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Google Cloud Training
This is a self-paced lab that takes place in the Google Cloud console. In this lab, you implement discovery and protection for sensitive data in BigQuery using Sensitive Data Protection.
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What's inside
Syllabus
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Read about what's good
what should give you pause and
possible dealbreakers
Uses Sensitive Data Protection, which helps learners discover and protect sensitive data within BigQuery, a critical aspect of modern data governance
Takes place in the Google Cloud console, providing a hands-on experience within the Google Cloud ecosystem, which is valuable for practical application
Presented by Google Cloud, which is recognized for its innovative cloud solutions and contributions to data security and governance
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Reviews summary
Applying sensitive data protection in bigquery
According to learners, this lab provides a practical, hands-on introduction to using Sensitive Data Protection within BigQuery. Students generally find the lab instructions clear and appreciate the opportunity to directly apply concepts in the Google Cloud console. While many find it a useful starting point for discovering sensitive data, some note potential issues with the lab environment setup or wish for more advanced scenarios. Overall, it is seen as a valuable, focused exercise for understanding basic SDP functionality, particularly for those new to the tool.
Good for beginners, less for advanced
"Good overview, but maybe too basic if you know GCP already."
"A solid starting point for SDP discovery."
"I was hoping for more depth or complex examples."
"Suitable for beginners looking for a first look at SDP."
Easy to follow step-by-step
"Steps were clear and easy to follow."
"The instructions were very precise."
"I found the guidance provided in the lab easy to understand."
"Well-written steps made completing the lab straightforward."
Practical application is key
"The lab is the best part, lets you actually do it."
"Gave me practical experience with SDP."
"Great hands-on practice using Sensitive Data Protection."
"Applying the concepts directly in the console made a big difference."
Some technical glitches encountered
"Had some trouble getting the lab environment set up."
"Encountered a minor technical issue during the lab..."
"The lab setup was a bit finicky for me."
"A few steps in the environment seemed unstable at times."
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 Enabling Sensitive Data Protection Discovery for BigQuery with these
activities:
Review BigQuery Fundamentals
Show steps
Reviewing BigQuery fundamentals ensures a solid foundation for understanding sensitive data discovery within the platform.
Show steps
-
Review BigQuery documentation on data warehousing concepts.
-
Practice writing basic SQL queries in BigQuery.
-
Explore BigQuery's data loading and exporting capabilities.
Review 'BigQuery: The Definitive Guide'
Show steps
Reviewing this book provides a deeper understanding of BigQuery, which is essential for effectively using Sensitive Data Protection within the platform.
View
Google BigQuery: The Definitive Guide: Data...
on Amazon
Show steps
-
Read the chapters on BigQuery architecture and features.
-
Take notes on key concepts and definitions.
-
Reflect on how these features relate to Sensitive Data Protection.
Read 'Data Privacy: Principles and Practice'
Show steps
Reading this book provides a broader understanding of data privacy principles, which are essential for effectively using Sensitive Data Protection.
View
Putting Knowledge to Work
on Amazon
Show steps
-
Read the chapters on data governance and compliance.
-
Take notes on key concepts and definitions.
-
Reflect on how these principles apply to BigQuery data.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Google Cloud's Sensitive Data Protection Tutorials
Show steps
Following Google Cloud's tutorials provides hands-on experience with Sensitive Data Protection features and configurations.
Show steps
-
Find the official Google Cloud documentation for Sensitive Data Protection.
-
Work through the tutorials on data masking and de-identification.
-
Experiment with different configuration options.
Document Sensitive Data Discovery Process
Show steps
Creating documentation reinforces understanding and provides a valuable resource for future reference.
Show steps
-
Outline the steps involved in discovering sensitive data in BigQuery.
-
Document the configuration options for Sensitive Data Protection.
-
Include screenshots and examples to illustrate the process.
-
Share the documentation with peers for feedback.
Implement Data Masking in a BigQuery Dataset
Show steps
Implementing data masking in a real-world scenario solidifies understanding of Sensitive Data Protection techniques.
Show steps
-
Identify a BigQuery dataset containing sensitive data.
-
Configure Sensitive Data Protection to mask the sensitive fields.
-
Test the data masking implementation to ensure it is working correctly.
-
Document the data masking configuration and results.
Contribute to a Data Privacy Project
Show steps
Contributing to an open-source project provides practical experience and exposure to real-world data privacy challenges.
Show steps
-
Find an open-source project related to data privacy or security.
-
Review the project's documentation and code.
-
Identify an area where you can contribute, such as bug fixes or documentation.
-
Submit your contributions to the project.
Review BigQuery Fundamentals
Show steps
Reviewing BigQuery fundamentals ensures a solid foundation for understanding sensitive data discovery within the platform.
Show steps
Show steps
Show steps
- Review BigQuery documentation on data warehousing concepts.
- Practice writing basic SQL queries in BigQuery.
- Explore BigQuery's data loading and exporting capabilities.
Review 'BigQuery: The Definitive Guide'
Show steps
Reviewing this book provides a deeper understanding of BigQuery, which is essential for effectively using Sensitive Data Protection within the platform.
View
Google BigQuery: The Definitive Guide: Data...
on Amazon
Show steps
Show steps
Show steps
- Read the chapters on BigQuery architecture and features.
- Take notes on key concepts and definitions.
- Reflect on how these features relate to Sensitive Data Protection.
Read 'Data Privacy: Principles and Practice'
Show steps
Reading this book provides a broader understanding of data privacy principles, which are essential for effectively using Sensitive Data Protection.
View
Putting Knowledge to Work
on Amazon
Show steps
Show steps
Show steps
- Read the chapters on data governance and compliance.
- Take notes on key concepts and definitions.
- Reflect on how these principles apply to BigQuery data.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Follow Google Cloud's Sensitive Data Protection Tutorials
Show steps
Following Google Cloud's tutorials provides hands-on experience with Sensitive Data Protection features and configurations.
Show steps
Show steps
Show steps
- Find the official Google Cloud documentation for Sensitive Data Protection.
- Work through the tutorials on data masking and de-identification.
- Experiment with different configuration options.
Document Sensitive Data Discovery Process
Show steps
Creating documentation reinforces understanding and provides a valuable resource for future reference.
Show steps
Show steps
Show steps
- Outline the steps involved in discovering sensitive data in BigQuery.
- Document the configuration options for Sensitive Data Protection.
- Include screenshots and examples to illustrate the process.
- Share the documentation with peers for feedback.
Implement Data Masking in a BigQuery Dataset
Show steps
Implementing data masking in a real-world scenario solidifies understanding of Sensitive Data Protection techniques.
Show steps
Show steps
Show steps
- Identify a BigQuery dataset containing sensitive data.
- Configure Sensitive Data Protection to mask the sensitive fields.
- Test the data masking implementation to ensure it is working correctly.
- Document the data masking configuration and results.
Contribute to a Data Privacy Project
Show steps
Contributing to an open-source project provides practical experience and exposure to real-world data privacy challenges.
Show steps
Show steps
Show steps
- Find an open-source project related to data privacy or security.
- Review the project's documentation and code.
- Identify an area where you can contribute, such as bug fixes or documentation.
- Submit your contributions to the project.
Career center
Learners who complete Enabling Sensitive Data Protection Discovery for BigQuery will develop knowledge and skills
that may be useful to these careers:
Cloud Security Engineer
Cloud Security Engineer
Cloud security engineers are responsible for designing, implementing, and managing security controls in cloud environments such as Google Cloud. In this role, you will secure cloud data, protect cloud infrastructure, and ensure compliance with regulations. This course helps cloud security engineers gain practical experience in implementing sensitive data discovery and protection in BigQuery, which is a crucial part of securing data in Google Cloud. Learning the proper methods to deploy specific data protection measures will help you develop the skills needed to carry out your duty.
Cloud Data Architect
Cloud Data Architect
Cloud data architects design and develop cloud-based data solutions. They make architectural decisions about how data will be stored, processed, and accessed in a cloud environment. This course helps a cloud data architect understand how to implement sensitive data protection measures in BigQuery. This is invaluable knowledge for properly designing a cloud data solution to maintain data security regulations and practices within Google Cloud. Architects need to know how to implement discovery and protection measures in Google Cloud.
Data Security Analyst
Data Security Analyst
A data security analyst is responsible for protecting an organization's data from unauthorized access, breaches, and other security threats. This role involves implementing security measures, monitoring systems for vulnerabilities, and responding to incidents. This course may be useful for data security analysts because it provides hands-on experience with discovering and protecting sensitive data in BigQuery using Sensitive Data Protection. Specifically, learning how to implement discovery and protection measures can help an analyst understand how to identify where sensitive information exists in a database and how to properly secure it within the Google Cloud environment.
Information Security Analyst
Information Security Analyst
An information security analyst is responsible for securing an organization's information assets. This role involves conducting risk assessments, implementing security controls, and responding to security incidents. This course is helpful for an information security analyst because it provides practical experience in discovering and protecting sensitive data in BigQuery cloud databases. Learning hands-on how to implement discovery and protection using the Sensitive Data Protection tool will help an information security analyst better understand threats in the Google Cloud environment.
Database Administrator
Database Administrator
Database administrators manage and maintain databases to ensure their security, performance, and reliability. They perform backups, monitor database activities, and implement security measures. As part of their duties, they should understand how to protect sensitive data within them. This course may be useful for database administrators as it provides practice using Sensitive Data Protection to discover and protect data in BigQuery. Knowing how to implement proper protection measures in Google Cloud will greatly help database administrators as they carry out their role.
Technology Consultant
Technology Consultant
Technology consultants advise organizations on how to best use technology to achieve their goals. This often requires an understanding of data and security measures. This course may be useful to a technology consultant who works with Google Cloud because it provides practical experience implementing data security practices. It specifically shows how to use Sensitive Data Protection to discover and protect data in BigQuery. This deep understanding of information security will better help them advise their clients.
Data Governance Specialist
Data Governance Specialist
Data governance specialists develop and implement policies and procedures to ensure the quality, integrity, and security of an organization's data. A key part of this work is to make sure sensitive data is handled safely in databases. This course may be helpful for data governance specialists because it provides hands-on experience in using Sensitive Data Protection with BigQuery, which will help to understand data protection measures within this environment. This understanding allows the specialist to create more robust data governance policies.
Big Data Engineer
Big Data Engineer
Big data engineers are responsible for designing, building, and maintaining large-scale data processing systems. They construct the technical infrastructure required for big data initiatives. This course may be useful for a big data engineer because it introduces one of the most important aspects of data work: data security. Understanding how to discover and protect sensitive data in BigQuery using Sensitive Data Protection can help a big data engineer build a more secure and reliable system that integrates with Google Cloud.
Solutions Architect
Solutions Architect
A solutions architect designs and develops technology solutions that address an organization's needs. Because data is often a component in these solutions, the architect is responsible for ensuring that it is handled appropriately. This course may be helpful for solutions architects who are deploying infrastructure in Google Cloud as it teaches how to protect sensitive data using Sensitive Data Protection in BigQuery. With this knowledge, a solutions architect will be able to develop more robust solutions that comply with data security regulations.
Systems Administrator
Systems Administrator
Systems administrators maintain a computer network. One of their duties includes ensuring data security. They are often implementing and maintaining measures relating to infrastructure and access. This course may be useful for a systems administrator because the course will help anyone on the technical side of a tech organization to gain familiarity with protecting sensitive data in BigQuery using Sensitive Data Protection. The hands on aspect of learning how to deploy these measures will help a systems administrator when they are tasked with implementing or maintaining them.
Compliance Officer
Compliance Officer
Compliance officers ensure an organization follows laws and regulations. They develop policies and procedures to prevent violations and manage compliance programs. A compliance officer should understand how to handle sensitive data and make sure that it adheres to regulatory guidelines, such as those requiring information security. The course may be useful for compliance officers to understand how to secure sensitive data in Google Cloud using Sensitive Data Protection in BigQuery. This kind of knowledge will enable the compliance officer to more carefully assess and enforce policy.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software applications. They often manage data and must also secure it. This course may be helpful for software engineers, especially as they work with databases in cloud environments, such as Google Cloud, because they will learn data security measures. The course teaches how to implement discovery and protection using Sensitive Data Protection in BigQuery, which helps software engineers build more secure applications.
Data Quality Analyst
Data Quality Analyst
Data quality analysts focus on ensuring the accuracy, completeness, and consistency of an organization's data. They work to improve data quality by identifying and resolving issues. This course may be helpful for data quality analysts because it exposes them to data protection measures through implementation of discovery and protection of sensitive data in BigQuery using Sensitive Data Protection. By understanding more about how to handle sensitive data in Google Cloud, a data quality analyst can help ensure that quality measures also account for security.
Data Analyst
Data Analyst
Data analysts interpret data and create reports to help organizations make better decisions. They must also be aware of risks and security implications when handling sensitive data. While this course does not explicitly teach analytics, it does provide practical experience using Sensitive Data Protection in BigQuery. This training will better prepare a data analyst to work with sensitive data within the Google Cloud environment. A data analyst should be aware of various data protection measures as they carry out their duty.
Data Scientist
Data Scientist
Data scientists use statistical and machine learning techniques to extract insights from data. They require a deep understanding of data and how to handle it. While this course does not focus on statistical analysis, it does provide experience in protecting sensitive data. Thus, a data scientist may find this course to be helpful when dealing with datasets in the Google Cloud environment. The course teaches how to implement discovery and protection using Sensitive Data Protection in BigQuery.
For more career information including salaries, visit:
OpenCourser.com/course/k2viju/enabling
Reading list
We've selected two 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
Enabling Sensitive Data Protection Discovery for BigQuery.
Provides a comprehensive guide to using BigQuery. It is useful for understanding the underlying architecture and features of the platform. While not specifically focused on sensitive data protection, it provides valuable context for understanding how Sensitive Data Protection integrates with BigQuery. This book is commonly used as a textbook at academic institutions and by industry professionals.
Provides a comprehensive overview of data privacy principles and practices. It is useful for understanding the broader context of sensitive data protection. While not specific to BigQuery, it offers valuable insights into data governance, compliance, and ethical considerations. This book is more valuable as additional reading than it is as a current reference.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/k2viju/enabling
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