Data Observability

Kevin Hu (@kevinzenghu, Co-Founder | CEO at @Metaplane) talks about the concepts behind Data Observability and the unique challenges for Data Engineers.

SHOW: 594

CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotw

CHECK OUT OUR NEW PODCAST - "CLOUDCAST BASICS"

SHOW SPONSORS:

• BMC Wants to Know if your business is on it's A-Game
• BMC Autonomous Digital Enterprise
• Usage.ai (homepage)
• Start saving up to 57% of your AWS EC2 spend in under 5 minutes with Usage AI. No code change, no downtime, no engineering work required.
• New Relic (homepage)
• Services down? New Relic offers full stack visibility with 16 different monitoring products in a single platform.

SHOW NOTES:

• Metaplane (homepage)

Topic 1 - Welcome to the show. Let’s talk about your background and what led you to start Metaplane.

Topic 2 - Let’s start by talking about the concept of what is a modern data engineer. What is this person doing, what are they responsible for, and who are their typical “customers” within a business. 

Topic 3 - Beyond just huge volumes of data and trying to make the data usable (formatting, ETL, storage access, etc.), what sort of problems do data engineers encounter? How much is typically “first-party data” and how much comes from external systems? 

Topic 4 - Let’s talk about Data Observability. First off, what is it?. And second, how is it different from the Observability that we’ve seen from Datadog or Honeycomb or Observe or many others? 

Topic 5 - What are the types of Data Observability problems that Metaplane is focused on solving for Data engineers? Are these usually done independently, or in collaboration with the application or business analyst teams?

Topic 6 - What are some of the immediate results (improvements) that companies see when adding Data Observability to their environments?

FEEDBACK?

• Email: show at the cloudcast dot net
• Twitter: @thecloudcastnet