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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 Platform in technical detail.
Read more
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 Platform in technical detail.
Read more
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 Platform in technical detail.
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 Platform 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. Learners will get hands-on experience with data lakes and warehouses on Google Cloud Platform using QwikLabs.
Register for this course and see more details by visiting:
OpenCourser.com/course/c2f2ok/modernizing
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What's inside
Syllabus
Introduction
Introduction to Data Engineering
Building a Data Lake
Building a data warehouse
Read more
Syllabus
Introduction
Introduction to Data Engineering
Building a Data Lake
Building a data warehouse
Read more
Summary
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation for beginners in data engineering concepts
Provides hands-on experience with data engineering services on Google Cloud Platform
Introduces data pipelines and their components, focusing specifically on data lakes and warehouses
Geared towards individuals interested in cloud data engineering, particularly on the Google Cloud Platform
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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 GCP with these
activities:
Refresher on data architecture and design principles
Show steps
Refreshes and strengthens the core concepts of data architecture and design principles, which are fundamental to understanding data lakes and warehouses.
Browse courses on
Data Storage
Show steps
-
Review notes, articles, or online resources on data architecture and design principles.
-
Take practice quizzes or mock tests to assess understanding.
-
Engage in discussions or forums to clarify concepts.
Organize and annotate course materials for future reference
Show steps
Enhanced organization and annotations will support efficient review and recall of key concepts.
Show steps
-
Create a dedicated folder or notebook for course materials.
-
Annotate notes, assignments, and quizzes with key takeaways and insights.
Review Data Engineering Principles
Show steps
Refresh your understanding of core data engineering concepts and practices to strengthen your foundation for this course.
Browse courses on
Data Engineering
Show steps
-
Revisit concepts of data pipelines, data lakes, and data warehouses.
-
Review the role and responsibilities of a data engineer.
-
Explore the benefits of implementing successful data pipelines for business operations.
17 other activities
Expand to see all activities and additional details
Show all 20 activities
Review basic data science fundamentals
Show steps
Brush up on basic data science concepts to ensure a strong foundation for this course.
Browse courses on
Data Science
Show steps
-
Revisit key concepts from introductory data science courses or tutorials.
-
Work through practice problems or exercises to test your understanding.
Set Up Project Environment
Show steps
Setting up your project environment will familiarize you with Google Cloud and enable you to begin building your data pipelines.
Browse courses on
Cloud Platform
Show steps
-
Create Google Cloud account and project.
-
Install Google Cloud SDK and configure credentials.
Create and Load Data Lakes
Show steps
Practice building data lakes will improve your understanding of data storage on Google Cloud.
Browse courses on
Data Lake
Show steps
-
Create a Cloud Storage bucket to store data.
-
Ingest data into Cloud Storage from a variety of sources.
-
Structure and organize data in Cloud Storage.
Guided tutorial on Google Cloud Platform's data lake and warehouse services
Show steps
Provides hands-on experience with the specific tools and services that will be used in the course, enhancing practical understanding.
Browse courses on
Google Cloud Storage
Show steps
-
Follow step-by-step tutorials to create and manage data lakes and warehouses on GCP.
-
Experiment with different data loading, querying, and analysis techniques.
-
Refer to documentation and support resources to clarify concepts.
Participate in Discussion Forums
Show steps
Engage with peers in discussion forums, sharing insights, asking questions, and learning from others' perspectives.
Browse courses on
Discussion
Show steps
-
Identify relevant discussion forums related to data lakes and warehouses.
-
Participate in discussions by posting thoughtful questions and responses.
-
Review and comment on other participants' posts, fostering a collaborative learning environment.
Explore Data Warehouses
Show steps
Following these guided tutorials will enhance your understanding of data warehouses in the context of Google Cloud.
Browse courses on
Data Warehouse
Show steps
-
Access Google Cloud's documentation on data warehouses.
-
Review available tutorials on data warehouses.
-
Complete a tutorial on building a data warehouse using BigQuery.
Explore QwikLabs Tutorials
Show steps
Gain hands-on experience through QwikLabs tutorials, reinforcing the practical aspects of data lake and warehouse concepts.
Browse courses on
Hands-on Experience
Show steps
-
Identify relevant QwikLabs tutorials on data lakes and warehouses.
-
Complete the tutorials, following the instructions and experimenting with the provided tools.
-
Document your learnings and any challenges encountered during the tutorials.
Explore case studies of data lakes and warehouses
Show steps
Gain practical insights by examining real-world examples of data lakes and warehouses.
Show steps
-
Identify case studies that align with your interests or industry.
-
Analyze the problems these case studies address and the solutions implemented.
-
Extract key takeaways and best practices.
Write a blog post or article on a specific aspect of data lakes or warehouses
Show steps
Share your knowledge and insights with others to reinforce your understanding.
Show steps
-
Choose a specific topic or aspect of data lakes or warehouses to write about.
-
Research and gather information from reliable sources.
-
Write a well-structured and informative article.
Attend a Data Engineering Workshop
Show steps
Deepen your understanding by attending a data engineering workshop, gaining practical insights and hands-on experience.
Browse courses on
Data Engineering
Show steps
-
Research and identify upcoming data engineering workshops.
-
Register for a workshop that aligns with your learning goals.
-
Actively participate in the workshop, asking questions and engaging in discussions.
-
Apply the knowledge and skills gained to your own projects or work.
Design a Data Pipeline Diagram
Show steps
Create a visual representation of a data pipeline, showcasing your understanding of the data flow and components involved.
Browse courses on
Data Pipeline
Show steps
-
Identify a specific data pipeline scenario to focus on.
-
Design a diagram that illustrates the data sources, data transformations, data storage, and data consumption.
-
Include annotations and labels to explain the functionality and flow of data.
-
Share your diagram for feedback and discussion.
Experiment with data lake and warehouse services on Google Cloud
Show steps
Hands-on experience will deepen your understanding of data lake and warehouse concepts.
Show steps
-
Create a Google Cloud account and set up the necessary services.
-
Follow tutorials to build and manage data lakes and warehouses.
-
Perform data analysis and visualization tasks using these services.
Contribute to open-source projects related to data lakes or warehouses
Show steps
Gain practical experience and showcase your skills by contributing to the open-source community.
Show steps
-
Search for open-source projects on platforms like GitHub or Apache Software Foundation.
-
Identify issues or features that you can contribute to.
-
Fork the project repository and start working on your contributions.
Build a Data Pipeline
Show steps
Building a data pipeline will apply your knowledge of data lakes and data warehouses.
Browse courses on
Data Pipeline
Show steps
-
Design the data pipeline architecture.
-
Use Google Cloud to create the pipeline.
-
Test and validate the data pipeline.
Practice exercises on data query optimization and performance tuning
Show steps
Strengthens skills in optimizing data queries and tuning data warehouse performance, which are crucial for efficient data analysis and reporting.
Show steps
-
Solve practice problems or coding challenges related to data query optimization.
-
Experiment with different query techniques and indexing strategies.
-
Monitor and analyze query performance using profiling tools.
Contribute to Open Source Data Engineering Projects
Show steps
Enhance your practical skills and expand your knowledge by contributing to open-source data engineering projects.
Browse courses on
Open Source
Show steps
-
Identify open-source data engineering projects on platforms like GitHub.
-
Review the project documentation and identify areas where you can contribute.
-
Make code contributions, bug fixes, or documentation improvements.
-
Collaborate with other contributors and maintainers.
Design a data lake or warehouse solution for a specific business scenario
Show steps
Apply your knowledge to solve real-world problems and demonstrate your understanding.
Show steps
-
Identify a business scenario or problem that requires a data lake or warehouse solution.
-
Design the architecture and implementation plan for the solution.
-
Create a presentation or report outlining your design.
Refresher on data architecture and design principles
Show steps
Refreshes and strengthens the core concepts of data architecture and design principles, which are fundamental to understanding data lakes and warehouses.
Browse courses on
Data Storage
Show steps
Show steps
Show steps
- Review notes, articles, or online resources on data architecture and design principles.
- Take practice quizzes or mock tests to assess understanding.
- Engage in discussions or forums to clarify concepts.
Organize and annotate course materials for future reference
Show steps
Enhanced organization and annotations will support efficient review and recall of key concepts.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- Annotate notes, assignments, and quizzes with key takeaways and insights.
Review Data Engineering Principles
Show steps
Refresh your understanding of core data engineering concepts and practices to strengthen your foundation for this course.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Revisit concepts of data pipelines, data lakes, and data warehouses.
- Review the role and responsibilities of a data engineer.
- Explore the benefits of implementing successful data pipelines for business operations.
17 other activities
Expand to see all activities and additional details
Show all 20 activities
Review basic data science fundamentals
Show steps
Brush up on basic data science concepts to ensure a strong foundation for this course.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Revisit key concepts from introductory data science courses or tutorials.
- Work through practice problems or exercises to test your understanding.
Set Up Project Environment
Show steps
Setting up your project environment will familiarize you with Google Cloud and enable you to begin building your data pipelines.
Browse courses on
Cloud Platform
Show steps
Show steps
Show steps
- Create Google Cloud account and project.
- Install Google Cloud SDK and configure credentials.
Create and Load Data Lakes
Show steps
Practice building data lakes will improve your understanding of data storage on Google Cloud.
Browse courses on
Data Lake
Show steps
Show steps
Show steps
- Create a Cloud Storage bucket to store data.
- Ingest data into Cloud Storage from a variety of sources.
- Structure and organize data in Cloud Storage.
Guided tutorial on Google Cloud Platform's data lake and warehouse services
Show steps
Provides hands-on experience with the specific tools and services that will be used in the course, enhancing practical understanding.
Browse courses on
Google Cloud Storage
Show steps
Show steps
Show steps
- Follow step-by-step tutorials to create and manage data lakes and warehouses on GCP.
- Experiment with different data loading, querying, and analysis techniques.
- Refer to documentation and support resources to clarify concepts.
Participate in Discussion Forums
Show steps
Engage with peers in discussion forums, sharing insights, asking questions, and learning from others' perspectives.
Browse courses on
Discussion
Show steps
Show steps
Show steps
- Identify relevant discussion forums related to data lakes and warehouses.
- Participate in discussions by posting thoughtful questions and responses.
- Review and comment on other participants' posts, fostering a collaborative learning environment.
Explore Data Warehouses
Show steps
Following these guided tutorials will enhance your understanding of data warehouses in the context of Google Cloud.
Browse courses on
Data Warehouse
Show steps
Show steps
Show steps
- Access Google Cloud's documentation on data warehouses.
- Review available tutorials on data warehouses.
- Complete a tutorial on building a data warehouse using BigQuery.
Explore QwikLabs Tutorials
Show steps
Gain hands-on experience through QwikLabs tutorials, reinforcing the practical aspects of data lake and warehouse concepts.
Browse courses on
Hands-on Experience
Show steps
Show steps
Show steps
- Identify relevant QwikLabs tutorials on data lakes and warehouses.
- Complete the tutorials, following the instructions and experimenting with the provided tools.
- Document your learnings and any challenges encountered during the tutorials.
Explore case studies of data lakes and warehouses
Show steps
Gain practical insights by examining real-world examples of data lakes and warehouses.
Show steps
Show steps
Show steps
- Identify case studies that align with your interests or industry.
- Analyze the problems these case studies address and the solutions implemented.
- Extract key takeaways and best practices.
Write a blog post or article on a specific aspect of data lakes or warehouses
Show steps
Share your knowledge and insights with others to reinforce your understanding.
Show steps
Show steps
Show steps
- Choose a specific topic or aspect of data lakes or warehouses to write about.
- Research and gather information from reliable sources.
- Write a well-structured and informative article.
Attend a Data Engineering Workshop
Show steps
Deepen your understanding by attending a data engineering workshop, gaining practical insights and hands-on experience.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Research and identify upcoming data engineering workshops.
- Register for a workshop that aligns with your learning goals.
- Actively participate in the workshop, asking questions and engaging in discussions.
- Apply the knowledge and skills gained to your own projects or work.
Design a Data Pipeline Diagram
Show steps
Create a visual representation of a data pipeline, showcasing your understanding of the data flow and components involved.
Browse courses on
Data Pipeline
Show steps
Show steps
Show steps
- Identify a specific data pipeline scenario to focus on.
- Design a diagram that illustrates the data sources, data transformations, data storage, and data consumption.
- Include annotations and labels to explain the functionality and flow of data.
- Share your diagram for feedback and discussion.
Experiment with data lake and warehouse services on Google Cloud
Show steps
Hands-on experience will deepen your understanding of data lake and warehouse concepts.
Show steps
Show steps
Show steps
- Create a Google Cloud account and set up the necessary services.
- Follow tutorials to build and manage data lakes and warehouses.
- Perform data analysis and visualization tasks using these services.
Contribute to open-source projects related to data lakes or warehouses
Show steps
Gain practical experience and showcase your skills by contributing to the open-source community.
Show steps
Show steps
Show steps
- Search for open-source projects on platforms like GitHub or Apache Software Foundation.
- Identify issues or features that you can contribute to.
- Fork the project repository and start working on your contributions.
Build a Data Pipeline
Show steps
Building a data pipeline will apply your knowledge of data lakes and data warehouses.
Browse courses on
Data Pipeline
Show steps
Show steps
Show steps
- Design the data pipeline architecture.
- Use Google Cloud to create the pipeline.
- Test and validate the data pipeline.
Practice exercises on data query optimization and performance tuning
Show steps
Strengthens skills in optimizing data queries and tuning data warehouse performance, which are crucial for efficient data analysis and reporting.
Show steps
Show steps
Show steps
- Solve practice problems or coding challenges related to data query optimization.
- Experiment with different query techniques and indexing strategies.
- Monitor and analyze query performance using profiling tools.
Contribute to Open Source Data Engineering Projects
Show steps
Enhance your practical skills and expand your knowledge by contributing to open-source data engineering projects.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Identify open-source data engineering projects on platforms like GitHub.
- Review the project documentation and identify areas where you can contribute.
- Make code contributions, bug fixes, or documentation improvements.
- Collaborate with other contributors and maintainers.
Design a data lake or warehouse solution for a specific business scenario
Show steps
Apply your knowledge to solve real-world problems and demonstrate your understanding.
Show steps
Show steps
Show steps
- Identify a business scenario or problem that requires a data lake or warehouse solution.
- Design the architecture and implementation plan for the solution.
- Create a presentation or report outlining your design.
Career center
Learners who complete Modernizing Data Lakes and Data Warehouses with GCP will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
Data Engineers design, construct, deploy, maintain, and manage data pipelines and infrastructure. This course provides the core knowledge of data lakes and data warehouses on Google Cloud Platform that a Data Engineer needs to be successful.
Data Analyst
Data Analyst
Data Analysts analyze data and provide insights to stakeholders. This course's focus on data lakes and data warehouses will provide a foundational understanding of where data is stored and managed, which is crucial for Data Analysts.
Data Scientist
Data Scientist
Data Scientists use data to build models and make predictions. This course provides an overview of data lakes and data warehouses, which are essential for storing and managing the data Data Scientists use.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to provide insights to businesses. This course provides a foundational understanding of data lakes and data warehouses, which are essential for storing and managing the data Business Intelligence Analysts use.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides an overview of data lakes and data warehouses, which are often used to store and manage data for software systems.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. This course provides an overview of data lakes and data warehouses, which are often used to store and manage data in databases.
Cloud Architect
Cloud Architect
Cloud Architects design and manage cloud computing systems. This course provides an overview of data lakes and data warehouses, which are often used to store and manage data in cloud computing systems.
Data Architect
Data Architect
Data Architects design and manage data systems. This course provides an overview of data lakes and data warehouses, which are often used to store and manage data in data systems.
Project Manager
Project Manager
Project Managers plan and execute projects. This course provides an overview of data lakes and data warehouses, which are often used to store and manage data for projects.
Product Manager
Product Manager
Product Managers manage the development and launch of products. This course provides an overview of data lakes and data warehouses, which are often used to store and manage data for products.
Finance Manager
Finance Manager
Finance Managers manage financial operations and ensure financial health. This course provides an overview of data lakes and data warehouses, which are often used to store and manage data for financial operations.
Sales Manager
Sales Manager
Sales Managers manage sales teams and drive revenue. This course provides an overview of data lakes and data warehouses, which are often used to store and manage data for sales teams.
Customer Success Manager
Customer Success Manager
Customer Success Managers manage customer relationships and ensure customer satisfaction. This course provides an overview of data lakes and data warehouses, which are often used to store and manage data for customer success teams.
Account Manager
Account Manager
Account Managers manage customer accounts and drive revenue. This course provides an overview of data lakes and data warehouses, which are often used to store and manage data for customer accounts.
Marketing Manager
Marketing Manager
Marketing Managers plan and execute marketing campaigns. This course provides an overview of data lakes and data warehouses, which are often used to store and manage data for marketing campaigns.
For more career information including salaries, visit:
OpenCourser.com/course/c2f2ok/modernizing
Reading list
We've selected 12 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 GCP.
Provides a comprehensive guide to Spark, covering topics such as data processing, data analysis, and machine learning. It valuable resource for anyone looking to learn more about Spark.
Provides a comprehensive introduction to statistical learning, covering topics such as linear regression, logistic regression, and decision trees. It valuable resource for anyone looking to learn more about statistical learning.
Provides a comprehensive guide to dimensional modeling, a technique for designing data warehouses. It valuable resource for anyone looking to learn more about data warehouse design.
Provides a practical guide to data science, covering topics such as data analysis, machine learning, and data visualization. It valuable resource for anyone looking to learn more about data science.
Provides a hands-on guide to deep learning, using the Fastai library. It covers topics such as data preprocessing, model training, and model evaluation. It valuable resource for anyone looking to learn more about deep learning.
Provides a comprehensive overview of Elasticsearch, including its architecture, programming model, and use cases. It valuable resource for anyone who wants to learn more about Elasticsearch.
Provides a comprehensive overview of artificial intelligence, covering topics such as machine learning, natural language processing, and computer vision. It valuable resource for anyone looking to learn more about artificial intelligence.
Provides a comprehensive overview of data warehousing fundamentals. It valuable resource for anyone who wants to learn more about data warehousing.
Provides a comprehensive overview of data-intensive text processing with MapReduce. It covers all aspects of data-intensive text processing, including data preparation, text processing algorithms, and evaluation techniques.
Save
Provides a comprehensive overview of Python programming. It covers all aspects of Python, including its syntax, semantics, and idioms. It valuable resource for anyone who wants to learn more about Python.
Provides a comprehensive guide to machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone looking to learn more about machine learning.
Provides a comprehensive overview of Hadoop, including its architecture, programming model, and use cases. It valuable resource for anyone who wants to learn more about Hadoop.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/c2f2ok/modernizing
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Time
215 hours
Level
Intermediate
Via
Pluralsight
Instructor
Google Cloud
Language
English
Good to know
Builds a strong foundation for beginners in data engineering concepts
Provides hands-on experience with data engineering services on Google Cloud Platform
Introduces data pipelines and their components, focusing specifically on data lakes and warehouses
Geared towards individuals interested in cloud data engineering, particularly on the Google Cloud Platform
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