Weekly Episode Summaries and Programming Notes – Week of October 22, 2023
Oct 22, 2023
auto_awesome
This weekly summary episode introduces upcoming episodes on becoming data-driven and applying site reliability engineering practices to data. The importance of reliability engineering in data operations is discussed, as well as Big Panda's journey towards becoming data driven. The top eight takeaways from a panel discussion on applying reliability engineering to data are presented, along with a discussion on legacy systems, interconnectedness, and observability in data.
The importance of effective communication and conversations to help teams understand and embrace the concept of producing and owning data.
The significance of observability and measuring beyond data quality in reliability engineering practices for data systems.
Deep dives
Setting the Groundwork to Become Data-Driven
This episode features an interview with Corin Schloemow-Goldenburg from Big Panda. She shares her experience of transforming a company from being data-immature to data-capable. The conversation emphasizes the importance of having good conversations with teams that have not yet embraced the concept of producing and owning data. Corin provides valuable insights on how to make teams understand the significance of data and move forward in utilizing it effectively.
Applying Site Reliability Engineering Practices to Data
In this panel episode, Emily Gorsinski leads a discussion with Amy Toby from Equinix and Alex Adalgo from Noble 9. The panel explores the application of reliability engineering practices to data, drawing lessons from other disciplines like software engineering. The conversation highlights the need for observability in data systems and the importance of measuring beyond just data quality. The panelists also emphasize the significance of understanding criticality and prioritization when it comes to reliability engineering in the data realm.
Key Takeaways on Reliability Engineering for Data
This extended summary provides key takeaways from both the interview with Corin Schloemow-Goldenburg and the panel on applying reliability engineering practices to data. The takeaways cover various aspects of reliability engineering, including observability, service level objectives, value-driven data approaches, cost management, and the need for awareness and investment in reliability engineering practices. The summary also points out the challenges and opportunities in the current state of data tooling and the importance of understanding system complexity and interconnectedness in the data realm.