Master Data Engineering using GCP Data Analytics
Udemy · Durga Viswanatha Raju Gadiraju · Updated
AI Tutor Rating
8.2/10
Duration
19.5 hours video
Classes
283
Learn GCS for Data Lake, BigQuery for Data Warehouse, GCP Dataproc and Databricks for Big Data Pipelines.
Master Data Engineering using GCP Data Analytics on Udemy is a comprehensive, nearly 20-hour video course taught by Durga Viswanatha Raju Gadiraju. It provides practitioner-level training focused on building data solutions on Google Cloud Platform. The curriculum covers designing data warehouses and data lakes with BigQuery and GCS, building big data pipelines using Dataproc and Databricks, and deploying machine learning workloads. This course serves data engineers, analysts, and software developers seeking to gain hands-on, production-oriented skills in the GCP data analytics ecosystem, requiring only basic SQL as a foundation.
What you'll learn in Master Data Engineering using GCP Data Analytics
Our Review of Master Data Engineering using GCP Data Analytics
The structure of Master Data Engineering using GCP Data Analytics is ambitious, organized into twelve distinct chapters that progress from foundational concepts to advanced optimization and troubleshooting. With 283 lectures packed into 19.5 hours, the density suggests a fast-paced, detail-oriented format. The teaching is delivered entirely through video, which for a technical subject like cloud data engineering relies heavily on the instructor's ability to demonstrate live console work and architecture diagrams effectively. The curriculum promises a practitioner's skill set, moving beyond theory to actionable outcomes like deploying ML workloads and building pipelines with specific services like Dataproc.
The outcomes and curriculum suggest a learner who completes this course will be able to design and implement core GCP data infrastructure components, specifically data lakes and warehouses, and orchestrate data movement and processing at scale. The inclusion of chapters on tools, troubleshooting, and best practices indicates a focus on real-world application. At its promotional price of $14.99, the course offers significant volume of content for the cost, and the included certificate of completion adds a tangible credential for professional profiles, though it is not a formal industry certification. The value proposition is strong for self-motivated learners who can absorb technical material through a video-first format.
Pros and cons of Master Data Engineering using GCP Data Analytics
Pros
- Comprehensive curriculum covering major GCP data services like BigQuery, Dataproc, and Databricks
- High volume of content with 19.5 hours of video across 283 lectures
- Clear, production-oriented learning outcomes focused on deployment and pipeline building
- Includes a certificate of completion for professional development
- Accessible pricing model typical of the Udemy platform
Things to consider
- Requires a foundational knowledge of SQL, which may be a barrier for absolute beginners
- Solely video-based format may not suit all learning styles for complex technical topics
- The fast pace implied by the lecture count could be challenging without hands-on practice alongside the videos
Who should take Master Data Engineering using GCP Data Analytics?
This course is best for data professionals, such as engineers or analysts with basic SQL knowledge, who need to quickly gain practical, hands-on skills for implementing data solutions on Google Cloud Platform. It fits those aiming to design data warehouses, build big data pipelines with Dataproc, and deploy ML workloads, preferring a structured, video-led learning path at an accessible price point.
Course curriculum for Master Data Engineering using GCP Data Analytics
Master Data Engineering using GCP Data Analytics at a glance
| Provider | Udemy |
|---|---|
| Instructor | Durga Viswanatha Raju Gadiraju |
| Level | Advanced |
| Time to complete | 19.5 hours video |
| Pricing | $14.99 |
| Certificate | Certificate |
| Prerequisites | Basic SQL |
Fit
Best for
Not ideal for
The bottom line on Master Data Engineering using GCP Data Analytics
Master Data Engineering using GCP Data Analytics delivers a substantial, focused curriculum on Google's data stack at a very competitive price. It is a strong option for SQL-proficient learners seeking a project-ready understanding of GCP data services, though success depends on the ability to learn effectively from a dense, video-only format and to supplement with personal practice.
Master Data Engineering using GCP Data Analytics: frequently asked questions
What exactly will I learn to build in the Master Data Engineering using GCP Data Analytics course?
You will learn to build practical data solutions on Google Cloud, including designing data warehouses and data lakes on GCP, constructing big data pipelines using GCP Dataproc and Databricks, and deploying machine learning workloads.
What are the prerequisites needed before taking this GCP data engineering course?
The only stated prerequisite for Master Data Engineering using GCP Data Analytics is a basic understanding of SQL. This foundational skill is essential for working with data warehouses and querying services like BigQuery.
Does the Udemy course provide a certificate and is the cost worth it?
Yes, the course includes a certificate of completion. At its listed price of $14.99 for nearly 20 hours of structured content, it represents significant value for learners seeking affordable, credential-backed training in GCP data engineering.
How does this Udemy course compare to official Google Cloud training for data engineering?
This Udemy course offers a comprehensive, project-focused curriculum on GCP data analytics at a fraction of the cost of many official programs. It covers key services like BigQuery and Dataproc but is an independent course, not an official Google certification prep.
How can I get the most out of the Master Data Engineering using GCP Data Analytics course?
To get the most from this course, have a GCP account ready for hands-on practice alongside the 283 video lectures. Actively follow the curriculum chapters on building pipelines and deploying workloads, as the course is designed for practical application.