Data Engineering for Beginners: Learn SQL, Python & Spark
Udemy · Durga Viswanatha Raju Gadiraju · Updated
AI Tutor Rating
8.2/10
Duration
56 hours video
Classes
623
Master SQL, Python, and Apache Spark (PySpark) with Hands-On Projects using Databricks on Google Cloud.
Data Engineering for Beginners: Learn SQL, Python & Spark is a Udemy course taught by Durga Viswanatha Raju Gadiraju that covers the full beginner-to-practitioner stack of modern data engineering. Across 623 lectures and 56 hours of video, learners work through SQL, Python, Apache Spark, PySpark, Databricks, and Google Cloud Platform. The course targets absolute beginners, lists no prerequisites, and culminates in hands-on projects that simulate real-world ETL and ELT pipeline construction on cloud infrastructure, making it one of the most comprehensive entry-level data engineering programs available on the platform.
What you'll learn in Data Engineering for Beginners: Learn SQL, Python & Spark
Our Review of Data Engineering for Beginners: Learn SQL, Python & Spark
Data Engineering for Beginners: Learn SQL, Python & Spark is structured as a genuine ground-up curriculum rather than a highlight reel of buzzwords. The chapter progression moves logically from foundational SQL and Python through PySpark mechanics, Databricks optimization, and GCP integration, before landing in applied, real-world data engineering scenarios. With 623 lectures spread over 56 hours, the course has the density of a part-time bootcamp, which is both a strength and a commitment signal. Learners who work through the full sequence will have touched every major layer of a modern data stack: query languages, a general-purpose scripting language, a distributed compute framework, a managed lakehouse platform, and a major cloud provider.
The curriculum chapters titled "PySpark in Practice," "Scalable ETL/ELT Pipelines," and "Working with GCP" suggest that the course does not stop at conceptual introductions. The inclusion of a "Python Best Practices" module and an "Optimizing Databricks" section implies that the instructor pushes learners past syntax familiarity toward production-aware thinking, which is rare at this price point. The "Putting It All Together" capstone chapter is a strong architectural choice because it forces learners to connect isolated skills into a coherent pipeline, which is exactly the kind of integrative exercise that prepares someone for a junior data engineering role.
At $15.99, Data Engineering for Beginners: Learn SQL, Python & Spark delivers exceptional cost-per-hour value, and the included Udemy certificate of completion adds a lightweight but real credential for a resume or LinkedIn profile. The certificate will not substitute for a degree or professional certification, but at this price it costs almost nothing to obtain and signals demonstrated effort to hiring managers scanning entry-level applicants. The main value risk is the sheer volume of content: learners who are not self-disciplined may stall before reaching the cloud and pipeline chapters where the most employable skills live.
Pros and cons of Data Engineering for Beginners: Learn SQL, Python & Spark
Pros
- No prerequisites make Data Engineering for Beginners: Learn SQL, Python & Spark genuinely accessible to career-changers and students with zero prior experience.
- 56 hours across 623 lectures provides bootcamp-level depth at a fraction of the cost, covering SQL, Python, PySpark, Databricks, and GCP in one course.
- Hands-on projects using Databricks on Google Cloud expose learners to real cloud infrastructure rather than purely local or simulated environments.
- A dedicated 'Optimizing Databricks' chapter and 'Python Best Practices' module push beyond beginner syntax into production-relevant habits.
- A Udemy certificate of completion is included, giving learners a shareable credential for under $16.
Things to consider
- At 56 hours, Data Engineering for Beginners: Learn SQL, Python & Spark demands significant self-discipline; learners who struggle with long self-paced courses may not reach the most valuable pipeline and cloud modules.
- The Udemy certificate carries no accreditation or industry-body recognition, so it supplements but does not replace credentials like AWS, GCP, or Databricks certifications for competitive job applications.
- The course covers a very broad stack, meaning individual topics such as advanced SQL tuning or deep Python software engineering receive less focused treatment than a single-subject course would provide.
Who should take Data Engineering for Beginners: Learn SQL, Python & Spark?
Data Engineering for Beginners: Learn SQL, Python & Spark is best for career-changers, recent graduates, or analysts who want a single, structured path into data engineering without prior coding or database experience. It suits learners who can commit several weeks of consistent study and want practical exposure to the exact tools, specifically Databricks, PySpark, and GCP, that appear most frequently in entry-level and junior data engineering job descriptions.
Course curriculum for Data Engineering for Beginners: Learn SQL, Python & Spark
Data Engineering for Beginners: Learn SQL, Python & Spark at a glance
| Provider | Udemy |
|---|---|
| Instructor | Durga Viswanatha Raju Gadiraju |
| Level | Beginner |
| Time to complete | 56 hours video |
| Pricing | $15.99 |
| Certificate | Certificate |
| Prerequisites | None |
Fit
Best for
Not ideal for
The bottom line on Data Engineering for Beginners: Learn SQL, Python & Spark
Data Engineering for Beginners: Learn SQL, Python & Spark is a rare course that earns its 'beginner' label while still delivering cloud-native, production-oriented skills. The $15.99 price makes the risk of enrollment negligible, and the 56-hour curriculum is broad enough to serve as a genuine career foundation. Learners who commit to the full sequence will finish with a working understanding of the modern data engineering stack and a portfolio of hands-on Databricks projects to show for it.
Data Engineering for Beginners: Learn SQL, Python & Spark: frequently asked questions
What does Data Engineering for Beginners: Learn SQL, Python & Spark actually teach you to build?
The course teaches learners to build scalable ETL and ELT data pipelines using Apache Spark and PySpark, run on Databricks hosted on Google Cloud Platform. Hands-on projects are integrated throughout, so learners practice constructing and optimizing real pipelines rather than only studying concepts. By the final capstone module, students connect SQL, Python, and Spark skills into a cohesive data engineering workflow.
Do you need any prior experience to take Data Engineering for Beginners: Learn SQL, Python & Spark on Udemy?
No prior experience is required. The course lists no prerequisites, making it suitable for complete beginners who have never written SQL or Python before. The curriculum is designed to build from foundational concepts upward, so learners do not need a programming background, a data background, or prior cloud experience to start and progress through the material.
Is the certificate from Data Engineering for Beginners: Learn SQL, Python & Spark worth anything for job applications?
The course includes a Udemy certificate of completion, which is a lightweight but real credential. It is not accredited and does not carry the weight of a vendor certification from Google or Databricks. However, at a $15.99 course price, it costs almost nothing to earn and can signal demonstrated initiative on a resume or LinkedIn profile when targeting entry-level data engineering roles.
How does Data Engineering for Beginners: Learn SQL, Python & Spark compare to taking separate courses in SQL, Python, and Spark?
Taking three separate courses would likely cost more and require learners to self-integrate the skills. Data Engineering for Beginners: Learn SQL, Python & Spark bundles all three alongside Databricks and GCP into a single structured sequence with a capstone project that connects them. The trade-off is that each individual topic receives less depth than a dedicated single-subject course, but the integrated pipeline context is more directly job-relevant for a data engineering career path.
What is the best way to get the most out of Data Engineering for Beginners: Learn SQL, Python & Spark given its 56-hour length?
Set a consistent daily or weekly schedule and prioritize completing the hands-on project sections rather than passively watching lectures. The 'Optimizing Databricks,' 'PySpark in Practice,' and 'Putting It All Together' chapters contain the most employable skills, so treat earlier modules as preparation for those. Actively coding along in Databricks on GCP rather than just watching will reinforce retention and give you portfolio artifacts to reference in job interviews.