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Google Cloud Training
The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment.
This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.
Register for this course and see more details by visiting:
OpenCourser.com/course/mnd524/modernizing
What's inside
Syllabus
Introduction
This module introduces the Data Engineering on Google Cloud source series and this Modernizing Data Lakes and Data Warehouses with Google Cloud course.
Read more
Syllabus
Introduction
This module introduces the Data Engineering on Google Cloud source series and this Modernizing Data Lakes and Data Warehouses with Google Cloud course.
Read more
Introduction to Data Engineering
This module discusses the role of data engineering and motivates the claim why data engineering should be done in the Cloud
Building a Data Lake
In this module, we describe what data lake is and how to use Cloud Storage as your data lake on Google Cloud.
Building a data warehouse
In this module, we talk about BigQuery as a data warehousing option on Google Cloud
Summary
A summary of the key learning points
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a solid foundational understanding of data engineering's role in the cloud
Teaches learners about the practical use of Google Cloud's data lake and warehouse solutions
Examines a variety of use cases for implementing data lakes and warehouses within a data pipeline
Is part of a broader series on data engineering on Google Cloud, offering a structured learning path
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Reviews summary
Modern data lakes and data warehouses in google cloud
learners say this course covers a wealth of topics on modernizing data lakes and data warehouses with Google Cloud. According to students, it is a comprehensive course well received by learners. It engaging assignments that help you learn how to use Google Cloud Platform (GCP) tools like BigQuery, Cloud Storage, and Cloud SQL to build and manage your own data lakes and data warehouses. The labs are especially helpful for putting your new skills into practice.
Course provides a good foundation for understanding data lakes and data warehouses.
"This course lays the foundation of data engineering through the hands-on exercises on GCP services."
"This course provides a good introduction to Cloud Storage and Big Query as well as a nice overview of data engineering concepts."
Concepts are explained in a clear and easy-to-understand way.
"This was very good overview of the google cloud products for managing data warehousing solutions at scale."
"A really well structured and highly informative course, to help strengthen the skills of any student who aims to become a cloud engineer someday.!"
Concepts and use cases are explained very well.
"Very detailed explanation on Data Lake and Data Ware house and use cases."
"In depth course explaining how to build scalable & state of the art data lakes & data warehouse on GCS, CloudSQL, Bigquery."
Course focuses on using Google Cloud Platform tools.
"This course is a great course for people wishing to make a career in Data Engineering on the Google Cloud Platform. Highly recommended!"
"I especially liked the more thorough lab on arrays and structs."
Many labs to practice and reinforce concepts.
"Nice and hope to do the rest of specialization. The team ,course content and the resources are highly appreciated. Thanks"
"I really liked it. However, the pacing was a bit odd in some of the long video lectures"
Labs have strict time limits.
"Limited timing on labs stresses you out and doesn't let you discover enough about various functionalities."
"The labs sometimes take a long time to load, and require me to restart my browser even though I am browsing Coursera in incognito mode"
Some instructors are difficult to understand.
"The last part in week 2: monitor and reservation is kind of not that clear."
"Julie's lectures is hard work, she is rapid, I needed subtitles to comprehend the speech."
Some labs and concepts can be challenging.
"The course is not engaging. I watched "big-picture" videos."
"Demo's are presented by persons that click and scroll and click and scroll while mumbling along. Really worthless."
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 Modernizing Data Lakes and Data Warehouses with Google Cloud with these
activities:
Review Python Programming
Show steps
Refreshes Python programming skills for effective data engineering.
Browse courses on
Python
Show steps
-
Review the basics of Python syntax and data structures.
-
Practice writing simple Python programs.
Review of Basics of Modern Data Engineering
Show steps
Reinforce your understanding of the fundamental concepts related to data engineering, data lakes, and data warehouses to build a stronger foundation.
Browse courses on
Data Pipeline
Show steps
-
Review data warehousing concepts.
-
Re-familiarize yourself with data lake concepts.
-
Explore the basics of cloud computing.
Explore Data Engineering Concepts
Show steps
Provides a foundation for understanding data engineering concepts.
Browse courses on
Data Engineering
Show steps
-
Review the course syllabus and learning objectives.
-
Read the assigned textbook chapters.
-
Complete the practice exercises provided in the course materials.
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Guided Tutorial on Data Engineering Tools
Show steps
Become familiar with essential data engineering tools used in the course
Browse courses on
Google Cloud
Show steps
-
Follow tutorials on using Google Cloud data engineering tools
-
Complete hands-on exercises to apply what you've learned
Build a Data Pipeline with Cloud Dataflow
Show steps
Provides hands-on experience in building data pipelines using Cloud Dataflow.
Browse courses on
Data Pipelines
Show steps
-
Find a tutorial on building data pipelines with Cloud Dataflow.
-
Follow the tutorial to build a data pipeline.
-
Test the data pipeline and make necessary adjustments.
Build a Data Lake using Cloud Storage
Show steps
Build a foundation in using Cloud Storage for data lake applications
Browse courses on
Data Lakes
Show steps
-
Create a Cloud Storage bucket
-
Upload sample data to the bucket
-
Develop a script to query data from the bucket
Practice Data Engineering Queries
Show steps
Improve proficiency in writing efficient data engineering queries
Browse courses on
SQL
Show steps
-
Solve practice problems using SQL
-
Analyze query performance and optimize queries
Join a Data Engineering Study Group
Show steps
Engage with peers to discuss course concepts, share knowledge, and work through challenges together.
Browse courses on
Data Engineering
Show steps
-
Find a study partner or group.
-
Establish a regular meeting schedule.
-
Discuss course materials, ask questions, and solve problems.
Design a Data Warehouse using BigQuery
Show steps
Gain hands-on experience designing a data warehouse using BigQuery
Browse courses on
Data Warehouse
Show steps
-
Create a BigQuery dataset
-
Import sample data into the dataset
-
Develop SQL queries to analyze the data
Guided Tutorials on Google Cloud Storage
Show steps
Deepen your knowledge of Google Cloud Storage, a key component for building data lakes on Google Cloud.
Browse courses on
Google Cloud Storage
Show steps
-
Explore tutorials on creating and managing buckets.
-
Practice uploading, downloading, and managing objects.
-
Learn about security and access control for data lakes.
BigQuery Practice Exercises
Show steps
Enhance your proficiency in BigQuery by completing a set of practice exercises.
Browse courses on
BigQuery
Show steps
-
Execute SQL queries to extract and analyze data.
-
Create tables, views, and datasets.
-
Explore data visualization and reporting features.
Implement a Data Warehouse Solution
Show steps
Gains experience in implementing data warehouse solutions in the cloud.
Show steps
-
Gather requirements for the data warehouse.
-
Design the data warehouse schema.
-
Load data into the data warehouse.
-
Create queries to access and analyze data from the data warehouse.
Develop a Data Pipeline for a Real-World Dataset
Show steps
Apply your knowledge to a practical project by building a data pipeline that addresses a real-world problem.
Browse courses on
Data Pipelines
Show steps
-
Identify a dataset and define the problem statement.
-
Design the data pipeline architecture.
-
Implement data ingestion, processing, and analysis.
-
Deploy the pipeline and monitor its performance.
Design a Data Lake Architecture
Show steps
Demonstrate your understanding of data lake concepts by designing an architecture for a specific use case.
Browse courses on
Data Engineering
Show steps
-
Identify the requirements and goals of the data lake.
-
Choose appropriate data sources and storage formats.
-
Design the data ingestion and processing pipelines.
-
Implement security and governance measures.
Develop a Data Engineering Project
Show steps
Provides an opportunity to apply data engineering skills to a real-world project.
Browse courses on
Cloud Data Engineering
Show steps
-
Identify a business problem that can be solved using data engineering.
-
Design a data engineering solution to solve the problem.
-
Implement the data engineering solution.
-
Evaluate the results of the data engineering solution.
Contribute to the Google Cloud Data Engineering GitHub Repository
Show steps
Gain practical experience and contribute to the data engineering community by working on real-world projects.
Browse courses on
Data Engineering
Show steps
-
Identify an open issue or feature request.
-
Propose a solution or implementation.
-
Submit a pull request to the repository.
-
Collaborate with other contributors to improve the project.
Review Python Programming
Show steps
Refreshes Python programming skills for effective data engineering.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review the basics of Python syntax and data structures.
- Practice writing simple Python programs.
Review of Basics of Modern Data Engineering
Show steps
Reinforce your understanding of the fundamental concepts related to data engineering, data lakes, and data warehouses to build a stronger foundation.
Browse courses on
Data Pipeline
Show steps
Show steps
Show steps
- Review data warehousing concepts.
- Re-familiarize yourself with data lake concepts.
- Explore the basics of cloud computing.
Explore Data Engineering Concepts
Show steps
Provides a foundation for understanding data engineering concepts.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Review the course syllabus and learning objectives.
- Read the assigned textbook chapters.
- Complete the practice exercises provided in the course materials.
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Guided Tutorial on Data Engineering Tools
Show steps
Become familiar with essential data engineering tools used in the course
Browse courses on
Google Cloud
Show steps
Show steps
Show steps
- Follow tutorials on using Google Cloud data engineering tools
- Complete hands-on exercises to apply what you've learned
Build a Data Pipeline with Cloud Dataflow
Show steps
Provides hands-on experience in building data pipelines using Cloud Dataflow.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Find a tutorial on building data pipelines with Cloud Dataflow.
- Follow the tutorial to build a data pipeline.
- Test the data pipeline and make necessary adjustments.
Build a Data Lake using Cloud Storage
Show steps
Build a foundation in using Cloud Storage for data lake applications
Browse courses on
Data Lakes
Show steps
Show steps
Show steps
- Create a Cloud Storage bucket
- Upload sample data to the bucket
- Develop a script to query data from the bucket
Practice Data Engineering Queries
Show steps
Improve proficiency in writing efficient data engineering queries
Browse courses on
SQL
Show steps
Show steps
Show steps
- Solve practice problems using SQL
- Analyze query performance and optimize queries
Join a Data Engineering Study Group
Show steps
Engage with peers to discuss course concepts, share knowledge, and work through challenges together.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Find a study partner or group.
- Establish a regular meeting schedule.
- Discuss course materials, ask questions, and solve problems.
Design a Data Warehouse using BigQuery
Show steps
Gain hands-on experience designing a data warehouse using BigQuery
Browse courses on
Data Warehouse
Show steps
Show steps
Show steps
- Create a BigQuery dataset
- Import sample data into the dataset
- Develop SQL queries to analyze the data
Guided Tutorials on Google Cloud Storage
Show steps
Deepen your knowledge of Google Cloud Storage, a key component for building data lakes on Google Cloud.
Browse courses on
Google Cloud Storage
Show steps
Show steps
Show steps
- Explore tutorials on creating and managing buckets.
- Practice uploading, downloading, and managing objects.
- Learn about security and access control for data lakes.
BigQuery Practice Exercises
Show steps
Enhance your proficiency in BigQuery by completing a set of practice exercises.
Browse courses on
BigQuery
Show steps
Show steps
Show steps
- Execute SQL queries to extract and analyze data.
- Create tables, views, and datasets.
- Explore data visualization and reporting features.
Implement a Data Warehouse Solution
Show steps
Gains experience in implementing data warehouse solutions in the cloud.
Show steps
Show steps
Show steps
- Gather requirements for the data warehouse.
- Design the data warehouse schema.
- Load data into the data warehouse.
- Create queries to access and analyze data from the data warehouse.
Develop a Data Pipeline for a Real-World Dataset
Show steps
Apply your knowledge to a practical project by building a data pipeline that addresses a real-world problem.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Identify a dataset and define the problem statement.
- Design the data pipeline architecture.
- Implement data ingestion, processing, and analysis.
- Deploy the pipeline and monitor its performance.
Design a Data Lake Architecture
Show steps
Demonstrate your understanding of data lake concepts by designing an architecture for a specific use case.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Identify the requirements and goals of the data lake.
- Choose appropriate data sources and storage formats.
- Design the data ingestion and processing pipelines.
- Implement security and governance measures.
Develop a Data Engineering Project
Show steps
Provides an opportunity to apply data engineering skills to a real-world project.
Browse courses on
Cloud Data Engineering
Show steps
Show steps
Show steps
- Identify a business problem that can be solved using data engineering.
- Design a data engineering solution to solve the problem.
- Implement the data engineering solution.
- Evaluate the results of the data engineering solution.
Contribute to the Google Cloud Data Engineering GitHub Repository
Show steps
Gain practical experience and contribute to the data engineering community by working on real-world projects.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Identify an open issue or feature request.
- Propose a solution or implementation.
- Submit a pull request to the repository.
- Collaborate with other contributors to improve the project.
Career center
Learners who complete Modernizing Data Lakes and Data Warehouses with Google Cloud will develop knowledge and skills
that may be useful to these careers:
Cloud Data Engineer
Cloud Data Engineer
Cloud Data Engineers design, build, and manage data pipelines and data infrastructure in the cloud. They work with cloud platforms such as AWS, Azure, and Google Cloud, leveraging cloud-native services and tools to build scalable, reliable, and cost-effective data solutions. This course on Modernizing Data Lakes and Data Warehouses with Google Cloud provides Cloud Data Engineers with an in-depth understanding of Google Cloud's data storage and management offerings. Through hands-on labs and real-world examples, learners gain practical experience in designing and implementing data pipelines using Cloud Storage, BigQuery, and other Google Cloud services.
Data Engineer
Data Engineer
Data Engineers use programming and engineering practices to design and build scalable, reliable, and efficient data systems. They work with various data sources such as databases, data lakes, and data warehouses, ensuring data integrity, security, and accessibility. This course on Modernizing Data Lakes and Data Warehouses with Google Cloud provides a solid foundation for aspiring Data Engineers by introducing them to the concepts of data lakes and warehouses and their implementation on Google Cloud Platform. With hands-on labs and real-world examples, learners gain practical experience in building and managing data pipelines, which is essential for success in this role.
Data Warehouse Engineer
Data Warehouse Engineer
Data Warehouse Engineers specialize in designing, building, and maintaining data warehouses. They work with data architects and data analysts to ensure the data warehouse meets the organization's data storage and processing requirements. This course on Modernizing Data Lakes and Data Warehouses with Google Cloud provides Data Warehouse Engineers with in-depth knowledge of BigQuery, a leading cloud data warehouse solution. Through hands-on labs and real-world examples, learners gain practical experience in designing and managing data warehouses, essential for success in this role.
Data Architect
Data Architect
Data Architects design and manage data systems and pipelines, ensuring they meet the organization's business requirements. They work closely with stakeholders to understand data needs, design data models, and implement data governance policies. This course provides Data Architects with an understanding of modern data lakes and data warehouses on Google Cloud. Learning about the capabilities and best practices of Cloud Storage and BigQuery empowers them to make informed decisions when designing and implementing data solutions that align with the latest cloud technologies.
Data Scientist
Data Scientist
Data Scientists leverage data to extract insights and solve business problems. They use statistical modeling, machine learning, and other analytical techniques to uncover patterns, trends, and anomalies in data. This course on Modernizing Data Lakes and Data Warehouses with Google Cloud provides Data Scientists with a foundational understanding of data storage and management technologies. By learning about the capabilities of Cloud Storage and BigQuery, they can effectively access and analyze large datasets to support their data science projects.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts gather, analyze, and interpret data to provide insights that inform business decisions. They work with stakeholders across the organization to identify data needs, develop reports and visualizations, and communicate findings. This course on Modernizing Data Lakes and Data Warehouses with Google Cloud provides Business Intelligence Analysts with an understanding of the latest data storage and management technologies. Learning about the capabilities of Cloud Storage and BigQuery enables them to access and analyze data efficiently, supporting their role in providing valuable insights to the business.
Data Analyst
Data Analyst
Data Analysts analyze data to extract insights and identify trends. They use statistical techniques, data visualization tools, and machine learning algorithms to uncover patterns and relationships in data. This course on Modernizing Data Lakes and Data Warehouses with Google Cloud provides Data Analysts with an understanding of data storage and management technologies. Learning about the capabilities of Cloud Storage and BigQuery enables them to access and analyze large datasets efficiently, supporting their role in providing valuable insights to the business.
Database Administrator
Database Administrator
Database Administrators ensure the reliability, performance, and security of databases. They work with database systems such as MySQL, Oracle, and PostgreSQL, managing user access, optimizing performance, and implementing backup and recovery strategies. This course on Modernizing Data Lakes and Data Warehouses with Google Cloud provides Database Administrators with an understanding of cloud-based data storage and management solutions. Learning about the capabilities of Cloud Storage and BigQuery enables them to explore alternative data storage options and consider cloud migration strategies.
Cloud Engineer
Cloud Engineer
Cloud Engineers design, build, and manage cloud infrastructure and services. They work with cloud platforms such as AWS, Azure, and Google Cloud, ensuring the availability, scalability, and security of cloud-based applications and services. This course on Modernizing Data Lakes and Data Warehouses with Google Cloud provides Cloud Engineers with an understanding of data storage and management in the cloud. Learning about the capabilities of Cloud Storage and BigQuery enables them to design and implement data solutions that leverage the benefits of cloud computing.
Privacy Engineer
Privacy Engineer
Privacy Engineers design and implement technical solutions to protect personal data and comply with privacy regulations. They work with legal, compliance, and IT teams to assess privacy risks, develop data protection strategies, and implement privacy-enhancing technologies. This course on Modernizing Data Lakes and Data Warehouses with Google Cloud provides Privacy Engineers with an understanding of cloud-based data storage and management solutions. Learning about the capabilities of Cloud Storage and BigQuery enables them to explore alternative data storage options and consider cloud migration strategies in the context of data privacy.
IT Architect
IT Architect
IT Architects design, build, and maintain IT infrastructure and systems. They work with various technologies, including hardware, software, networks, and cloud platforms, to ensure the reliability, performance, and security of IT systems. This course on Modernizing Data Lakes and Data Warehouses with Google Cloud provides IT Architects with an understanding of cloud-based data storage and management solutions. Learning about the capabilities of Cloud Storage and BigQuery enables them to explore alternative data storage options and consider cloud migration strategies for their organizations.
Data Governance Specialist
Data Governance Specialist
Data Governance Specialists develop and implement policies and procedures to ensure the quality, consistency, and security of data. They work with stakeholders across the organization to define data governance standards, monitor data usage, and enforce data compliance. This course on Modernizing Data Lakes and Data Warehouses with Google Cloud provides Data Governance Specialists with an understanding of cloud-based data storage and management solutions. Learning about the capabilities of Cloud Storage and BigQuery enables them to explore alternative data storage options and consider cloud migration strategies in the context of data governance.
Information Architect
Information Architect
Information Architects design and manage information systems and structures. They work with stakeholders to understand information needs, develop taxonomies and metadata, and ensure the accessibility and usability of information. This course on Modernizing Data Lakes and Data Warehouses with Google Cloud provides Information Architects with an understanding of cloud-based data storage and management solutions. Learning about the capabilities of Cloud Storage and BigQuery enables them to explore alternative data storage options and consider cloud migration strategies for their organizations.
Data Security Analyst
Data Security Analyst
Data Security Analysts protect data from unauthorized access, use, disclosure, disruption, modification, or destruction. They work with IT security teams to implement data security measures, monitor data access, and respond to security incidents. This course on Modernizing Data Lakes and Data Warehouses with Google Cloud provides Data Security Analysts with an understanding of cloud-based data storage and management solutions. Learning about the capabilities of Cloud Storage and BigQuery enables them to explore alternative data storage options and consider cloud migration strategies in the context of data security.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with programming languages, software development tools, and cloud platforms to build and deploy software solutions. This course on Modernizing Data Lakes and Data Warehouses with Google Cloud provides Software Engineers with an understanding of data storage and management technologies. Learning about the capabilities of Cloud Storage and BigQuery enables them to integrate data-driven features into their software applications, enhancing their functionality and value.
For more career information including salaries, visit:
OpenCourser.com/course/mnd524/modernizing
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Modernizing Data Lakes and Data Warehouses with Google Cloud.
Provides comprehensive coverage of Google Cloud's data lake and warehouse solutions and valuable resource for those who want to dive deeper into the technical details of these technologies.
Classic in the field of data warehousing. It provides a comprehensive overview of data warehousing concepts, technologies, and applications. It valuable resource for those looking to learn more about data warehousing.
Save
Provides a comprehensive overview of Hadoop operations, including performance tuning and security.
This classic book provides a foundational understanding of data warehousing concepts and principles, which are essential for building and managing effective data warehouses.
Provides a broader perspective on data science and big data analytics, which is helpful for understanding the role of data lakes and warehouses in the larger context of data-driven decision-making.
Provides a gentle introduction to data lakes and good resource for those who are new to the concept.
Provides a gentle introduction to data warehouses and good resource for those who are new to the concept.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mnd524/modernizing
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Good to know
Builds a solid foundational understanding of data engineering's role in the cloud
Teaches learners about the practical use of Google Cloud's data lake and warehouse solutions
Examines a variety of use cases for implementing data lakes and warehouses within a data pipeline
Is part of a broader series on data engineering on Google Cloud, offering a structured learning path
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