to complete (at a suggested pace of 10 hours/week)
• No prior experience in data engineering is required, but basic knowledge of Python and SQL is beneficial.

Pros:

• Offers a well-rounded introduction to data engineering concepts and tools.
• Provides hands-on experience through projects and labs.
• Awarded by IBM, adding value to your resume.

Cons:

• May not delve deeply into advanced topics or specialized areas of data engineering.
• Requires a significant time commitment to complete the entire program.

Specific Benefits:

• Prepares learners for entry-level data engineering roles.
• Earns a professional certificate from IBM upon completion.

At a glance:

• Duration: 6 months (10 hours/week)
• Cost: Varies (financial aid available)
• Format: Online, self-paced
• Prerequisites: Basic computer skills and a grounding in IT systems recommended

3. Data Engineering Nanodegree

Udacity's Data Engineering Nanodegree program offers a comprehensive curriculum covering data modeling, data warehousing, data lakes, and real-time data processing.

Key Modules and Learning Outcomes:

• Build data pipelines and warehouses using Apache Airflow, Apache Spark, and cloud services.
• Learn to design and implement data models for optimal storage and retrieval.
• Work on real-world projects to gain practical experience in data engineering.

Format, Duration, and Prerequisites:

• Self-paced
• Estimated completion time: 2 months
• Prerequisites: Intermediate Python and SQL knowledge required.

Pros:

• Project-based learning provides hands-on experience.
• Focuses on in-demand skills and technologies used by industry leaders.
• Career services assistance, including resume review and interview preparation.

Cons:

• Can be more expensive compared to other online courses.
• Requires a dedicated time commitment for successful completion.

Specific Benefits:

• Nanodegree credential recognized by industry partners.
• Access to a vibrant student community and mentorship opportunities.

At a glance:

• Duration: Approximately 2 months
• Cost: Varies (payment plans available)
• Format: Online, self-paced
• Prerequisites: Intermediate Python and SQL knowledge

4. The Complete Apache Spark and Python for Big Data with PySpark

This Udemy course provides an in-depth exploration of Apache Spark and its Python API, PySpark, for big data processing.

Key Modules and Learning Outcomes:

• Master the core concepts of Apache Spark and its distributed computing architecture.
• Learn to use PySpark for data cleaning, transformation, analysis, and machine learning.
• Work on real-world projects to apply Spark and PySpark skills.

Format, Duration, and Prerequisites:

• Self-paced
• Approximately 10.5 hours of video content
• Prerequisites: Basic Python knowledge recommended.

Pros:

• Deep dive into Apache Spark and PySpark, essential tools for data engineers.
• Affordable price point compared to nanodegree programs.
• Lifetime access to course materials and updates.

Cons:

• Focuses primarily on Spark and PySpark, may not cover other data engineering tools and technologies.
• Quality control on Udemy can vary; read reviews before enrolling.

Specific Benefits:

• Gain expertise in big data processing using Spark and PySpark.
• Learn from an experienced instructor with industry knowledge.

At a glance:

• Duration: 10.5 hours of video content
• Cost: Varies (frequent discounts available)
• Format: Online, self-paced
• Prerequisites: Basic Python knowledge recommended

5. Data Engineering on Google Cloud Platform Specialization

This specialization, offered on Coursera in partnership with Google Cloud Training, comprises a series of courses designed to equip learners with the skills needed to design and build data processing systems on GCP.

Key Modules and Learning Outcomes:

• Learn to use BigQuery, Dataflow, Dataproc, and other GCP services for data ingestion, processing, and analysis.
• Design and implement data pipelines for batch and streaming data.
• Build data warehouses and data lakes on GCP.

Format, Duration, and Prerequisites:

• Self-paced
• Estimated completion time: 1 month (at a suggested pace of 10 hours/week)
• Prerequisites: Basic knowledge of SQL and Python is recommended.

Pros:

• Comprehensive coverage of GCP data engineering tools and services.
• Hands-on labs and projects provide practical experience.
• Specialization certificate from Google Cloud upon completion.

Cons:

• Requires a commitment to complete multiple courses within the specialization.
• Assumes some familiarity with cloud computing concepts.

Specific Benefits:

• Prepares learners for the Google Cloud Professional Data Engineer certification.
• Develops in-demand skills for cloud-based data engineering roles.

At a glance:

• Duration: Approximately 1 month
• Cost: Varies (financial aid available)
• Format: Online, self-paced
• Prerequisites: Basic SQL and Python knowledge recommended

6. AWS Certified Big Data – Specialty

This certification course focuses on preparing learners for the AWS Certified Big Data – Specialty exam, validating their expertise in designing and implementing big data solutions on AWS.

Key Modules and Learning Outcomes:

• Master AWS big data services like EMR, Redshift, Kinesis, and Glue.
• Learn to design and implement scalable and cost-efficient big data solutions.
• Gain hands-on experience with AWS tools and services through labs and practice exams.

Format, Duration, and Prerequisites:

• Self-paced or instructor-led options
• Duration varies depending on the chosen format and learning pace
• Prerequisites: At least 2 years of experience working with AWS big data services is recommended.

Pros:

• Directly from AWS, ensuring alignment with industry best practices.
• Industry-recognized certification demonstrates expertise in AWS big data solutions.
• Focuses on practical skills and real-world scenarios.

Cons:

• Assumes prior experience with AWS and big data technologies.
• Can be more expensive compared to other online courses.

Specific Benefits:

• Boosts career prospects for data engineers working with AWS.
• Validates skills and knowledge required for the AWS Certified Big Data – Specialty certification.

At a glance:

• Duration: Varies
• Cost: Varies (depends on chosen format)
• Format: Online, self-paced or instructor-led
• Prerequisites: 2+ years of experience with AWS big data services recommended

7. Data Engineering with Azure

This collection of learning paths and modules provides a comprehensive introduction to data engineering on Microsoft Azure.

Key Modules and Learning Outcomes:

Learn to use Azure services like Data Factory, Databricks, Synapse Analytics, and HDInsight for data engineering tasks.

• Design and implement data pipelines for batch and real-time processing.
• Build data warehouses and data lakes on Azure.

Format, Duration, and Prerequisites:

• Self-paced
• Duration varies depending on the chosen learning paths and modules
• Prerequisites: Basic knowledge of SQL and Python is recommended.

Pros:

• Free access to learning resources directly from Microsoft.
• Covers a wide range of Azure data engineering tools and services.
• Offers flexibility to choose learning paths based on your interests and goals.

Cons:

• May require additional resources or practice to fully master the concepts.
• Learning paths and modules may not be as structured as a formal course.

Specific Benefits:

• Develops in-demand skills for Azure data engineering roles.
• Provides a cost-effective way to learn about Azure data services.

At a glance:

• Duration: Varies
• Cost: Free
• Format: Online, self-paced
• Prerequisites: Basic SQL and Python knowledge recommended

8. The Ultimate Hands-On Hadoop – Tame Your Big Data!

This hands-on Udemy course teaches the fundamentals of Hadoop and its ecosystem for big data management and processing.

Key Modules and Learning Outcomes:

• Understand the core components of Hadoop, including HDFS, MapReduce, and YARN.
• Learn to use Hadoop tools like Hive, Pig, and HBase for data analysis and storage.
• Work on practical exercises and projects to apply Hadoop skills.

Format, Duration, and Prerequisites:

• Self-paced
• Approximately 14.5 hours of video content
• Prerequisites: Basic understanding of Linux command line and Java is beneficial.

Pros:

• Focuses on Hadoop, a foundational technology in big data ecosystems.
• Hands-on approach with practical exercises and projects.
• Affordable price point.

Cons:

• May not cover newer big data technologies or cloud-based solutions extensively.
• Quality control on Udemy can vary; read reviews before enrolling.

Specific Benefits:

• Gain foundational knowledge of Hadoop and its ecosystem.
• Learn from an experienced instructor with real-world experience.

At a glance:

• Duration: Approximately 14.5 hours of video content
• Cost: Varies (frequent discounts available)
• Format: Online, self-paced
• Prerequisites: Basic Linux and Java knowledge recommended

9. Data Warehousing for Business Intelligence Specialization

This course explores the concepts and techniques of data warehousing and its application in business intelligence.

Key Modules and Learning Outcomes:

• Understand data warehousing architecture and design principles.
• Learn to use ETL tools and techniques for data extraction, transformation, and loading.
• Explore data modeling and visualization for business intelligence.

Format, Duration, and Prerequisites:

• Self-paced
• Estimated completion time: 3 months (at a suggested pace of 5 hours/week)
• Prerequisites: None, but a basic understanding of databases and SQL is beneficial.

Pros:

• Focuses on the intersection of data engineering and business intelligence.
• Covers essential concepts and tools for data warehousing.
• Specialization certificate from the University of Colorado System.

Cons:

• May not delve deeply into advanced data engineering topics or big data technologies.
• Primarily focuses on conceptual understanding rather than hands-on practice.

Specific Benefits:

• Develop skills in data warehousing and business intelligence.
• Learn from experienced instructors from the University of Colorado.

At a glance:

• Duration: Approx. 3 months
• Cost: Varies (financial aid available)
• Format: Online, self-paced
• Prerequisites: None, basic database and SQL knowledge recommended

10. Complete Data Science Bootcamp 2024: Zero to Mastery

This comprehensive Zero to Mastery bootcamp covers a wide range of data science topics, including data engineering fundamentals, machine learning, and data visualization.

Key Modules and Learning Outcomes:

• Learn Python programming, data analysis libraries (NumPy, Pandas), and machine learning algorithms.
• Build data pipelines and work with databases.
• Create data visualizations and communicate insights effectively.

Format, Duration, and Prerequisites:

• Self-paced
• Over 200 lectures
• Prerequisites: No prior programming or data science experience required.

Pros:

• Covers a broad spectrum of data science topics, including data engineering basics.
• Suitable for beginners with no prior experience.
• Affordable price point.

Cons:

• May not provide the same depth in data engineering as specialized courses.
• Focuses more on data science concepts than advanced data engineering techniques.

Specific Benefits:

• Provides a solid foundation in data science and data engineering fundamentals.
• Learn from an experienced instructor with a passion for teaching.

At a glance:

• Duration: Over 200 lectures
• Cost: Varies (frequent discounts available)
• Format: Online, self-paced
• Prerequisites: None

Choosing the Right Data Engineering Course for Your Career Goals

With the multitude of online data engineering courses available, it's crucial to select one that aligns with your career aspirations and learning preferences. Here's a breakdown of some key factors to consider:

• Career Stage: Are you just starting or looking to advance in your data engineering career? Choose a course that caters to your current skill level and provides the knowledge needed for your next step.
• Desired Skills: Do you want to focus on specific tools and technologies, or are you looking for a broader understanding of data engineering principles? Select a course that covers the skills most relevant to your desired career path.
• Long-Term Goals: Do you aspire to become a data architect, machine learning engineer, or a data engineering specialist? Consider courses that offer advanced topics or specializations aligned with your long-term goals.
• Budget and Time: Evaluate the course cost and time commitment required. Choose an option that fits your budget and allows you to balance learning with your other responsibilities.
• Industry Applications: If you have a specific industry in mind, look for courses that offer case studies or projects related to that domain.

Conclusion

Data continues to grow in volume and complexity–the demand for skilled data engineers is only increasing. Investing in a robust data engineering course provides you with valuable skills and knowledge, and helps you position yourself for a successful and rewarding career.

Choosing the right course is a personal decision! Carefully consider your career goals, learning style, and budget before making your selection. We encourage you to explore the courses we've highlighted and read student reviews to get a better sense of their suitability for your needs.

The right course can be a cornerstone for your career development in data engineering. Embrace the learning journey, stay curious, and keep exploring the ever-evolving world of data.