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Modernizing Data Lakes and Data Warehouses with GCP
Google Cloud
Google Cloud
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
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
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Modernizing Data Lakes and Data Warehouses with GCP.
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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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