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The Data Engineering Course offered by SkillFloor is a comprehensive program designed to equip aspiring professionals with the skills and knowledge required to excel in the field of big data engineering. Through a blend of theoretical concepts and hands-on practical exercises, this online course delves deep into the intricacies of data engineering, covering topics such as data ingestion, storage, processing, and analysis. Participants will learn to harness the power of cutting-edge technologies and tools commonly used in the industry, ensuring they are well-prepared to tackle real-world challenges as data engineers. With a focus on practical applications and industry-relevant projects, SkillFloor's Data Engineering Course stands out as a top choice for individuals seeking to embark on a rewarding career path in this field. Explore the course today and take the first step towards becoming a proficient data engineer.

SkillFloor's Data Engineer Course is a premier online offering tailored for individuals aspiring to specialize in data engineering. This comprehensive program covers all aspects of data management and processing, providing participants with the necessary skills to thrive in the current informative environment. By enrolling in this course, students gain access to a wealth of resources and hands-on learning opportunities, allowing them to master key concepts such as data warehousing, ETL (Extract, Transform, Load) processes, and distributed computing. With a curriculum designed by industry experts and instructors with extensive experience, SkillFloor's Big Data Engineer Course is an ideal choice for anyone looking to enhance their expertise and advance their career in the field of data engineering. 




Skill Level



1-month Unpaid

Live Project




Live Training


Career Assistance


Expiry Period

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Course Highlights

  • Introduction to Data Engineering: An overview of the data engineering field, including its importance and how it fits within the broader context of data analysis and data science.

  • Data Modeling Concepts: Basic principles of data modelling, including the creation and management of databases, schema design, and normalization processes.

  • SQL and NoSQL Databases: Instruction on both traditional SQL databases for structured data and NoSQL databases for unstructured data, emphasizing when and how to use each.

  • ETL Processes: Understanding of Extract, Transform, and Load (ETL) processes, including how to efficiently gather data from multiple sources, clean it, and prepare it for analysis.

  • Big Data Technologies: An introduction to the tools and technologies used to process and analyze big data, such as Hadoop and Spark.

  • Cloud Data Services: Exploration of cloud-based data engineering services offered by platforms like AWS, Google Cloud, and Microsoft Azure, and how to leverage them for scalable data solutions.

  • Data Pipelines and Workflows: Techniques for building and managing automated data pipelines that ensure the smooth flow of data from source to destination, including workflow orchestration tools.

  • Data Security and Compliance: Fundamental concepts in securing data and ensuring compliance with legal and regulatory requirements, such as GDPR.

  • Real-world Projects: Hands-on projects that mimic real-world data engineering problems, allowing learners to apply the concepts and tools they have learned.

  • Preparation for Certification: Guidance and resources to help participants prepare for and complete the certification exam, affirming their knowledge and skills in data engineering.



Getting the Certified Data Engineering Associate certificate means you're really good at dealing with data. It's like a badge saying, "Hey, I know how to design, build, and take care of systems that handle lots of information.People who earn this certificate learn important things like managing databases, organizing data, and ensuring big sets of information are processed and analyzed well. They also get familiar with using cloud-based solutions for data.

This certificate is a big deal because it proves that someone is committed to staying up-to-date with the latest in data engineering. When someone completes this program, it means they know the basics of data engineering really well. This makes them valuable in the world of technology that relies on data.So, having the Certified Data Engineering Associate badge is like saying, "I know my stuff when it comes to handling data in today's tech world." It's a recognized mark that shows someone is skilled in the important principles of data engineering.

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Top 10 reasons

  1. The Basics: Understand key concepts like data modeling, ETL processes, and managing databases to build a strong foundation.

  2. Hands-on Learning: Apply what you learn in real-world projects, ensuring you can use your knowledge in a job setting.

  3. Tools of the Trade: Get familiar with the latest tools and technologies used in data engineering, including popular platforms and programming languages.

  4. Data Warehousing: Learn how to design and implement efficient data storage solutions.

  5. Big Data Integration: Master handling large volumes of data in modern business environments with the integration of big data technologies.

  6. Data Quality and Governance: Discover the importance of maintaining clean and reliable data and learn strategies for doing so.

  7. Cloud Computing: Explore how cloud platforms can be used for scalable and flexible data processing.

  8. Group Projects: Work with your peers on real-world projects to improve teamwork and problem-solving skills.

  9. Certification Exam Prep: Get ready for the certification exam to showcase your data engineering skills.

  10. Career Support: Receive guidance, industry insights, and networking opportunities to kickstart or advance your data engineering career.


  •  Overview of Data Engineering and its role in the tech industry

  •  The difference between Data Engineering, Data Science, and Data Analysis

  •  Introduction to the Data Engineering Ecosystem


  •  Understanding Data Warehouses and Data Lakes

  •  Design principles of Data Warehousing

  •  ETL (Extract, Transform, Load) processes


  •  Introduction to SQL and NoSQL databases

  •  Data modeling and database design

  •  Managing and optimizing databases for performance

  •  Overview of Big Data and Hadoop Ecosystem

  •  Introduction to distributed data processing frameworks like Apache Spark

  •  Working with real-time data processing technologies


  •  Designing and building scalable data pipelines

  •  Data ingestion tools and techniques

  •  Batch vs. streaming data processing

  •  Ensuring data quality and integrity in data pipelines

  •  Data governance policies and compliance (e.g., GDPR, CCPA)

  •  Introduction to data security and privacy measures


  •  Cloud computing fundamentals

  •  Overview of cloud data services (AWS, Google Cloud Platform, Azure)

  •  Building and managing data solutions in the cloud


  •  Introduction to machine learning models for data engineers

  •  Containerization and orchestration with Docker and Kubernetes

  •  Monitoring, logging, and operationalizing data pipelines



A certified data engineering associate is someone who has completed a recognized certification program in data engineering, demonstrating their ability to design, build, and maintain data pipelines and infrastructure.

Certification in data engineering validates your skills, enhances your credibility, and increases your job prospects in fields where managing and processing data efficiently is crucial.


To get certified, you typically need to complete specific training courses, gain practical experience with data engineering tools and technologies, and pass an exam that tests your knowledge of data engineering concepts.

Skills include understanding of databases, proficiency in programming languages like SQL and Python, knowledge of data warehousing and ETL processes, and experience with cloud platforms like AWS or Azure.


With certification, you can pursue roles such as data engineer, ETL developer, database administrator, or cloud engineer in various industries.


It varies but typically takes several months to a year of study and practice to get certified.

It can lead to better job prospects, higher salaries, and opportunities for career advancement in the field of data engineering.


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