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Google Cloud Training
Datenpipelines folgen in der Regel dem Muster Extrahieren und Laden (EL), Extrahieren, Laden und Transformieren (ELT) oder Extrahieren, Transformieren und Laden (ETL). Im Kurs wird beschrieben, welcher Ansatz in welcher Situation für Batchdaten geeignet ist....
Read more
Datenpipelines folgen in der Regel dem Muster Extrahieren und Laden (EL), Extrahieren, Laden und Transformieren (ELT) oder Extrahieren, Transformieren und Laden (ETL). Im Kurs wird beschrieben, welcher Ansatz in welcher Situation für Batchdaten geeignet ist....
Read more
Datenpipelines folgen in der Regel dem Muster Extrahieren und Laden (EL), Extrahieren, Laden und Transformieren (ELT) oder Extrahieren, Transformieren und Laden (ETL). Im Kurs wird beschrieben, welcher Ansatz in welcher Situation für Batchdaten geeignet ist. Außerdem werden verschiedene Technologien der Google Cloud Platform zur Datentransformation behandelt, wie BigQuery, das Ausführen von Spark in Cloud Dataproc, Pipelinediagramme in Cloud Data Fusion und die serverlose Datenverarbeitung mit Cloud Dataflow. In Qwiklabs erstellen die Kursteilnehmer dann selbst Komponenten einer Datenpipeline in der Google Cloud Platform.
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what to watch for , and
possible dealbreakers
Examines multiple data processing and transformation techniques suited for batch data
Delves into different data transformation technologies available on Google Cloud Platform
Provides hands-on experience in setting up a data pipeline on Google Cloud Platform using Qwiklabs
Suitable for learners with a basic understanding of data pipelines and cloud computing concepts
Assumes learners have prior knowledge of cloud platforms such as 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 Building Batch Data Pipelines on GCP auf Deutsch with these
activities:
Review the course syllabus and readings
Show steps
Get a head start on the course by reviewing the syllabus and readings.
Show steps
-
Read the course syllabus.
-
Skim the required readings for the first few weeks of the course.
Review basic data modeling concepts
Show steps
Refresh your understanding of data modeling to ensure you have a strong foundation for the course.
Browse courses on
Data Modeling
Show steps
-
Review your notes from a previous course on data modeling.
-
Work through a few practice problems on data modeling.
Complete the Google Cloud Platform Data Engineering Fundamentals quest
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Deepen your understanding of data engineering fundamentals on Google Cloud Platform.
Browse courses on
Google Cloud Platform
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-
Follow the step-by-step instructions in the quest.
-
Complete the hands-on exercises in the quest.
Four other activities
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Show all seven activities
Solve data transformation problems using BigQuery
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Practice writing SQL queries to transform data in BigQuery.
Browse courses on
BigQuery
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-
Find a dataset on BigQuery Public Data.
-
Write a SQL query to transform the data.
-
Test your query and make sure it returns the expected results.
Attend a workshop on data engineering
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Expand your knowledge of data engineering by attending a workshop.
Browse courses on
Data Engineering
Show steps
-
Find a workshop that covers topics relevant to the course.
-
Register for the workshop.
-
Attend the workshop and participate actively.
Build a data pipeline using Cloud Dataflow
Show steps
Gain hands-on experience building a data pipeline using Cloud Dataflow.
Browse courses on
Cloud Dataflow
Show steps
-
Design your data pipeline.
-
Write the code for your data pipeline.
-
Deploy your data pipeline.
-
Monitor your data pipeline.
Help other students in the course
Show steps
Reinforce your understanding of the course material by helping other students.
Show steps
-
Answer questions in the course discussion forum.
-
Participate in study groups.
Review the course syllabus and readings
Show steps
Get a head start on the course by reviewing the syllabus and readings.
Show steps
Show steps
Show steps
- Read the course syllabus.
- Skim the required readings for the first few weeks of the course.
Review basic data modeling concepts
Show steps
Refresh your understanding of data modeling to ensure you have a strong foundation for the course.
Browse courses on
Data Modeling
Show steps
Show steps
Show steps
- Review your notes from a previous course on data modeling.
- Work through a few practice problems on data modeling.
Complete the Google Cloud Platform Data Engineering Fundamentals quest
Show steps
Deepen your understanding of data engineering fundamentals on Google Cloud Platform.
Browse courses on
Google Cloud Platform
Show steps
Show steps
Show steps
- Follow the step-by-step instructions in the quest.
- Complete the hands-on exercises in the quest.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Solve data transformation problems using BigQuery
Show steps
Practice writing SQL queries to transform data in BigQuery.
Browse courses on
BigQuery
Show steps
Show steps
Show steps
- Find a dataset on BigQuery Public Data.
- Write a SQL query to transform the data.
- Test your query and make sure it returns the expected results.
Attend a workshop on data engineering
Show steps
Expand your knowledge of data engineering by attending a workshop.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Find a workshop that covers topics relevant to the course.
- Register for the workshop.
- Attend the workshop and participate actively.
Build a data pipeline using Cloud Dataflow
Show steps
Gain hands-on experience building a data pipeline using Cloud Dataflow.
Browse courses on
Cloud Dataflow
Show steps
Show steps
Show steps
- Design your data pipeline.
- Write the code for your data pipeline.
- Deploy your data pipeline.
- Monitor your data pipeline.
Help other students in the course
Show steps
Reinforce your understanding of the course material by helping other students.
Show steps
Show steps
Show steps
- Answer questions in the course discussion forum.
- Participate in study groups.
Career center
Learners who complete Building Batch Data Pipelines on GCP auf Deutsch will develop knowledge and skills
that may be useful to these careers:
Big Data Engineer
Big Data Engineer
Big data engineers design and implement systems to manage and process large volumes of data. They work with businesses to understand their needs, and with data engineers to develop systems to meet those needs. Big data engineers need to have a strong understanding of data pipelines, as they are often responsible for designing and implementing the systems that move data between different systems.
Data Engineer
Data Engineer
Data engineers design, build, and maintain solutions to manage and process large volumes of data. This often involves designing the architecture of a data management system, as well as writing code to extract, transform, and load data into a data warehouse, data lake, or other data storage system. This course can help data engineers understand data pipelines.
Data Scientist
Data Scientist
Data scientists are responsible for developing and applying statistical and machine learning models to large datasets. They work with businesses to help them make decisions based on data. Data scientists need to have a strong understanding of data pipelines, as they are often responsible for designing and implementing the systems that move data between different systems.
Data Architect
Data Architect
Data architects design and implement the systems that manage and process data. They work with businesses to understand their needs, and with data engineers to develop systems to meet those needs. Data architects need to have a strong understanding of data pipelines, as they are often responsible for designing and implementing the systems that move data between different systems.
Data Integration Engineer
Data Integration Engineer
Data integration engineers design and implement systems to integrate data from different sources. They work with businesses to understand their needs, and with data engineers to develop systems to meet those needs. Data integration engineers need to have a strong understanding of data pipelines, as they are often responsible for designing and implementing the systems that move data between different systems.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers are responsible for building, deploying, and maintaining machine learning models. They work with data scientists to develop models, and with software engineers to deploy models into production. Machine learning engineers need to have a strong understanding of data pipelines, as they are often responsible for designing and implementing the systems that move data between different systems.
Data Analyst
Data Analyst
In today's data-driven world, Data Analysts help businesses make sense of the vast amount of information they gather from a wide variety of sources. They are responsible for collecting, cleaning, and analyzing data. The course is particularly useful if one wishes to enter in this field as it covers the 3 data pipeline patterns: EL, ELT, and ETL.
Cloud Architect
Cloud Architect
Cloud architects design and implement cloud computing solutions. They work with businesses to understand their needs, and with cloud engineers to develop solutions to meet those needs. Cloud architects need to have a strong understanding of data pipelines, as they are often responsible for designing and implementing the systems that move data between different systems.
Data Governance Analyst
Data Governance Analyst
Data governance analysts work with businesses to develop and implement data governance policies and procedures. They work with businesses to understand their needs, and with data engineers to develop systems to meet those needs. Data governance analysts need to have a strong understanding of data pipelines, as they are often responsible for designing and implementing the systems that move data between different systems.
Data Privacy Analyst
Data Privacy Analyst
Data privacy analysts work with businesses to develop and implement data privacy policies and procedures. They work with businesses to understand their needs, and with data engineers to develop systems to meet those needs. Data privacy analysts need to have a strong understanding of data pipelines, as they are often responsible for designing and implementing the systems that move data between different systems.
Business Analyst
Business Analyst
Business analysts help businesses understand their data and make better decisions. They work with businesses to identify their needs, and with data scientists to develop models to meet those needs. Business analysts need to have a strong understanding of data pipelines, as they are often responsible for designing and implementing the systems that move data between different systems.
DevOps Engineer
DevOps Engineer
DevOps engineers work with developers and operations teams to ensure that software is developed and deployed quickly and efficiently. They work with businesses to understand their needs, and with developers and operations teams to develop and deploy software to meet those needs. DevOps engineers need to have a strong understanding of data pipelines, as they are often responsible for designing and implementing the systems that move data between different systems.
Database Administrator (DBA)
Database Administrator (DBA)
Database administrators (DBAs) are responsible for ensuring the performance, availability, and security of an organization IT environment. They work with database software and hardware, as well as with other IT professionals, to ensure that data is always accessible and secure. This course will also be helpful for an aspiring DBA as they need to know about data pipelines.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software applications. They work with businesses to help them achieve their goals through the use of technology. Software engineers need to have a strong understanding of data pipelines, as they are often responsible for designing and implementing the systems that move data between different systems.
For more career information including salaries, visit:
OpenCourser.com/course/o4ob4q/building
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
Building Batch Data Pipelines on GCP auf Deutsch.
Covers the basics of machine learning and provides some insights into how machine learning can be used for data engineering tasks, which are not covered by the course.
Provides a comprehensive overview of Hadoop, which popular open-source framework for big data processing, but it is not covered by the course.
Save
Provides a high-level overview of data-intensive applications and their design patterns. It could provide useful background knowledge for the course.
Provides a practical guide to data engineering on Google Cloud Platform. It covers some of the same topics as the course, but it has a different focus and is more hands-on.
Provides a practical guide to machine learning engineering with Python. It covers some of the same topics as the course, but it has a different focus and is more hands-on.
Provides a gentle introduction to data science. It could be helpful for learners who are new to the field and want to get a basic understanding of data pipelines.
Provides a practical guide to building RESTful APIs with ASP.NET Core 3. It is not directly related to data pipelines, but it could be helpful for learners who want to develop web applications that interact with data pipelines.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/o4ob4q/building
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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 (viewing)
- Modernizing Data Lakes and Data Warehouses with GCP auf Deutsch
- 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
Examines multiple data processing and transformation techniques suited for batch data
Delves into different data transformation technologies available on Google Cloud Platform
Provides hands-on experience in setting up a data pipeline on Google Cloud Platform using Qwiklabs
Suitable for learners with a basic understanding of data pipelines and cloud computing concepts
Assumes learners have prior knowledge of cloud platforms such as Google Cloud Platform
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