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Elaine Hanley
DataOps is defined by Gartner as "a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and consumers across an organization. Much like DevOps, DataOps is not a rigid dogma, but a principles-based practice influencing how data can be provided and updated to meet the need of the organization’s data consumers.”
Read more
DataOps is defined by Gartner as "a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and consumers across an organization. Much like DevOps, DataOps is not a rigid dogma, but a principles-based practice influencing how data can be provided and updated to meet the need of the organization’s data consumers.”
Read more
DataOps is defined by Gartner as "a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and consumers across an organization. Much like DevOps, DataOps is not a rigid dogma, but a principles-based practice influencing how data can be provided and updated to meet the need of the organization’s data consumers.”
The DataOps Methodology is designed to enable an organization to utilize a repeatable process to build and deploy analytics and data pipelines. By following data governance and model management practices they can deliver high-quality enterprise data to enable AI. Successful implementation of this methodology allows an organization to know, trust and use data to drive value.
In the DataOps Methodology course you will learn about best practices for defining a repeatable and business-oriented framework to provide delivery of trusted data. This course is part of the Data Engineering Specialization which provides learners with the foundational skills required to be a Data Engineer.
Register for this course and see more details by visiting:
OpenCourser.com/course/b21bpl/dataops
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What's inside
Syllabus
Establish DataOps - Prepare for operation
In this module you will learn the fundamentals of a DataOps approach. You will learn about the people who are involved in defining data, curating it for use by a wide variety of data consumers, and how they can work together to deliver data for a specific purpose:
Read more
Syllabus
Establish DataOps - Prepare for operation
In this module you will learn the fundamentals of a DataOps approach. You will learn about the people who are involved in defining data, curating it for use by a wide variety of data consumers, and how they can work together to deliver data for a specific purpose:
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Useful for business professionals who lack technical knowledge
Leads to greater data manipulation, insight, and decision-making
Establishes best practices for working with data across an organization
Provides data comprehension at a higher level
Helps optimize data quality and data workflows
Helps translate data into actionable insights
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Reviews summary
Dataops methodology: foundational framework
According to learners, this course offers a strong foundational understanding of the DataOps methodology, highly relevant for modern enterprise environments. Students particularly appreciate the emphasis on data governance, achieving trusted data, and ensuring high data quality across an organization. The content is considered clear and logically structured, focusing on defining the business value that DataOps brings. While providing a comprehensive theoretical framework and including valuable real-world context, some learners suggest the course could be enhanced with more hands-on technical exercises or tool-specific implementation details to complement the strategic insights.
Highlights the strategic importance of DataOps for organizations.
"The course effectively articulates the business value of DataOps, showing how it drives real impact for AI."
"I found the discussions on optimizing data flows and supporting AI-based systems highly relevant to current industry needs."
"It clearly connects DataOps practices to organizational goals and decision-making."
Emphasizes crucial aspects of trusted data and data integrity.
"The modules on 'Trust your data' and data classification were particularly insightful, highlighting key governance practices."
"I appreciated the strong focus on data quality and the methods for detection and remediation mentioned in the course."
"Learning about defining policies and ensuring high-fidelity data sources is invaluable for my work."
Provides a robust conceptual understanding of DataOps principles.
"This course offers a solid introduction to DataOps, clearly outlining the methodology and its benefits."
"I gained a comprehensive understanding of how to establish and iterate DataOps within an organization."
"For anyone new to the DataOps domain, this course provides an excellent and highly relevant overview."
Primarily theoretical; could benefit from more hands-on application.
"While excellent for methodology, I wished for more hands-on labs or practical demonstrations with specific tools."
"The course is more conceptual than practical, which is great for understanding the 'what' and 'why' but less on the 'how'."
"I would have preferred deeper technical dives or coding examples to fully grasp the implementation aspects."
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 DataOps Methodology with these
activities:
Review DataOps methodologies
Show steps
Ensure that you are familiar with the foundational DataOps methodologies and concepts before beginning this course to make your learning more efficient.
Browse courses on
DataOps
Show steps
-
Read the DataOps Wikipedia Page
-
Read DataOps Concepts
-
Revisit the previous module or materials that go over DataOps methodologies
Review Apache Spark Architecture
Show steps
Review the basics of this technology's architecture to ensure the foundational knowledge for the course is strong.
Browse courses on
Apache Spark
Show steps
-
Review Apache Spark documentation
-
Take an online course or workshop on Apache Spark architecture
Compile course materials
Show steps
Compiling your course materials will help you become familiar with the course content and identify areas where you may need additional support.
Show steps
-
Gather course syllabus, lecture notes, and other relevant materials
-
Review materials and make note of key concepts and topics
-
Organize materials into a study binder notebook or digital folder
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Join a study group or online forum
Show steps
Joining a study group or online forum will allow you to connect with other students and learn from each other.
Show steps
-
Find a study group or online forum for data engineering
-
Participate in discussions and ask questions
-
Help other students with their questions
Build a DataOps Pipeline
Show steps
Applying knowledge from the course will ensure that the information is retained more effectively.
Browse courses on
Data Pipeline
Show steps
-
Choose a data source
-
Define the data pipeline architecture
-
Implement the data pipeline
-
Monitor and evaluate the data pipeline
Learn DataOps with Hands-on Projects
Show steps
Deepen your understanding of DataOps principles and methodologies by working through hands-on projects and tutorials.
Browse courses on
DataOps
Show steps
-
Go to the course website
-
Click on the DataOps Tutorial link
-
Complete the hands-on tutorial
Practice data modeling and transformations
Show steps
Practicing data modeling and transformations will help you develop the skills necessary to work with real-world datasets.
Browse courses on
Data Modeling
Show steps
-
Identify a real-world dataset and download it
-
Explore and clean the dataset
-
Create a data model that captures the relationships between the data
-
Transform the data into a format that is suitable for analysis
Attend industry meetups and conferences
Show steps
Attending industry events will help you build your network and stay up-to-date on the latest trends in data engineering.
Show steps
-
Research industry meetups and conferences in your area
-
Attend events and connect with professionals in the field
-
Share your knowledge and experiences with others
Develop a DataOps plan for a specific business use case
Show steps
Develop your ability to translate abstract concepts into practical solutions by formulating a DataOps plan catered towards a specific business requirement.
Browse courses on
DataOps
Show steps
-
Identify a business problem or challenge
-
Create a DataOps plan that outlines the steps needed to address the problem
Develop a data pipeline
Show steps
Developing a data pipeline will give you hands-on experience with the entire data engineering process.
Browse courses on
Data Pipeline
Show steps
-
Define the purpose and scope of the data pipeline
-
Design the architecture of the pipeline
-
Implement the pipeline using appropriate tools and technologies
-
Test and validate the pipeline
-
Deploy and monitor the pipeline
Participate in data engineering workshops
Show steps
Participating in data engineering workshops will give you the opportunity to learn from experts and practice new skills.
Show steps
-
Find data engineering workshops in your area
-
Register for workshops and attend the sessions
-
Practice the skills you learn in the workshops
Build a data visualization dashboard
Show steps
Building a data visualization dashboard will help you develop the skills necessary to communicate data insights effectively.
Browse courses on
Data Visualization
Show steps
-
Identify the key metrics and insights that you want to visualize
-
Select the appropriate visualization types
-
Design and create the dashboard
-
Test and validate the dashboard
-
Deploy and share the dashboard
Create a resource repository for data engineering
Show steps
Creating a resource repository will help you consolidate your knowledge and create a valuable resource for the community.
Browse courses on
Data Engineering
Show steps
-
Gather resources such as articles, tutorials, and videos on data engineering
-
Organize the resources into a cohesive collection
-
Share the repository with other students and professionals
Review DataOps methodologies
Show steps
Ensure that you are familiar with the foundational DataOps methodologies and concepts before beginning this course to make your learning more efficient.
Browse courses on
DataOps
Show steps
Show steps
Show steps
- Read the DataOps Wikipedia Page
- Read DataOps Concepts
- Revisit the previous module or materials that go over DataOps methodologies
Review Apache Spark Architecture
Show steps
Review the basics of this technology's architecture to ensure the foundational knowledge for the course is strong.
Browse courses on
Apache Spark
Show steps
Show steps
Show steps
- Review Apache Spark documentation
- Take an online course or workshop on Apache Spark architecture
Compile course materials
Show steps
Compiling your course materials will help you become familiar with the course content and identify areas where you may need additional support.
Show steps
Show steps
Show steps
- Gather course syllabus, lecture notes, and other relevant materials
- Review materials and make note of key concepts and topics
- Organize materials into a study binder notebook or digital folder
Ten other activities
Expand to see all activities and additional details
Show all 13 activities
Join a study group or online forum
Show steps
Joining a study group or online forum will allow you to connect with other students and learn from each other.
Show steps
Show steps
Show steps
- Find a study group or online forum for data engineering
- Participate in discussions and ask questions
- Help other students with their questions
Build a DataOps Pipeline
Show steps
Applying knowledge from the course will ensure that the information is retained more effectively.
Browse courses on
Data Pipeline
Show steps
Show steps
Show steps
- Choose a data source
- Define the data pipeline architecture
- Implement the data pipeline
- Monitor and evaluate the data pipeline
Learn DataOps with Hands-on Projects
Show steps
Deepen your understanding of DataOps principles and methodologies by working through hands-on projects and tutorials.
Browse courses on
DataOps
Show steps
Show steps
Show steps
- Go to the course website
- Click on the DataOps Tutorial link
- Complete the hands-on tutorial
Practice data modeling and transformations
Show steps
Practicing data modeling and transformations will help you develop the skills necessary to work with real-world datasets.
Browse courses on
Data Modeling
Show steps
Show steps
Show steps
- Identify a real-world dataset and download it
- Explore and clean the dataset
- Create a data model that captures the relationships between the data
- Transform the data into a format that is suitable for analysis
Attend industry meetups and conferences
Show steps
Attending industry events will help you build your network and stay up-to-date on the latest trends in data engineering.
Show steps
Show steps
Show steps
- Research industry meetups and conferences in your area
- Attend events and connect with professionals in the field
- Share your knowledge and experiences with others
Develop a DataOps plan for a specific business use case
Show steps
Develop your ability to translate abstract concepts into practical solutions by formulating a DataOps plan catered towards a specific business requirement.
Browse courses on
DataOps
Show steps
Show steps
Show steps
- Identify a business problem or challenge
- Create a DataOps plan that outlines the steps needed to address the problem
Develop a data pipeline
Show steps
Developing a data pipeline will give you hands-on experience with the entire data engineering process.
Browse courses on
Data Pipeline
Show steps
Show steps
Show steps
- Define the purpose and scope of the data pipeline
- Design the architecture of the pipeline
- Implement the pipeline using appropriate tools and technologies
- Test and validate the pipeline
- Deploy and monitor the pipeline
Participate in data engineering workshops
Show steps
Participating in data engineering workshops will give you the opportunity to learn from experts and practice new skills.
Show steps
Show steps
Show steps
- Find data engineering workshops in your area
- Register for workshops and attend the sessions
- Practice the skills you learn in the workshops
Build a data visualization dashboard
Show steps
Building a data visualization dashboard will help you develop the skills necessary to communicate data insights effectively.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify the key metrics and insights that you want to visualize
- Select the appropriate visualization types
- Design and create the dashboard
- Test and validate the dashboard
- Deploy and share the dashboard
Create a resource repository for data engineering
Show steps
Creating a resource repository will help you consolidate your knowledge and create a valuable resource for the community.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Gather resources such as articles, tutorials, and videos on data engineering
- Organize the resources into a cohesive collection
- Share the repository with other students and professionals
Career center
Learners who complete DataOps Methodology will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
A Data Engineer designs and builds the infrastructure that allows data to be stored, processed, and analyzed. This course may be useful for a Data Engineer because it provides a foundation in DataOps, which is essential for managing and delivering high-quality data.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help organizations make informed decisions. This course may be useful for a Data Analyst because it provides a foundation in DataOps, which can help them to understand how data is managed and delivered within an organization.
Data Scientist
Data Scientist
A Data Scientist uses data to build models and algorithms that can help organizations solve problems and make predictions. This course may be useful for a Data Scientist because it provides a foundation in DataOps, which can help them to understand how data is managed and delivered within an organization.
Data Architect
Data Architect
A Data Architect designs and manages the architecture of an organization's data systems. This course may be useful for a Data Architect because it provides a foundation in DataOps, which can help them to understand how data is managed and delivered within an organization.
Database Administrator
Database Administrator
A Database Administrator manages and maintains an organization's databases. This course may be useful for a Database Administrator because it provides a foundation in DataOps, which can help them to understand how data is managed and delivered within an organization.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course may be useful for a Software Engineer because it provides a foundation in DataOps, which can help them to understand how data is managed and delivered within an organization.
Business Analyst
Business Analyst
A Business Analyst helps organizations to understand their business processes and identify opportunities for improvement. This course may be useful for a Business Analyst because it provides a foundation in DataOps, which can help them to understand how data is managed and delivered within an organization.
Project Manager
Project Manager
A Project Manager plans and executes projects. This course may be useful for a Project Manager because it provides a foundation in DataOps, which can help them to understand how data is managed and delivered within an organization.
IT Manager
IT Manager
An IT Manager plans and executes IT projects. This course may be useful for an IT Manager because it provides a foundation in DataOps, which can help them to understand how data is managed and delivered within an organization.
Data Governance Manager
Data Governance Manager
A Data Governance Manager develops and implements data governance policies and procedures. This course may be useful for a Data Governance Manager because it provides a foundation in DataOps, which can help them to understand how data is managed and delivered within an organization.
Data Quality Manager
Data Quality Manager
A Data Quality Manager develops and implements data quality policies and procedures. This course may be useful for a Data Quality Manager because it provides a foundation in DataOps, which can help them to understand how data is managed and delivered within an organization.
Chief Data Officer
Chief Data Officer
A Chief Data Officer is responsible for the overall management of an organization's data. This course may be useful for a Chief Data Officer because it provides a foundation in DataOps, which can help them to understand how data is managed and delivered within an organization.
Data Privacy Officer
Data Privacy Officer
A Data Privacy Officer is responsible for ensuring that an organization's data is used in a compliant and ethical manner. This course may be useful for a Data Privacy Officer because it provides a foundation in DataOps, which can help them to understand how data is managed and delivered within an organization.
Data Security Officer
Data Security Officer
A Data Security Officer is responsible for ensuring that an organization's data is protected from unauthorized access and use. This course may be useful for a Data Security Officer because it provides a foundation in DataOps, which can help them to understand how data is managed and delivered within an organization.
Data Compliance Officer
Data Compliance Officer
A Data Compliance Officer is responsible for ensuring that an organization's data is compliant with all applicable laws and regulations. This course may be useful for a Data Compliance Officer because it provides a foundation in DataOps, which can help them to understand how data is managed and delivered within an organization.
For more career information including salaries, visit:
OpenCourser.com/course/b21bpl/dataops
Reading list
We've selected eight 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
DataOps Methodology .
Provides a deep dive into advanced analytics techniques using Apache Spark, which is valuable for practitioners looking to enhance their DataOps capabilities.
Save
Provides a comprehensive overview of data quality management principles and practices, which are crucial for building trust in data used in DataOps.
Provides a practical introduction to machine learning algorithms and techniques, which are increasingly used in DataOps to automate data analysis and insights generation.
Provides guidance on establishing and implementing data governance frameworks, which are essential for ensuring data quality and trust in DataOps.
Provides a practical guide to using Pandas, a popular Python library for data manipulation and analysis, which is widely used in DataOps.
Provides a hands-on introduction to data science using Python, which popular programming language used in DataOps for data analysis and machine learning.
Provides an overview of big data analytics concepts and techniques, which are often used in conjunction with DataOps to extract insights from large datasets.
Provides a foundational understanding of data science concepts and algorithms from scratch, using Python.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/b21bpl/dataops
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Effort
8 weeks, 1-2 hours per week
Level
Introductory
Via
Coursera
Institution
IBM
Instructor
Elaine Hanley
Language
English
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