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Google Cloud Training
Die zwei wichtigsten Komponenten jeder Datenpipeline sind Data Lakes und Data Warehouses. In diesem Kurs werden Anwendungsfälle für beide Speicherarten vorgestellt. Außerdem wird eine technisch detaillierte Einteilung der Data-Lake- und Data-Warehouse...
Read more
Die zwei wichtigsten Komponenten jeder Datenpipeline sind Data Lakes und Data Warehouses. In diesem Kurs werden Anwendungsfälle für beide Speicherarten vorgestellt. Außerdem wird eine technisch detaillierte Einteilung der Data-Lake- und Data-Warehouse...
Read more
Die zwei wichtigsten Komponenten jeder Datenpipeline sind Data Lakes und Data Warehouses. In diesem Kurs werden Anwendungsfälle für beide Speicherarten vorgestellt. Außerdem wird eine technisch detaillierte Einteilung der Data-Lake- und Data-Warehouse-Lösungen vorgenommen, die in der Google Cloud Platform verfügbar sind. Im Kurs wird auch erläutert, welche Aufgaben ein Data Engineer hat, wie eine erfolgreiche Datenpipeline Geschäftsvorgänge unterstützt und warum Data Engineering in einer Cloud-Umgebung stattfinden sollte. Mithilfe von Qwiklabs arbeiten die Kursteilnehmer dann selbst mit Data Lakes und Data Warehouses in der Google Cloud Platform.
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what to watch for , and
possible dealbreakers
Develops skills essential for working with Data Lakes and Data Warehouses, which are increasingly necessary in data-driven decision making
Provides a detailed comparison of Data Lake and Data Warehouse solutions available in Google Cloud Platform, offering insights for selecting the right solution for specific needs
Explores industry-standard data storage solutions, making the course highly relevant for professionals in data-related fields
Taught by Google Cloud Training, who are recognized for their expertise in cloud computing and data management
Offers hands-on practice through Qwiklabs, allowing learners to apply concepts and reinforce understanding
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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 auf Deutsch with these
activities:
Review Data Lakes Concepts
Show steps
Refresh your understanding of data lakes, their purpose, and their components, ensuring a solid foundation for the course.
Browse courses on
Data Lakes
Show steps
-
Read an overview of data lakes from a reputable source.
-
Identify the key benefits and use cases of data lakes.
-
Review the different types of data lakes, including their advantages and disadvantages.
Review scalable data storage
Show steps
Reviewing how scalable data storage works will help you learn faster about Data Lakes and Data Warehouses during the course
Browse courses on
BigQuery
Show steps
-
Read books, research articles and watch videos about scalable data storage
-
Complete online tutorials on scalable data storage
-
Review BigQuery, Data Storage and Cloud Storage documentations
Follow tutorials on data pipeline management
Show steps
Following tutorials on data pipeline management will help you learn faster how to work with Data Lakes and Data Warehouses during the course
Browse courses on
Data Warehousing
Show steps
-
Find tutorials on data pipeline management
-
Follow the tutorials and complete the exercises
-
Review the material and make sure you understand the concepts
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Volunteer at a data science organization
Show steps
Volunteering at a data science organization will help you learn faster about Data Lakes and Data Warehouses during the course
Browse courses on
Data Science
Show steps
-
Find a data science organization that you are interested in volunteering at
-
Contact the organization and inquire about volunteer opportunities
-
Attend volunteer training and orientation
-
Start volunteering!
Follow Guided Tutorials on Data Warehouses
Show steps
Deepen your understanding of data warehouses by following guided tutorials, providing hands-on practice and reinforcing key concepts.
Browse courses on
Data Warehouses
Show steps
-
Find well-regarded tutorials on data warehouse design and implementation.
-
Follow the tutorials step-by-step, implementing the concepts in a practical setting.
-
Experiment with different data warehouse tools and techniques.
Practice analyzing data pipelines
Show steps
Practicing analyzing data pipelines will help you learn faster how to work with Data Lakes and Data Warehouses during the course
Browse courses on
Data Analysis
Show steps
-
Read books, research articles and watch videos about data pipeline analysis
-
Complete online tutorials on data pipeline analysis
-
Review documentation of GCP tools for data pipeline analysis
Practice Data Lake and Data Warehouse Queries
Show steps
Enhance your proficiency in querying data lakes and data warehouses by completing practice exercises, improving your analytical skills and data retrieval techniques.
Browse courses on
Data Queries
Show steps
-
Find online resources or practice platforms that offer data lake and data warehouse querying exercises.
-
Attempt the exercises, focusing on optimizing your queries for performance and efficiency.
-
Analyze the results of your queries to ensure accurate data retrieval.
Contribute to an open-source data pipeline project
Show steps
Contributing to an open-source data pipeline project will help you learn faster about Data Lakes and Data Warehouses during the course
Browse courses on
Open Source
Show steps
-
Find an open-source data pipeline project that you are interested in contributing to
-
Fork the project and make changes
-
Submit a pull request
-
Respond to feedback and iterate on your changes
Design a data pipeline
Show steps
Designing a data pipeline will help you learn faster how to work with Data Lakes and Data Warehouses during the course
Browse courses on
Data Warehousing
Show steps
-
Identify the data sources and the data to be processed
-
Design the data pipeline architecture
-
Implement the data pipeline using GCP tools
Build a Data Pipeline Prototype
Show steps
Solidify your understanding of data pipelines by designing and building a prototype in the Google Cloud Platform, applying the principles and technologies covered in the course.
Browse courses on
Data Pipelines
Show steps
-
Define the purpose and scope of your data pipeline prototype.
-
Choose appropriate data sources and data transformation tools.
-
Implement the data pipeline in the Google Cloud Platform, utilizing its data processing and storage services.
-
Test and evaluate the performance of your prototype.
Build a data pipeline using Google Cloud Platform
Show steps
Building a data pipeline using Google Cloud Platform will help you learn faster how to work with Data Lakes and Data Warehouses during the course
Show steps
-
Set up a GCP project
-
Create a data pipeline using GCP tools
-
Deploy the data pipeline
-
Monitor the data pipeline
Volunteer at a Data-Driven Organization
Show steps
Gain practical experience and contribute to real-world projects by volunteering at an organization that utilizes data-driven decision-making, enhancing your understanding of data application and its impact.
Browse courses on
Data Analytics
Show steps
-
Identify organizations in your area that align with your interests and utilize data.
-
Contact the organizations and inquire about volunteer opportunities.
-
Contribute to data-related projects, such as data analysis, visualization, or machine learning.
Review Data Lakes Concepts
Show steps
Refresh your understanding of data lakes, their purpose, and their components, ensuring a solid foundation for the course.
Browse courses on
Data Lakes
Show steps
Show steps
Show steps
- Read an overview of data lakes from a reputable source.
- Identify the key benefits and use cases of data lakes.
- Review the different types of data lakes, including their advantages and disadvantages.
Review scalable data storage
Show steps
Reviewing how scalable data storage works will help you learn faster about Data Lakes and Data Warehouses during the course
Browse courses on
BigQuery
Show steps
Show steps
Show steps
- Read books, research articles and watch videos about scalable data storage
- Complete online tutorials on scalable data storage
- Review BigQuery, Data Storage and Cloud Storage documentations
Follow tutorials on data pipeline management
Show steps
Following tutorials on data pipeline management will help you learn faster how to work with Data Lakes and Data Warehouses during the course
Browse courses on
Data Warehousing
Show steps
Show steps
Show steps
- Find tutorials on data pipeline management
- Follow the tutorials and complete the exercises
- Review the material and make sure you understand the concepts
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Volunteer at a data science organization
Show steps
Volunteering at a data science organization will help you learn faster about Data Lakes and Data Warehouses during the course
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find a data science organization that you are interested in volunteering at
- Contact the organization and inquire about volunteer opportunities
- Attend volunteer training and orientation
- Start volunteering!
Follow Guided Tutorials on Data Warehouses
Show steps
Deepen your understanding of data warehouses by following guided tutorials, providing hands-on practice and reinforcing key concepts.
Browse courses on
Data Warehouses
Show steps
Show steps
Show steps
- Find well-regarded tutorials on data warehouse design and implementation.
- Follow the tutorials step-by-step, implementing the concepts in a practical setting.
- Experiment with different data warehouse tools and techniques.
Practice analyzing data pipelines
Show steps
Practicing analyzing data pipelines will help you learn faster how to work with Data Lakes and Data Warehouses during the course
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Read books, research articles and watch videos about data pipeline analysis
- Complete online tutorials on data pipeline analysis
- Review documentation of GCP tools for data pipeline analysis
Practice Data Lake and Data Warehouse Queries
Show steps
Enhance your proficiency in querying data lakes and data warehouses by completing practice exercises, improving your analytical skills and data retrieval techniques.
Browse courses on
Data Queries
Show steps
Show steps
Show steps
- Find online resources or practice platforms that offer data lake and data warehouse querying exercises.
- Attempt the exercises, focusing on optimizing your queries for performance and efficiency.
- Analyze the results of your queries to ensure accurate data retrieval.
Contribute to an open-source data pipeline project
Show steps
Contributing to an open-source data pipeline project will help you learn faster about Data Lakes and Data Warehouses during the course
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find an open-source data pipeline project that you are interested in contributing to
- Fork the project and make changes
- Submit a pull request
- Respond to feedback and iterate on your changes
Design a data pipeline
Show steps
Designing a data pipeline will help you learn faster how to work with Data Lakes and Data Warehouses during the course
Browse courses on
Data Warehousing
Show steps
Show steps
Show steps
- Identify the data sources and the data to be processed
- Design the data pipeline architecture
- Implement the data pipeline using GCP tools
Build a Data Pipeline Prototype
Show steps
Solidify your understanding of data pipelines by designing and building a prototype in the Google Cloud Platform, applying the principles and technologies covered in the course.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Define the purpose and scope of your data pipeline prototype.
- Choose appropriate data sources and data transformation tools.
- Implement the data pipeline in the Google Cloud Platform, utilizing its data processing and storage services.
- Test and evaluate the performance of your prototype.
Build a data pipeline using Google Cloud Platform
Show steps
Building a data pipeline using Google Cloud Platform will help you learn faster how to work with Data Lakes and Data Warehouses during the course
Show steps
Show steps
Show steps
- Set up a GCP project
- Create a data pipeline using GCP tools
- Deploy the data pipeline
- Monitor the data pipeline
Volunteer at a Data-Driven Organization
Show steps
Gain practical experience and contribute to real-world projects by volunteering at an organization that utilizes data-driven decision-making, enhancing your understanding of data application and its impact.
Browse courses on
Data Analytics
Show steps
Show steps
Show steps
- Identify organizations in your area that align with your interests and utilize data.
- Contact the organizations and inquire about volunteer opportunities.
- Contribute to data-related projects, such as data analysis, visualization, or machine learning.
Career center
Learners who complete Modernizing Data Lakes and Data Warehouses with GCP auf Deutsch will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
A Data Engineer is responsible for designing, building, and maintaining data pipelines that collect, store, and process data. This course provides a solid foundation in data lakes and data warehouses, which are essential components of any data pipeline. By understanding how to use these technologies, Data Engineers can build more efficient and effective pipelines that can handle the large volumes of data that are generated by today's businesses.
Data Architect
Data Architect
A Data Architect is responsible for designing and managing the overall data architecture of an organization. This includes planning for data storage, data processing, and data security. This course provides a comprehensive overview of data lakes and data warehouses, which are two of the most important technologies in any data architecture. By understanding how to use these technologies, Data Architects can design more scalable and secure data architectures that can meet the needs of their organizations.
Data Scientist
Data Scientist
A Data Scientist is responsible for using data to solve business problems. This includes collecting data, cleaning data, analyzing data, and building models. This course provides a foundation in data lakes and data warehouses, which are essential for storing and managing the large volumes of data that Data Scientists need to work with. By understanding how to use these technologies, Data Scientists can access and analyze data more efficiently, which can lead to better insights and more accurate predictions.
Database Administrator
Database Administrator
A Database Administrator is responsible for managing and maintaining databases. This includes creating databases, managing user access, and backing up data. This course provides a solid foundation in data lakes and data warehouses, which are two of the most important types of databases. By understanding how to use these technologies, Database Administrators can manage and maintain databases more effectively.
Business Analyst
Business Analyst
A Business Analyst is responsible for understanding the business needs of an organization and translating those needs into technical requirements. This course provides a foundation in data lakes and data warehouses, which are two of the most important technologies for storing and managing data. By understanding how to use these technologies, Business Analysts can better understand the data needs of their organizations and can develop more effective data solutions.
Software Engineer
Software Engineer
A Software Engineer is responsible for designing, developing, and maintaining software applications. This course provides a foundation in data lakes and data warehouses, which are two of the most important technologies for storing and managing data. By understanding how to use these technologies, Software Engineers can build more scalable and efficient software applications that can handle the large volumes of data that are generated by today's businesses.
Cloud Engineer
Cloud Engineer
A Cloud Engineer is responsible for designing, building, and maintaining cloud-based infrastructure. This course provides a foundation in data lakes and data warehouses, which are two of the most important technologies for storing and managing data in the cloud. By understanding how to use these technologies, Cloud Engineers can build more scalable and efficient cloud-based infrastructure that can meet the needs of their organizations.
Data Analyst
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data. This course provides a foundation in data lakes and data warehouses, which are two of the most important technologies for storing and managing data. By understanding how to use these technologies, Data Analysts can access and analyze data more efficiently, which can lead to better insights and more informed decision-making.
Information Security Analyst
Information Security Analyst
An Information Security Analyst is responsible for protecting an organization's data from unauthorized access, use, disclosure, disruption, modification, or destruction. This course provides a foundation in data lakes and data warehouses, which are two of the most important technologies for storing and managing data. By understanding how to use these technologies, Information Security Analysts can better protect their organizations' data from cyber threats.
Project Manager
Project Manager
A Project Manager is responsible for planning, executing, and closing projects. This course provides a foundation in data lakes and data warehouses, which are two of the most important technologies for storing and managing data. By understanding how to use these technologies, Project Managers can better plan and execute projects that involve data.
Product Manager
Product Manager
A Product Manager is responsible for developing and managing products. This course provides a foundation in data lakes and data warehouses, which are two of the most important technologies for storing and managing data. By understanding how to use these technologies, Product Managers can better understand the data needs of their users and can develop more successful products.
Marketing Manager
Marketing Manager
A Marketing Manager is responsible for planning and executing marketing campaigns. This course provides a foundation in data lakes and data warehouses, which are two of the most important technologies for storing and managing data. By understanding how to use these technologies, Marketing Managers can better target their marketing campaigns and can measure the effectiveness of their campaigns.
Sales Manager
Sales Manager
A Sales Manager is responsible for leading and managing a sales team. This course provides a foundation in data lakes and data warehouses, which are two of the most important technologies for storing and managing data. By understanding how to use these technologies, Sales Managers can better understand the sales needs of their customers and can develop more effective sales strategies.
Customer Success Manager
Customer Success Manager
A Customer Success Manager is responsible for ensuring that customers are successful with a company's products or services. This course provides a foundation in data lakes and data warehouses, which are two of the most important technologies for storing and managing data. By understanding how to use these technologies, Customer Success Managers can better understand the needs of their customers and can develop more effective strategies for helping them succeed.
Operations Manager
Operations Manager
An Operations Manager is responsible for overseeing the day-to-day operations of an organization. This course provides a foundation in data lakes and data warehouses, which are two of the most important technologies for storing and managing data. By understanding how to use these technologies, Operations Managers can better understand the data needs of their organization and can develop more efficient and effective operations.
For more career information including salaries, visit:
OpenCourser.com/course/ah2iun/modernizing
Reading list
We've selected ten 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 auf Deutsch.
Provides a comprehensive guide to preparing for the Google Cloud Data Engineering certification.
Save
How to make your data accessible and usable in the age of the data mesh.
Provides a deep dive into the design and implementation of data-intensive applications.
Provides a comprehensive overview of data science concepts and techniques.
Bridges the business and technology sides of data warehousing. Serves to help verify your data lake setup and use.
Save
Serves as a reference book that starts with data analytics basics, then discusses advanced data analytics.
Provides a practical guide to designing and implementing data lakes.
Serves as a guide for migrating to a data lake architecture. Book discusses common and uncommon use cases of data lakes, and how to overcome hurdles.
Provides a comprehensive overview of data warehousing concepts and technologies.
Advance your understanding of data management and data warehousing engineering for greater efficiency and better results.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ah2iun/modernizing
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Time
12 hours
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
This course belongs to a
collection
called
Data Engineer, Big Data and ML on Google Cloud auf Deutsch. It's comprised of five courses:
- Building Batch Data Pipelines on GCP auf Deutsch
- Modernizing Data Lakes and Data Warehouses with GCP auf Deutsch (viewing)
- Building Resilient Streaming Analytics Systems on GCP auf Deutsch
- Smart Analytics, Machine Learning, and AI on GCP auf Deutsch
- Google Cloud Platform Big Data and Machine Learning Fundamentals auf Deutsch
Good to know
Develops skills essential for working with Data Lakes and Data Warehouses, which are increasingly necessary in data-driven decision making
Provides a detailed comparison of Data Lake and Data Warehouse solutions available in Google Cloud Platform, offering insights for selecting the right solution for specific needs
Explores industry-standard data storage solutions, making the course highly relevant for professionals in data-related fields
Taught by Google Cloud Training, who are recognized for their expertise in cloud computing and data management
Offers hands-on practice through Qwiklabs, allowing learners to apply concepts and reinforce understanding
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