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Google Cloud Training
Ce cours intensif à la demande, d'une durée d'une semaine, complète le cours Big Data and Machine Learning Fundamentals de Google Cloud Platform (GCP). À travers un ensemble de présentations, de démonstrations et d'ateliers pratiques conduits par un formateur...
Read more
Ce cours intensif à la demande, d'une durée d'une semaine, complète le cours Big Data and Machine Learning Fundamentals de Google Cloud Platform (GCP). À travers un ensemble de présentations, de démonstrations et d'ateliers pratiques conduits par un formateur...
Read more
Ce cours intensif à la demande, d'une durée d'une semaine, complète le cours Big Data and Machine Learning Fundamentals de Google Cloud Platform (GCP). À travers un ensemble de présentations, de démonstrations et d'ateliers pratiques conduits par un formateur, les élèves apprendront à gérer l'entreposage, l'analyse et le traitement des pipelines de données no-ops.
Conditions préalables :
• Cours Google Cloud Platform Big Data and Machine Learning Fundamentals
• Expérience de l'utilisation d'un langage de requête de type SQL pour l'analyse de données
• Connaissance de Python ou de Java
Remarque concernant les comptes Google :
• Pour le moment, les services Google ne sont pas disponibles en Chine.
Register for this course and see more details by visiting:
OpenCourser.com/course/pocn17/serverless
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Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops data engineering pipelines, which are core skills for data engineering
Taught by Google Cloud Training, who are recognized for their work in cloud computing
Examines no-ops architecture, which is highly relevant to improving performance and reducing costs of data processing
Requires experience with SQL and Python or Java, indicating it is appropriate for intermediate learners
Assumes a prerequisite in Big Data and Machine Learning Fundamentals, which may limit accessibility
Save this course
Save Serverless Data Analysis with Google BigQuery and Cloud Dataflow en Français to your list so you can find it easily later:
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Reviews summary
Adequate examples
This course does an okay job of providing examples for students to learn from. Students who feel that this course is lacking in examples may want to look at other resources for supplemental help.
Could use more examples.
"...More examples DataPrep..."
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 Serverless Data Analysis with Google BigQuery and Cloud Dataflow en Français with these
activities:
Review Python or Java Basics
Show steps
Refreshing your Python or Java basics will help you ensure that you have a solid foundation for this course.
Browse courses on
Python
Show steps
-
Read through your notes or a tutorial on Python or Java basics.
-
Complete practice problems or exercises on Python or Java.
Attend industry meetups and conferences
Show steps
Expand your network and gain insights by connecting with professionals in the field.
Show steps
-
Identify relevant industry meetups and conferences
-
Attend these events and actively participate in discussions
-
Connect with other attendees and exchange knowledge
Review SQL for Data Analysis
Show steps
Reviewing SQL will help you solidify your understanding of the fundamentals of data analysis, which is essential for this course.
Browse courses on
SQL
Show steps
-
Read through your SQL notes or a SQL tutorial.
-
Complete practice problems or exercises on SQL.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Read 'Data Pipelines Pocket Reference' by James Densmore
Show steps
This book provides a comprehensive overview of data pipelines, which will help you understand the concepts covered in this course.
View
Data Pipelines Pocket Reference: Moving and...
on Amazon
Show steps
-
Read the book.
-
Take notes or highlight important sections.
Review NoOps architecture
Show steps
Refine your understanding of NoOps architecture and its implementation, which will be covered extensively in the course.
Browse courses on
NoOps
Show steps
-
Revisit NoOps concepts and principles
-
Review best practices for designing and implementing NoOps solutions
-
Explore case studies and examples of NoOps in practice
Join a Study Group or Participate in Discussion Forums
Show steps
Engaging with other students in study groups or discussion forums can help you clarify concepts and get different perspectives.
Show steps
-
Find a study group or discussion forum.
-
Participate regularly.
Practice Data Manipulation in Python or Java
Show steps
Practicing data manipulation in Python or Java will help you develop the skills you need to work with data in this course.
Browse courses on
Data Manipulation
Show steps
-
Find a dataset online or create your own.
-
Use Python or Java to clean and manipulate the data.
-
Analyze the data and draw conclusions.
Execute hands-on data pipeline exercises
Show steps
Reinforce your understanding of data pipeline management by completing practical exercises.
Show steps
-
Set up a data pipeline using Google Cloud Platform (GCP) tools
-
Ingest, transform, and analyze data using GCP services
-
Monitor and troubleshoot data pipelines
Create a Data Engineering Resources Compilation
Show steps
Creating a compilation of data engineering resources will help you organize and access the materials you need for this course and beyond.
Browse courses on
Data Engineering
Show steps
-
Gather resources such as articles, tutorials, and videos.
-
Organize the resources into categories or topics.
-
Share the compilation with other students or the instructor.
Design and implement a data pipeline solution
Show steps
Demonstrate your proficiency in data pipeline management by creating and implementing a comprehensive solution.
Show steps
-
Define the requirements and scope of the data pipeline
-
Design the architecture and components of the pipeline
-
Implement the pipeline using GCP tools and services
-
Test, evaluate, and deploy the pipeline
Explore advanced data analytics techniques
Show steps
Expand your knowledge of data analytics by following guided tutorials on specialized techniques.
Show steps
-
Identify and select appropriate advanced data analytics techniques
-
Follow tutorials to implement these techniques using GCP tools
-
Apply these techniques to analyze real-world data sets
Build a Simple Data Pipeline
Show steps
Building a simple data pipeline will help you apply the concepts you learn in this course to a real-world scenario.
Browse courses on
Data Pipelines
Show steps
-
Design the data pipeline.
-
Implement the data pipeline using a tool like Apache Beam or Airflow.
-
Test and deploy the data pipeline.
Follow Tutorials on Advanced Data Engineering Techniques
Show steps
Following tutorials on advanced data engineering techniques will help you expand your knowledge and skills beyond the scope of this course.
Browse courses on
Data Engineering
Show steps
-
Identify areas where you want to improve your knowledge.
-
Find tutorials on those topics.
-
Complete the tutorials and apply what you learn.
Contribute to an Open-Source Data Engineering Project
Show steps
Contributing to an open-source data engineering project will give you hands-on experience and allow you to learn from others in the field.
Browse courses on
Open Source
Show steps
-
Find an open-source data engineering project that interests you.
-
Identify an area where you can contribute.
-
Make a pull request with your contribution.
Review Python or Java Basics
Show steps
Refreshing your Python or Java basics will help you ensure that you have a solid foundation for this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Read through your notes or a tutorial on Python or Java basics.
- Complete practice problems or exercises on Python or Java.
Attend industry meetups and conferences
Show steps
Expand your network and gain insights by connecting with professionals in the field.
Show steps
Show steps
Show steps
- Identify relevant industry meetups and conferences
- Attend these events and actively participate in discussions
- Connect with other attendees and exchange knowledge
Review SQL for Data Analysis
Show steps
Reviewing SQL will help you solidify your understanding of the fundamentals of data analysis, which is essential for this course.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Read through your SQL notes or a SQL tutorial.
- Complete practice problems or exercises on SQL.
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Read 'Data Pipelines Pocket Reference' by James Densmore
Show steps
This book provides a comprehensive overview of data pipelines, which will help you understand the concepts covered in this course.
View
Data Pipelines Pocket Reference: Moving and...
on Amazon
Show steps
Show steps
Show steps
- Read the book.
- Take notes or highlight important sections.
Review NoOps architecture
Show steps
Refine your understanding of NoOps architecture and its implementation, which will be covered extensively in the course.
Browse courses on
NoOps
Show steps
Show steps
Show steps
- Revisit NoOps concepts and principles
- Review best practices for designing and implementing NoOps solutions
- Explore case studies and examples of NoOps in practice
Join a Study Group or Participate in Discussion Forums
Show steps
Engaging with other students in study groups or discussion forums can help you clarify concepts and get different perspectives.
Show steps
Show steps
Show steps
- Find a study group or discussion forum.
- Participate regularly.
Practice Data Manipulation in Python or Java
Show steps
Practicing data manipulation in Python or Java will help you develop the skills you need to work with data in this course.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Find a dataset online or create your own.
- Use Python or Java to clean and manipulate the data.
- Analyze the data and draw conclusions.
Execute hands-on data pipeline exercises
Show steps
Reinforce your understanding of data pipeline management by completing practical exercises.
Show steps
Show steps
Show steps
- Set up a data pipeline using Google Cloud Platform (GCP) tools
- Ingest, transform, and analyze data using GCP services
- Monitor and troubleshoot data pipelines
Create a Data Engineering Resources Compilation
Show steps
Creating a compilation of data engineering resources will help you organize and access the materials you need for this course and beyond.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Gather resources such as articles, tutorials, and videos.
- Organize the resources into categories or topics.
- Share the compilation with other students or the instructor.
Design and implement a data pipeline solution
Show steps
Demonstrate your proficiency in data pipeline management by creating and implementing a comprehensive solution.
Show steps
Show steps
Show steps
- Define the requirements and scope of the data pipeline
- Design the architecture and components of the pipeline
- Implement the pipeline using GCP tools and services
- Test, evaluate, and deploy the pipeline
Explore advanced data analytics techniques
Show steps
Expand your knowledge of data analytics by following guided tutorials on specialized techniques.
Show steps
Show steps
Show steps
- Identify and select appropriate advanced data analytics techniques
- Follow tutorials to implement these techniques using GCP tools
- Apply these techniques to analyze real-world data sets
Build a Simple Data Pipeline
Show steps
Building a simple data pipeline will help you apply the concepts you learn in this course to a real-world scenario.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Design the data pipeline.
- Implement the data pipeline using a tool like Apache Beam or Airflow.
- Test and deploy the data pipeline.
Follow Tutorials on Advanced Data Engineering Techniques
Show steps
Following tutorials on advanced data engineering techniques will help you expand your knowledge and skills beyond the scope of this course.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Identify areas where you want to improve your knowledge.
- Find tutorials on those topics.
- Complete the tutorials and apply what you learn.
Contribute to an Open-Source Data Engineering Project
Show steps
Contributing to an open-source data engineering project will give you hands-on experience and allow you to learn from others in the field.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find an open-source data engineering project that interests you.
- Identify an area where you can contribute.
- Make a pull request with your contribution.
Career center
Learners who complete Serverless Data Analysis with Google BigQuery and Cloud Dataflow en Français will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists develop and apply machine learning algorithms to solve business problems. They typically have a strong background in statistics, machine learning, and programming. Data Scientists often work with Data Engineers to build data pipelines and with Data Analysts to interpret results. This course may be useful for Data Scientists as it provides a good foundation in using BigQuery and Cloud Dataflow, two popular tools for data analysis and processing.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to identify trends, patterns, and opportunities for businesses. They work with stakeholders to translate data into actionable insights that can help drive decision-making. This course may be useful for Business Intelligence Analysts as it provides a strong foundation in using BigQuery and Cloud Dataflow for data analysis and processing.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop, deploy, and maintain machine learning models. They work closely with Data Scientists and Data Engineers to bring machine learning models into production. This course may be useful for someone seeking to build a career as a Machine Learning Engineer, as it provides a good foundation in using BigQuery and Cloud Dataflow for data analysis and processing.
Data Analyst
Data Analyst
Data Analysts use their knowledge of data mining, statistics, and machine learning to translate raw data into valuable insights and predictions. Businesses may employ Data Analysts for initiatives such as market research, risk management, customer segmentation, and fraud detection. This course may be useful for someone seeking this career path as it introduces the use of SQL, Python, and Java in data analysis pipelines.
Data Architect
Data Architect
Data Architects design and implement data management solutions. They work with stakeholders to understand data needs and develop strategies for meeting those needs. This course may be useful for Data Architects as it provides a good foundation in using BigQuery and Cloud Dataflow for data analysis and processing.
Data Warehouse Manager
Data Warehouse Manager
Data Warehouse Managers design, implement, and maintain data warehouses. They work with stakeholders to understand data needs and develop strategies for meeting those needs. This course may be useful for Data Warehouse Managers as it provides a good foundation in using BigQuery, a popular cloud-based data warehouse.
Data Consultant
Data Consultant
Data Consultants help organizations to understand and use data. They work with stakeholders to identify data needs, develop data strategies, and implement data solutions. This course may be useful for Data Consultants as it provides a good foundation in using BigQuery and Cloud Dataflow for data analysis and processing.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain the infrastructure responsible for managing and processing data. They may work with data scientists to develop pipelines and provide data to data analysts, data scientists, and business stakeholders. This course may be useful for someone looking to learn more about building and managing data pipelines.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. They ensure that databases are running smoothly and that data is secure. This course may be useful for Database Administrators as it provides a good foundation in using BigQuery, a popular cloud-based database.
Cloud Architect
Cloud Architect
Cloud Architects design and implement cloud computing solutions. They work with stakeholders to understand cloud needs and develop strategies for meeting those needs. This course may be useful for Cloud Architects as it provides a good foundation in using BigQuery and Cloud Dataflow, two popular cloud-based data analysis and processing tools.
Data Quality Manager
Data Quality Manager
Data Quality Managers ensure that data is accurate, complete, and consistent. They work with stakeholders to develop data quality standards and implement processes to ensure that data meets those standards. This course may be useful for Data Quality Managers as it provides a good foundation in using BigQuery and Cloud Dataflow for data analysis and processing.
Data Privacy Manager
Data Privacy Manager
Data Privacy Managers ensure that data is collected, used, and stored in a compliant manner. They work with stakeholders to develop data privacy policies and implement processes to ensure that data is protected. This course may be useful for Data Privacy Managers as it provides a good foundation in using BigQuery and Cloud Dataflow for data analysis and processing.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They may work on a variety of projects, from developing new features for existing applications to creating entirely new applications. This course may be useful for Software Engineers who want to learn more about using BigQuery and Cloud Dataflow for data analysis and processing.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. They work with stakeholders to define project scope, develop project plans, and manage project risks. This course may be useful for Project Managers as it provides a good foundation in using BigQuery and Cloud Dataflow for data analysis and processing.
For more career information including salaries, visit:
OpenCourser.com/course/pocn17/serverless
Reading list
We've selected 11 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
Serverless Data Analysis with Google BigQuery and Cloud Dataflow en Français.
Provides a comprehensive overview of Google BigQuery, covering topics such as data ingestion, data querying, and data analysis. It valuable resource for learners who want to gain a deep understanding of BigQuery and its capabilities.
Covers machine learning concepts and techniques using GCP's machine learning services. It useful resource for learners who want to learn how to build and deploy machine learning models on GCP.
Covers SQL fundamentals and best practices for data analysis. It helpful resource for learners who want to improve their SQL skills and become more proficient in data querying.
Provides a comprehensive overview of Python libraries and tools for data analysis. It valuable resource for learners who want to learn how to use Python for data manipulation, data visualization, and machine learning.
Save
Covers Java libraries and tools for data analysis. It useful resource for learners who want to learn how to use Java for data manipulation, data visualization, and machine learning.
Save
Provides a comprehensive overview of R libraries and tools for data analysis. It valuable resource for learners who want to learn how to use R for data manipulation, data visualization, and machine learning.
Covers data science concepts and techniques in a business context. It useful resource for learners who want to understand how data science can be applied to solve business problems.
Provides a comprehensive overview of Big Data analytics concepts and techniques. It helpful resource for learners who are new to Big Data and want to gain a foundational understanding of the field.
Covers artificial intelligence concepts and techniques in depth. It comprehensive resource for learners who want to gain a deep understanding of the field.
Covers deep learning concepts and techniques using Python. It valuable resource for learners who want to learn how to build and train deep learning models.
Covers Big Data architecture and design principles. It valuable resource for learners who want to understand how to build and manage Big Data systems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/pocn17/serverless
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Time
8 hours
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
This course belongs to a
collection
called
Data Engineering on Google Cloud Platform en Français. It's comprised of five courses:
- Building Resilient Streaming Systems on Google Cloud Platform en Français
- Google Cloud Big Data and Machine Learning Fundamentals en Français
- Serverless Data Analysis with Google BigQuery and Cloud Dataflow en Français (viewing)
- Leveraging Unstructured Data with Cloud Dataproc on Google Cloud Platform en Français
- Serverless Machine Learning with Tensorflow on Google Cloud Platform en Français
Good to know
Develops data engineering pipelines, which are core skills for data engineering
Taught by Google Cloud Training, who are recognized for their work in cloud computing
Examines no-ops architecture, which is highly relevant to improving performance and reducing costs of data processing
Requires experience with SQL and Python or Java, indicating it is appropriate for intermediate learners
Assumes a prerequisite in Big Data and Machine Learning Fundamentals, which may limit accessibility
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