7 Best Data Engineering Courses for Cloud Professionals

Becoming a data engineer can be challenging, but we are here to make the journey easier. In this blog, we have curated a list of the best data engineering courses so you can master this challenging field with confidence. Say goodbye to confusion and hello to a clear path to data engineering expertise!

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“We are moving slowly into an era where Big Data is the starting point, not the end.”— Pearl Zhu

Big data is changing how businesses function effectively, thus creating a need for data engineers who can collect and manage large volumes of data. Data engineers build the necessary data infrastructure for data scientists and data analysts to work with data. Currently, there are over 73K data engineer jobs in the US and over 21K in India, indicating the high demand for data engineers. But how will you stand out from the competitors? How will you gain the essential skills to jumpstart or advance your career in this domain? The answer lies in pursuing top data engineering courses! This blog discusses the top seven data engineering courses that will help you build a rewarding career in this field. It also sheds light on the key benefits of taking these courses and the critical factors to remember while picking the right ones. So, let us help you transform your cloud career with the power of data engineering!

Why Must Professionals Pursue Data Engineering Courses?

Data engineering courses offer significant advantages for professionals, including data scientists, data analysts, and data engineers. etc., enhancing their skills and career prospects in cloud-based data management. Here are four key benefits of pursuing these courses-

1. Enhanced Cloud Data Management Skills

Data engineering courses provide cloud professionals or data engineers with the knowledge and skills to effectively manage data in cloud environments. For example, a cloud architect might enroll in a data engineering course to learn how to design and implement data pipelines using cloud services. Gaining such expertise can streamline data processing, ensuring data is readily available for analytics and decision-making. 

Suppose a cloud professional takes a course focusing on using AWS Glue and Apache Spark for ETL (Extract, Transform, Load) processes. With this knowledge, they can design efficient data pipelines, automate data transformation, and ensure data quality and accuracy, which benefits their organization's data-driven initiatives.

Data engineering courses also teach data engineers how to leverage cloud resources for scalable data solutions while optimizing costs. Suppose a cloud data engineer completes a course that covers Google Cloud BigQuery and its cost-effective pricing model. They apply this knowledge to build data warehouses and reconfigure their company's data warehousing solution, optimizing costs by dynamically scaling resources based on workload demands.

Furthermore, data engineering courses enable cloud data engineers to integrate data seamlessly across various cloud platforms and data sources. This skill is highly valuable while working in multi-cloud or hybrid-cloud environments. Suppose a cloud solutions architect takes a course with hands-on experience with Azure Data Factory and AWS Lambda functions. By gaining these skills, they can design data pipelines that collect and store data from Azure and AWS sources, enabling seamless cross-platform data integration for their organization.

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2. Better Team Collaboration Skills

Data engineering courses emphasize teamwork and collaboration in data projects, helping professionals communicate effectively with cross-functional teams, including data scientists and analysts. Suppose a data engineer undertakes a data engineering course, which includes group projects involving data scientists. This experience facilitates effective collaboration, enabling the engineer to work seamlessly with data science teams to implement cloud data solutions.

3. Career Growth And Advancement

Data engineering courses offer cloud data engineers a pathway to career growth and advancement in data engineering and cloud technology. By acquiring in-demand data engineering skills, these cloud data engineers can become more valuable to their organizations, further opening up opportunities for leadership roles and higher-paying positions.

For instance, suppose a cloud administrator completes a course, gaining expertise in designing and managing AWS data pipelines. With this additional skill set, they become a suitable candidate for a promotion to team lead, where they can lead data-based projects and mentor junior team members, contributing to their career advancement.

7 Best Online Data Engineering Courses Worth Exploring in 2023

This section will list seven top data engineering courses you must consider pursuing to build a successful career in this domain. The courses have been categorized based on the level of expertise, such as beginners, intermediate, and advanced. Let us look at some of the best data engineering courses for beginners.

Best Data Engineering Courses For Beginners

Let us begin with some of the best data engineering courses online that are suitable for beginner-level professionals or college graduates looking for entry-level roles in this domain.

1. IBM: Data Engineering Foundations by Coursera

The first course in our list is IBM's specialized course, comprised of five flexible modules, which is your gateway to mastering essential data engineering skills. These modules give you a comprehensive introduction to the complete data engineering ecosystem and lifecycle. Through engaging video content and hands-on practice using various tools and real-world databases, you will grasp data engineering fundamentals and acquire skills directly applicable to a data engineer role. By completing this course, you will acquire practical knowledge and experience, enabling you to dive deeper into this domain and work on more advanced projects. 

Each module of this course involves multiple hands-on labs and assignments, helping you gain practical expertise. From working with raw data in various formats to the complex processes of transforming and loading data into a central repository and conducting in-depth data analysis using SQL and advanced techniques, you will explore a wide range of real-world databases and tools. Get ready to explore MySQL, PostgreSQL, IBM Db2, IBM Cloud, Python, Jupyter Notebooks, Watson Studio, and more- all in this Specialization course.

Ideal For

Since it is a beginner-level course, it is suitable for anyone with basic Computer & IT knowledge and working experience in one or more Operating Systems. You can take this course without any prior domain knowledge or experience.

Duration

The duration of this self-paced course will be nearly two months at ten hours a week.

Ratings/Reviews

This course has an overall rating of 4.7 stars and 1,004 reviews.

Data Engineering Project You Must Explore

Once you have completed this fundamental course, you must try working on the Hadoop Project to Perform Hive Analytics using SQL and Scala to help you brush up your skills.

Expert Overview of The ‘IBM Data Engineering Foundation Specialization Course’

Luis Oliveira, Co-Founder of Newbie to Proficient, shares an overview of the IBM Data Engineering Foundation Specialization course in one of his articles-

2. Data Engineering Zoomcamp By GitHub

The next course is the Data Engineering ZoomCamp, organized by DataTalks, a leading global hub for data enthusiasts and industry experts. You must take this GitHub course if you are looking for the best free data engineering courses online. This self-paced program is designed to help you learn the essential fundamental concepts of data engineering. This course also transforms the learning experience by integrating the collaborative power of Slack, Telegram, and YouTube. In this course, you can expect ongoing support and access to free resources to enhance your learning journey.

Over the course of several weeks, you will dive deeper into the world of data engineering, gaining hands-on experience with a diverse toolkit. You will explore tools like Docker, Airflow, Spark, and the Google Cloud Platform, acquiring practical skills highly valued in the industry. The final three weeks of this program will be dedicated to a capstone project where you can put your newly gained knowledge into action and build a real-world data engineering solution. But that's not all - your project will undergo peer review, offering you invaluable feedback and insights from your peers.

Ideal For

This course is suitable for anyone with a solid foundation in coding, command line usage, data systems, and a basic understanding of SQL. While prior experience with Python programming is beneficial, learning Python is relatively easier if you are familiar with other programming languages. Domain experience isn't a prerequisite, but it's worth noting that from the very start of the program, you will dive into advanced topics such as Google Cloud Platform, data collection and ingestion, batch and stream processing, analytics engineering, coding proficiency will be beneficial to help you confidently work in these complex areas.

Duration

The duration of this self-paced course will be nine weeks.

Ratings/Reviews

This course has almost 15k stars and around 80 contributors on GitHub.

Data Engineering Project You Must Explore

Once you have completed this fundamental course, you must try working on the End-to-End ML Model Monitoring using Airflow and Docker to help you brush up your skills.

3. IBM Data Engineering Professional Certificate by Coursera

This is one of the best data engineer certificate programs by IBM, encompassing an array of core data engineering concepts, ensuring you are well-prepared for entry-level roles in this field. With 13 flexible self-paced courses, this program covers Python and SQL fundamentals essential for data analysis and other database interaction. The best part is- you won't merely learn; you will apply your knowledge through hands-on projects and lab simulations, gaining practical expertise with Python libraries, Bash, Apache Spark, ETL tools, and Relational Database Management Systems (RDBMS). Apart from Python, you will master several highly demanded skills such as SQL, RDBMS, ETL, Data Warehousing, NoSQL, and Spark, all supported by interactive labs and projects. This program enables you to use Python programming and Linux/UNIX shell scripts for ETL data processes, explore Relational Databases using SQL queries, and manage NoSQL data models, databases, and unstructured data. 

Throughout the program, you will build a rich portfolio of projects, earning a Professional Certificate in data engineering and a Digital badge from IBM. Moreover, IBM offers valuable career resources, including mock interviews and resume support, supporting your job search.

Ideal For

Since this course starts with the basics, it is appropriate for individuals without any prior experience in programming or data engineering. 

Duration

The duration of this self-paced course will be five months (ten hours a week).

Ratings/Reviews

This certificate program has an overall rating of 4.6 stars and 3,435 reviews.

Data Engineering Project You Must Explore

Once you have completed this fundamental course, you must try working on the PySpark ETL Project-Build a Data Pipeline using S3 and MySQL to help you brush up your skills.

Best Data Engineering Programs For Intermediate

This section will list some of the best data engineering courses ideal for intermediate-level data engineers, data scientists, or data analysts willing to brush up their skills and expertise or move up the career ladder in the industry.

4. Data Engineering with AWS- Nanodegree Program by Udacity

This comprehensive online program focuses on the advanced aspects of data models, data warehouses, data lakes, and overall data architecture, equipping you with the skills needed to excel in data engineering. From automating complex data pipelines to adeptly handling extensive databases, including Amazon Web Services (AWS), this course enables data engineers to explore modern data management. You will gain proficiency in essential tools like PostgreSQL, Apache Spark, and many others, enabling you to solve real-world data engineering challenges. You will undertake a capstone project tailored to your interests at the end of your learning journey. 

Furthermore, the supportive Udacity Data Engineer community offers invaluable career coaching, including guidance on optimizing your LinkedIn profile and building an impressive data engineering portfolio to jumpstart your career. Additionally, you will have access to an active student community, providing a platform for resolving doubts and seeking clarifications to enhance your learning experience further.

Ideal For

This course is suitable for learners with a solid foundation in various key areas, such as those with intermediate-level Python programming and SQL skills. A basic understanding of command line interfaces, familiarity with GitHub, a grasp of Amazon Web Services fundamentals, and a working knowledge of relational databases, relational data models, database fundamentals, and data modelling basics will ensure a smooth and enriching learning experience.

Duration

The duration of this self-paced course will be four months.

Ratings/Reviews

This certificate program has an overall rating of 4.6 stars and 1198 reviews.

Data Engineering Project You Must Explore

Once you have completed this fundamental course, you must try working on the Build a Data Pipeline in AWS using NiFi, Spark, and ELK Stack to help you brush up your skills.

Expert Outlook on Udacity Data Engineering Nanodegree Programs

Antonello Benedetto, Lead Data Engineer at Wise, shares a study report relevant to the Udacity Nanodegree programs in one of his articles-

5. Data Engineering Essentials using SQL, Python, and PySpark by Udemy

This intermediate-level Udemy course helps you dive into the Data Engineering fundamentals, equipping you with the skills to build a scalable and efficient data pipeline. You will leverage the power of SQL and Python, focusing on leveraging technologies like Hadoop, Hive, and Spark SQL, along with the PySpark DataFrame APIs. Additionally, you will gain insights into the development and deployment life cycle of Python applications, incorporating Docker and PySpark on multi-node clusters for seamless execution. The course also provides essential knowledge on reviewing Spark Jobs using Spark UI. Furthermore, you will master the database essentials, employing PostgreSQL to create tables, implement indexes, execute SQL queries, and use important pre-defined functions, all to enhance your data engineering skill set. Upon completing this course successfully, you will achieve a certificate of completion to add to your LinkedIn profile and resume.

Ideal For

This course is an excellent fit for Computer Science and IT students and graduates interested in IT. Additionally, Data Warehouse Developers aiming to transition into data engineering roles, ETL Developers seeking new opportunities, and Database or PL/SQL Developers looking to dive into this domain will find this course helpful. Furthermore, Business Intelligence Developers willing to explore the world of Data Engineering, QA Engineers who want to enhance their fundamental knowledge in this domain, and Application Developers seeking to acquire data engineering skills will all benefit from this intermediate-level course.

Duration

This course includes 56 hours of on-demand video and two articles, which you can access on mobile and TV anytime.

Ratings/Reviews

This certificate program has an overall rating of 4.3 stars and 2,968 ratings, along with 57,540 students enrolled in it.

Data Engineering Project You Must Explore

Once you have completed this intermediate-level course, you must try working on the PySpark Project-Build a Data Pipeline using Hive and Cassandra to help you brush up your skills.

Best Data Engineer Training Courses For Advanced

This section will list some of the best data engineering courses ideal for advanced-level professionals who already possess solid foundation and expertise and are looking for further higher-level job roles in data engineering.

6. Data Engineering with Microsoft Azure- Nanodegree Program by Udacity

In this Nanodegree program, you will master the art of designing data models, building cloud data warehouses, and designing data lakes and data architecture. With a strong focus on hands-on experience, you will become proficient in creating data pipelines and handling extensive datasets within the Azure ecosystem. You will further discover the power of Azure Synapse Analytics, Azure Databricks, and Azure Data Factory as you proceed in the course. This course will teach you to use ETL techniques to build PostgreSQL and Apache Cassandra databases. You will also gain the expertise needed to build cloud-based data warehouses, honing your data warehousing skills and deepening your understanding of data architecture. You will further explore cloud data engineering with Azure by building, orchestrating, automating, and monitoring data pipelines using Azure Data Factory and Azure Synapse Analytics. To conclude your learning journey, you will work on an exciting capstone data engineering project to practically apply your newly acquired knowledge. Wait, there’s more-  the Udacity Data Engineer community will also support you, offering valuable guidance on optimizing your LinkedIn profile and building an exceptional data engineering portfolio to help you succeed in your career path.

Ideal For

This course is tailored for individuals possessing foundational skills, making it an ideal fit for those with intermediate-level Python programming and SQL proficiency. Moreover, a fundamental grasp of SQL, relational data models, basic knowledge of GitHub, familiarity with Azure basics, command line interface essentials, a basic understanding of Python, and a working knowledge of relational databases will set you on the right path for a successful learning experience with this online program.

Duration

The duration of this self-paced course will be four months.

Ratings/Reviews

This certificate program has an overall rating of 4.3 stars.

Data Engineering Project You Must Explore

Once you have completed this advanced-level course, you must try working on the Azure Data Factory and Databricks End-to-End Project to help you practically implement the skills and knowledge gained in this program.

7. Preparing for your Professional Data Engineer Journey By Coursera

One of the best data engineering certifications is the Professional Data Engineer (PDE) Certification by the Google Cloud Platform. This advanced-level program is your comprehensive guide to preparing for the PDE certification exam, helping you design a suitable study plan. This Google Cloud certificate program encompasses a broad spectrum, focusing on big data analytics and machine learning within the Google Cloud platform. It allows you to work on big data projects, equipping you with the expertise to use Google's Cloud SQL and data processing tools. Throughout this course, you will gain insights into the role of a data engineer in a retail organization. You will master crucial skills such as cloud computing, data migration, data processing, workload automation, and several other aspects of the data engineering domain.

Ideal For

This course is ideal for individuals with prior experience in SQL and a foundational understanding of machine learning algorithms and Python programming. It is important to note that the course primarily focuses on data engineering within the framework of Google Cloud, so you must possess at least six months of experience in cloud computing and data engineering. Also, this course is an excellent choice if you want to earn Google Cloud’s certificate in data engineering, as the comprehensive content across seven modules is designed to help you excel in the GCP Professional Data Engineer exam. Whether you want to develop scalable applications on the Google Cloud platform or enhance your machine learning credentials, this course is a valuable asset on your learning journey.

Duration

The duration of this self-paced course will be one to three months.

Ratings/Reviews

This certificate program has an overall rating of 4.6 stars.

Data Engineering Project You Must Explore

Once you have completed this advanced-level course, you must try working on the GCP Project-Build Pipeline using Dataflow Apache Beam Python to help you practically apply the skills and knowledge gained in this program.

Factors To Consider The Right Data Engineering Online Courses

Choosing the right data engineering course requires careful consideration of several key factors to ensure the course aligns with your career aspirations. Some of the factors you must consider while selecting a data engineering course include-

1. Career Goals

Begin by defining your career path and objectives clearly. Are you aiming to become a data engineer, data analyst, or data scientist? Your career goals will determine your desired depth and specialization in your selected course.

2. Course Content And Syllabus

The next factor involves examining the course syllabus closely. You must ensure it covers essential data engineering topics like data modeling, ETL (Extract, Transform, Load) processes, data warehousing, cloud platforms (e.g., AWS, Azure, Google Cloud), machine learning algorithms/models, big data technologies (e.g., Hadoop, Spark), and databases (e.g., SQL, NoSQL). It should align with your specific learning requirements and career goals.

3. Instructor Credibility

You must assess the instructor's qualifications and industry experience. An instructor with a strong background in data engineering or a related field brings practical insights and real-world relevance to the course. You must look for instructors with industry certifications or those who have worked on industry-level data engineering projects.

4. Course Reviews And Ratings

You must dig into reviews and ratings provided by previous learners/students. These insights can offer a glimpse into the course's quality, instructional style, and whether it meets the learners' expectations. Don’t forget to consider positive and negative feedback before making any decision.

5. Pricing And Financial Considerations

You must evaluate the course's cost relative to your budget. Some platforms offer free courses or financial aid options, while others require payment upfront. You must remember that investing in your education is an investment in your career, but finding a course that offers value for your money is essential.

6. Course Schedule And Flexibility

Another important factor to consider is your availability and preferred learning pace. Some courses are self-paced, allowing you to study on your schedule, while others have fixed start and end dates. You must select a course with a structure that aligns with your lifestyle and availability.

7. Certification And Recognition

It is crucial to determine whether the course provides certification upon completion and whether it is recognized in your industry or region. A reputable certification can make your resume shine bright and better than others and highlight your expertise to potential employers.

You must also consider factors such as the availability of hands-on projects, community support, and career resources like job placement assistance and networking opportunities. By evaluating these factors carefully, you can make a better-informed decision and select a course that aligns with your goals and helps you gain the skills and knowledge crucial for a successful data engineering career.

Excel Your Data Engineering Career With ProjectPro

In the data engineering domain, knowledge is your compass, but experience is your map. The courses discussed in this blog provide a roadmap to your professional growth, equipping you with the knowledge and expertise to excel in this field. But remember- your journey doesn't end with learning; it's just beginning. To truly master data engineering, you need hands-on experience. Over 270 industry-level end-to-end solved projects by ProjectPro let you apply your course knowledge to solve real-world challenges, turning essential concepts into effective solutions. Oh wait, there’s more! You will also gain access to free guided project videos to help you better understand the project solutions and a Big Data Tools Analyzer to help you pick the right tools for your data engineering projects.

So, take the courses, but don't stop there. Dive into these projects from the ProjectPro repository and build and automate data pipelines, tackle complex tasks, and solve real-world problems to thrive in the data engineering domain. Your journey to becoming a data engineering pro awaits!

FAQs on Data Engineering Courses

1. How to learn data engineering?

For learning data engineering, you must follow the steps below-

• Start with foundational courses in SQL, Python, and databases. 

• Then, explore specialized data engineering courses and certifications online. 

• Learn how to build data pipelines, work with big data tools, and master cloud platforms like AWS, Azure, or Google Cloud. 

• You must join data engineering communities, engage in real-world projects by GitHub and ProjectPro, and stay updated with industry trends to enhance your skills.

2. Where to learn data engineering?

You can learn data engineering from various online platforms such as Coursera, Udacity, Udemy, and LinkedIn Learning, which offer comprehensive courses and certifications. Cloud providers like AWS, Azure, and Google Cloud also offer specific data engineering training. Finally, don't forget to explore free resources like YouTube tutorials and online platforms like GitHub and ProjectPro for practical learning.

3. What is a data engineering course?

A data engineering course is an educational program that teaches individuals how to design, build, and manage data pipelines and infrastructure. These courses cover data modeling, ETL (Extract, Transform, Load) processes, database management, and big data technologies. They are designed to equip learners with the skills to effectively work with data and support data-specific decision-making in various industries.

4. Is it hard to become a data engineer?

No, it is not hard to become a data engineer. However, it can seem challenging initially, as it requires a strong grasp of programming languages like SQL and Python, database systems, big data technologies, and cloud platforms. But, with dedication, education, and hands-on experience, it's achievable for those passionate about data and technology.

About the Author

Daivi

Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

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