This document summarizes a presentation on using metrics and data to influence a DevOps culture. It discusses how aggregating data in meaningful ways can provide greater visibility into teams' work and help with decision making. Presenting metrics in different visualizations like Jupyter notebooks or dashboards allows users to quickly answer questions by combining data sources or changing the granularity. The document provides examples of metric categorization and collection sources. It promotes an "operate first" concept of incorporating operational experience into software projects and includes references to related tools and repositories.

1. Metrics All The Way: Data
Driven DevOps
Hema Veeradhi
Senior Software Engineer
Open Services | Office of the CTO
DevConf.CZ
January 2022

2. Hello!
● Undergrad - 2016
Computer Science, India
● BU Grad - 2019 MS
Computer Science
● Intern - Red Hat - 2018
● Software Engineer -
Red Hat - 2019
2
Sr. Software Engineer at Office of the CTO | Open Services
hveeradh@redhat.com hemaveeradhi hemajv
Boston,
United States

3. “Our highest priority is to satisfy the
customer
through early and continuous delivery
of valuable software.”
agilemanifesto.org/principles

4. “Our highest priority is to satisfy the
customer
through early and continuous delivery
of valuable software insights from data.”
agilemanifesto.org/principles

5. DevOps Culture
People Process Technology

6. Data+Metrics Can Influence DevOps Culture

7. Rise of Data Driven Development
● Aggregate the information in meaningful ways

8. Rise of Data Driven Development
● Aggregate the information in meaningful ways
● Provide greater visibility into in-progress work being done by
teams and also help in important decision making processes

9. Rise of Data Driven Development
● Aggregate the information in meaningful ways
● Provide greater visibility into in-progress work being done by
teams and also help in important decision making processes
● Answer questions quick, i.e. combining different data sources,
going back in time, changing the granularity of data, being able to
change the visualizations such as prototypying in Jupyter
notebooks vs creating dashboards vs generating PDF reports

10. Cooking
With
Metrics

11. Metrics
Categorization

12. Identify Data Sources
Collect Metrics

13. Exploring
Metrics

14. Metrics
Presentation

15. Demo

16. References
● Data Driven Development Repo -
https://github.com/AICoE/data-driven-development
● Data Driven Development JupyterBook -
http://data-driven-development.catalog.meteor.zone/
● Thoth Kebechet Application -
https://github.com/thoth-station/kebechet
● Jupyterbook Meteor Pipeline - https://shower.meteor.zone/

17. “A concept to incorporate operational experience into
software projects.”
www.operate-first.cloud
Operate First

18. Thank you!
https://www.operate-first.cloud
Hema Veeradhi
hveeradh@redhat.com