Data Engineering, Big Data, and Machine Learning on GCP
Coursera · Google Cloud · Updated
Platform rating
4.3/5
AI Tutor Rating
7.9/10
Duration
Multi-course specialization
Classes
6
This five-week, accelerated online specialization provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning. The course covers structured, unstructured, and streaming data. This course teaches the following skills: • Design and build data processing systems on Google Cloud Platform • Leverage unstructured data using Spark and ML APIs on Cloud Dataproc • Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow • Derive business insights from extremely large datasets using Google BigQuery • Train, evaluate and predict using machine learning models using Tensorflow and Cloud ML • Enable instant insights from streaming data This class is intended for developers who are responsible for: • Extracting, Loading, Transforming, cleaning, and validating data • Designing pipelines and architectures for data processing • Creating and maintaining machine learning and statistical models • Querying datasets, visualizing query results and creating reports >>> By enrolling in this specialization you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service <<<
Data Engineering, Big Data, and Machine Learning on GCP is a five-week, accelerated multi-course specialization on Coursera authored by Google Cloud. It provides a hands-on introduction to designing and building data processing systems on Google Cloud Platform. The curriculum covers structured, unstructured, and streaming data, teaching skills like building data pipelines with Cloud Dataflow, analyzing large datasets with BigQuery, and implementing machine learning with TensorFlow and Cloud ML. This specialization is intended for developers responsible for data extraction, transformation, pipeline design, and creating machine learning models.
What you'll learn in Data Engineering, Big Data, and Machine Learning on GCP
Our Review of Data Engineering, Big Data, and Machine Learning on GCP
This specialization's structure is a multi-course series delivered over five weeks, suggesting a fast-paced, intensive format. The teaching method relies on a combination of presentations, demos, and hands-on labs, which is crucial for learning practical cloud engineering skills. The curriculum is ambitious, aiming to take learners from designing systems to building end-to-end data pipelines and implementing machine learning models. This indicates a project-oriented approach where learners apply concepts directly to GCP services like Dataflow, Dataproc, and BigQuery.
The depth versus difficulty is notable. The course lists no prerequisites, yet the target audience is developers with responsibilities for data pipelines and ML models, implying it is designed for practitioners seeking to skill up rather than complete beginners. The outcomes suggest a learner who completes the hands-on labs will be able to implement autoscaling data pipelines, derive insights from large datasets, and train models with TensorFlow on GCP. The $49 price point and included certificate offer significant value for a credential directly from Google Cloud, especially for professionals needing to validate these specific platform skills. However, the accelerated timeline requires a substantial time commitment to complete the hands-on work effectively.
A critical consideration is the mandatory agreement to the Qwiklabs Terms of Service, as the hands-on labs are likely hosted on that platform. This is standard for Google Cloud training but means your lab access and data are governed by a third-party's terms, which learners should review. The specialization's value is tightly coupled to this hands-on, platform-specific experience.
Pros and cons of Data Engineering, Big Data, and Machine Learning on GCP
Pros
- Hands-on curriculum with labs for practical experience on GCP.
- Comprehensive coverage of key services like Dataflow, BigQuery, and Cloud ML.
- Direct authorship and credential from Google Cloud adds industry relevance.
- Accelerated five-week format for focused, intensive learning.
Things to consider
- Fast-paced, accelerated format may be challenging for true beginners.
- No listed prerequisites, but content is aimed at developers, assuming some foundational knowledge.
- Mandatory agreement to third-party Qwiklabs terms for lab access.
Who should take Data Engineering, Big Data, and Machine Learning on GCP?
This specialization is best for developers or data professionals with some foundational experience who need to quickly gain practical, hands-on skills in building data pipelines and machine learning systems specifically on Google Cloud Platform. It fits those aiming to design data processing systems, implement batch and streaming pipelines, and deploy ML models using core GCP services for their current role or projects.
Data Engineering, Big Data, and Machine Learning on GCP at a glance
| Provider | Coursera |
|---|---|
| Instructor | Google Cloud |
| Level | Beginner |
| Time to complete | Multi-course specialization |
| Pricing | $49 |
| Certificate | Certificate |
| Prerequisites | None |
Fit
Best for
Not ideal for
The bottom line on Data Engineering, Big Data, and Machine Learning on GCP
Data Engineering, Big Data, and Machine Learning on GCP is a high-value, intensive specialization for practitioners seeking authoritative, hands-on training directly from the platform provider. Its project-based approach on key services like Dataflow and BigQuery delivers concrete, applicable skills, though its pace assumes a motivated learner ready to engage deeply with the labs. For $49 and a Google Cloud certificate, it's a targeted investment for cloud-focused data engineering and ML roles.
Data Engineering, Big Data, and Machine Learning on GCP: frequently asked questions
What is the Data Engineering, Big Data, and Machine Learning on GCP specialization about?
This Coursera specialization is a five-week, hands-on course from Google Cloud that teaches how to design data processing systems, build end-to-end data pipelines, analyze large datasets with BigQuery, and implement machine learning using TensorFlow and Cloud ML on Google Cloud Platform.
What are the prerequisites for this Google Cloud data engineering course?
The course page lists no formal prerequisites. However, the intended audience is developers responsible for data pipelines and machine learning models, suggesting it is designed for individuals with some programming or IT background, not absolute beginners.
Does this Coursera course offer a certificate and what is the cost?
Yes, this specialization offers a certificate upon completion. The listed price for the course is $49, which covers access to the multi-course specialization and the credential from Google Cloud.
How does this GCP course compare to general data engineering courses?
Unlike general theory courses, this specialization is intensely platform-specific, focusing on hands-on implementation using Google Cloud services like Dataflow, Dataproc, and Cloud ML. It is for learners committed to building skills directly within the GCP ecosystem.
How can I succeed in this accelerated data engineering specialization?
To get the most from this five-week course, be prepared to actively engage with all hands-on labs, as the practical work on Qwiklabs is central to learning how to build systems on Dataflow, BigQuery, and Cloud ML effectively.