Database, Big Data, and DevOps Services in GCP from Coursera

Welcome to the Database, Big Data, and DevOps Services in Google Cloud Platform (GCP) course!

This course is designed to equip learners with comprehensive knowledge and practical skills in leveraging GCP's database, big data, and DevOps services. Whether you're a developer, system administrator, or data engineer, this course will delve into the essential tools and techniques offered by GCP to manage and optimize data storage, processing, and deployment workflows.

Prerequisites include familiarity with cloud computing concepts and basic understanding of GCP services.

By the end of this course, you will:

- Gain a deep understanding of GCP's database services, including Cloud SQL, Cloud Spanner, and Firestore, and their use cases.

- Master big data processing and analytics using GCP tools such as BigQuery, Dataflow, and Dataproc.

Implement effective DevOps practices on GCP, integrating tools like Cloud Build, Cloud Deployment Manager, and Kubernetes Engine.

- Design resilient and scalable architectures for databases and big data solutions on GCP.

- Optimize performance and cost-efficiency in managing large-scale data operations on GCP.

- Develop hands-on skills through practical labs, exercises, and real-world scenarios.

- Prepare for Google Cloud certifications related to database management, big data, or DevOps.

This course is designed with comprehensive video lectures, hands-on demos, and practical assignments to ensure a thorough understanding and practical application of GCP's database, big data, and DevOps services.

What's inside

Syllabus

Database Services

This module covers a range of topics including database services such as Cloud SQL, Cloud Spanner, Cloud Datastore, Cloud Firestore, and Cloud Bigtable. By the end, learners will understand various database types, their functionalities, and will be able to utilize different Google Cloud Platform database services effectively.

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Explores industry-standard database and DevOps services, including Cloud SQL, Cloud Spanner, Firestore, BigQuery, Dataflow, Dataproc, Cloud Build, and Kubernetes Engine

Teaches in-demand skills for managing and optimizing data storage, processing, and deployment workflows on GCP

Suitable for developers, system administrators, and data engineers seeking hands-on experience and practical knowledge in data management and optimization

Designed with comprehensive video lectures, hands-on demonstrations, and practical assignments to reinforce understanding and application

Provides preparation for Google Cloud certifications related to database management, big data, or DevOps

Recommended for learners with familiarity in cloud computing concepts and basic knowledge of GCP services

Reviews summary

Gcp data & devops deep dive

According to students, this course offers a comprehensive and thorough overview of GCP's database, big data, and DevOps services. Learners consistently highlight the instrumental hands-on labs and practical application, which are key for building real-world skills and preparing for certification. While the course is up-to-date and relevant, some feel the DevOps section could offer more depth or advanced examples. Beginners should note that despite stated prerequisites, the pace can be fast, suggesting more than basic GCP familiarity is beneficial. Notably, earlier issues with outdated labs appear to have been resolved, with recent reviews praising their quality.

Earlier problems with outdated labs have been addressed.

"The hands-on labs were instrumental in solidifying my understanding. I particularly appreciated the detailed explanations..."

"Excellent course!... The labs are challenging but fair, and they truly reinforce the concepts."

"The course content is decent, but some of the labs provided seem to be outdated or have broken links... The material needs maintenance."

Provides a broad and thorough understanding of GCP services.

"This course is incredibly thorough and well-structured, covering a vast array of GCP services for databases, big data, and DevOps."

"The breadth of topics covered is impressive. I went from having basic GCP knowledge to feeling confident about designing complex data architectures."

"A solid introduction to GCP's data and DevOps offerings... Good general overview of GCP services."

Labs are highly effective for practical skill development.

"The hands-on labs were instrumental in solidifying my understanding."

"Fantastic course for anyone serious about GCP. The labs are perfectly designed to give you real-world experience."

"Highly practical and hands-on! The exercises clarify complex concepts."

The DevOps section could benefit from more advanced examples.

"Some sections, especially on Kubernetes Engine for DevOps, could benefit from more advanced examples."

"DevOps felt a bit tacked on and less detailed than the other two [Database, Big Data]."

"Some of the DevOps tools could use more extensive examples."

Pacing can be fast, requiring more than basic GCP knowledge.

"Too fast-paced for a beginner. The prerequisites mentioned are minimal, but you really need more than just 'basic understanding' of GCP."

"I struggled to keep up with the pace and the technical jargon."

"The course covers a lot, perhaps too much, in a short span. While the overview is good, the depth on certain services... felt rushed."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Database, Big Data, and DevOps Services in GCP with these activities:

Join a study group to discuss course concepts

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Enhance understanding by engaging in discussions and sharing knowledge with peers in a study group.

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Find or join a study group with other students taking the same course.
Meet regularly to discuss lecture materials, assignments, and practice problems.
Share notes, insights, and resources with group members.

Follow a tutorial on SQL Data Modeling

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Reinforce understanding of database structures by following a hands-on tutorial on SQL Data Modeling.

Browse courses on Database Services

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Find a comprehensive tutorial on SQL Data Modeling.
Go through the tutorial step-by-step, applying the concepts to practical examples.
Create a database schema based on the principles learned in the tutorial.

Practice SQL queries and DML statements

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Enhance proficiency in SQL by practicing queries and DML statements.

Browse courses on SQL Queries

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Identify a dataset or database to work with.
Write SQL queries to retrieve, filter, and manipulate data.
Practice inserting, updating, and deleting data using DML statements.

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Deploy a serverless application on Google Cloud Functions

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Gain hands-on experience in deploying and managing serverless applications by building one on Google Cloud Functions.

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Design and develop the serverless function.
Configure and deploy the function to Google Cloud Functions.
Test and monitor the deployed function.

Build a data dashboard using Google Data Studio

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Develop practical skills in data visualization and analytics by creating a data dashboard with Google Data Studio.

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Gather and prepare the necessary data sources.
Explore the data and identify key metrics.
Design and build interactive dashboards using Google Data Studio.
Share and present the dashboard to stakeholders.

Review 'Designing Data-Intensive Applications' by Martin Kleppmann

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Gain in-depth knowledge of data modeling and scalability considerations by reviewing a leading book on data-intensive application design.

View Secret Colors on Amazon

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Read the chapters relevant to the course topics.
Take notes and highlight key concepts.
Apply the principles to your own data management projects.

Participate in a Cloud Data Challenge

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Test your skills and gain recognition by participating in a Cloud Data Challenge that aligns with the course topics.

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Identify and register for relevant Cloud Data Challenges.
Gather a team or work independently to solve the challenge problems.
Develop and submit your solutions.

Contribute to an open-source project related to Cloud Data Services

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Deepen understanding and make meaningful contributions by participating in an open-source project that aligns with the course content.

Browse courses on Database Services

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Identify an open-source project that aligns with your interests.
Review the project documentation and codebase.
Identify areas where you can make contributions.
Create and submit pull requests with your contributions.

Career center

Learners who complete Database, Big Data, and DevOps Services in GCP will develop knowledge and skills that may be useful to these careers:

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Database, Big Data, and DevOps Services in GCP

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What's inside

Syllabus

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Reviews summary

Gcp data & devops deep dive

Activities

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