Data Engineering using AWS Data Analytics
Udemy · Durga Viswanatha Raju Gadiraju · Updated
AI Tutor Rating
8.2/10
Duration
25.5 hours video
Classes
421
Build Data Engineering Pipelines on AWS using Data Analytics Services including Glue, EMR, Athena, Kinesis, Lambda, Redshift.
Data Engineering using AWS Data Analytics is a Udemy course taught by Durga Viswanatha Raju Gadiraju that walks learners through building production-grade data pipelines on Amazon Web Services. Spanning 25.5 hours across 421 lectures, the course covers the full AWS analytics stack, including Glue, EMR, Athena, Kinesis, Lambda, and Redshift. It targets developers and analysts who want to move from basic SQL and Python knowledge into hands-on data engineering roles, with a verifiable Udemy certificate awarded on completion.
What you'll learn in Data Engineering using AWS Data Analytics
Our Review of Data Engineering using AWS Data Analytics
Data Engineering using AWS Data Analytics is structured as a comprehensive, service-by-service tour of the AWS analytics ecosystem, and the 421-lecture count signals genuine breadth rather than a surface-level survey. The curriculum progresses logically, opening with foundational data engineering concepts before drilling into Glue techniques, advanced AWS concepts, and then branching into real-time streaming with Kinesis and big-data batch processing with Apache Spark on EMR. Dedicated chapters on Athena architecture, Lambda optimization, and Redshift in real-world scenarios round out the pipeline picture. That sequencing matters: a learner who completes the course should be able to design, build, and tune both batch ETL/ELT workflows and live streaming pipelines, which are the two dominant patterns in modern data engineering work.
The course's depth is its clearest editorial strength. Chapters like "Optimizing Lambda" and "Real-World Redshift" suggest the instructor moves beyond hello-world examples into the kind of performance and cost trade-offs that actually surface in production environments. The final project and assessment chapter is a meaningful differentiator at this price point, giving learners a concrete artifact to reference in job applications or portfolio reviews. At $14.99, the per-hour cost of instruction is exceptionally low, and the included certificate adds a lightweight credential that can be listed on a resume or LinkedIn profile while a learner builds toward more formal AWS certifications.
The main caveat is format dependency: 25.5 hours of video is the sole delivery mechanism listed, so learners who absorb material better through reading, interactive labs, or peer discussion will need to supplement externally. The prerequisite bar, basic SQL and Python, is also genuinely required rather than advisory; someone without that foundation will stall quickly once the course reaches Spark transformations or Lambda function logic. Neither limitation undercuts the course's value for its intended audience, but both are worth weighing before enrolling.
Pros and cons of Data Engineering using AWS Data Analytics
Pros
- Covers the full AWS analytics stack, including Glue, EMR, Athena, Kinesis, Lambda, and Redshift, in a single course
- 421 lectures across 25.5 hours provide genuine depth, including production-oriented topics like Lambda optimization and real-world Redshift usage
- Addresses both batch ETL/ELT and real-time streaming pipelines, reflecting the two core patterns in modern data engineering
- Final project and assessment chapter gives learners a portfolio artifact at a $14.99 price point
- Udemy certificate included, useful for resume and LinkedIn visibility while pursuing formal AWS credentials
Things to consider
- Video-only format offers no interactive labs or peer community listed, requiring learners to self-source hands-on AWS practice environments
- Requires genuine working knowledge of SQL and Python; learners without that foundation will struggle once Spark and Lambda content begins
- Breadth across so many AWS services means some topics may receive less depth than a dedicated single-service course would provide
Who should take Data Engineering using AWS Data Analytics?
Data Engineering using AWS Data Analytics is best suited for software developers, data analysts, or BI professionals who already write SQL and basic Python and want a structured, affordable path into AWS-based data engineering. It is particularly well matched to someone preparing for a data engineering role or AWS analytics certification who needs broad, practical exposure to Glue, Kinesis, Redshift, and EMR in one cohesive curriculum.
Course curriculum for Data Engineering using AWS Data Analytics
Data Engineering using AWS Data Analytics at a glance
| Provider | Udemy |
|---|---|
| Instructor | Durga Viswanatha Raju Gadiraju |
| Level | Advanced |
| Time to complete | 25.5 hours video |
| Pricing | $14.99 |
| Certificate | Certificate |
| Prerequisites | Basic SQL and Python |
Fit
Best for
Not ideal for
The bottom line on Data Engineering using AWS Data Analytics
Data Engineering using AWS Data Analytics delivers exceptional breadth and practical depth for its $14.99 price, covering the AWS analytics stack from streaming pipelines to big-data batch processing with a final project to show for it. The video-only format and real SQL and Python prerequisites are honest constraints, but for a motivated learner with that baseline, this course is a highly efficient on-ramp to production data engineering on AWS.
Data Engineering using AWS Data Analytics: frequently asked questions
What does Data Engineering using AWS Data Analytics actually teach you to build?
The course teaches you to build scalable ETL and ELT data pipelines, real-time streaming pipelines using Kinesis, and big-data processing workflows with Apache Spark on EMR. Specific AWS services covered include Glue, Athena, Lambda, Redshift, and EMR, giving you hands-on experience with the tools most commonly used in production AWS data engineering environments.
What prerequisites do you need before taking Data Engineering using AWS Data Analytics on Udemy?
The course requires basic SQL and Python knowledge. These are genuine prerequisites rather than suggestions; the curriculum moves into Spark transformations, Lambda function development, and Glue scripting relatively quickly, so learners without that foundation will find the material difficult to follow. No prior AWS experience is listed as required.
Is the $14.99 price for Data Engineering using AWS Data Analytics worth it, and does it include a certificate?
At $14.99 for 25.5 hours of instruction across 421 lectures, the per-hour cost is very low compared to most technical training options. A Udemy certificate is included upon completion, which can be added to a resume or LinkedIn profile. It is not an AWS-issued credential, but it serves as a useful signal while you build toward formal AWS certifications.
How does Data Engineering using AWS Data Analytics compare to taking individual AWS service courses for the same goal?
A single-service course on, say, Redshift or Kinesis will typically go deeper on that one tool, but Data Engineering using AWS Data Analytics covers the full analytics stack in one curriculum at a fraction of the combined cost. The trade-off is that some services may receive less exhaustive treatment, but the integrated pipeline perspective, connecting Glue, Kinesis, EMR, and Redshift, is harder to get from isolated courses.
How should you approach Data Engineering using AWS Data Analytics to get the most out of it?
Follow the curriculum sequentially since chapters build on each other, from foundational concepts through advanced streaming and batch patterns. Supplement the video lectures by running the demonstrated workflows in your own AWS account to build genuine hands-on experience. Treat the final project and assessment chapter as a portfolio piece, completing it thoroughly so you have a concrete example to discuss in technical interviews.