Taking A Multidimensional Approach To Data Observability At Acceldata
March 13th, 2022
1 hr 3 mins 17 secs
About this Episode
Data observability is a term that has been co-opted by numerous vendors with varying ideas of what it should mean. At Acceldata, they view it as a holistic approach to understanding the computational and logical elements that power your analytical capabilities. In this episode Tristan Spaulding, head of product at Acceldata, explains the multi-dimensional nature of gaining visibility into your running data platform and how they have architected their platform to assist in that endeavor.
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription
- RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder.
- TimescaleDB, from your friends at Timescale, is the leading open-source relational database with support for time-series data. Time-series data is time stamped so you can measure how a system is changing. Time-series data is relentless and requires a database like TimescaleDB with speed and petabyte-scale. Understand the past, monitor the present, and predict the future. That’s Timescale. Visit them today at dataengineeringpodcast.com/timescale
- Your host is Tobias Macey and today I’m interviewing Tristan Spaulding about Acceldata, a platform offering multidimensional data observability for modern data infrastructure
- How did you get involved in the area of data?
- Can you describe what Acceldata is and the story behind it?
- What does it mean for a data observability platform to be "multidimensional"?
- How do the architectural characteristics of the "modern data stack" influence the requirements and implementation of data observability strategies?
- The data observability ecosystem has seen a lot of activity over the past ~2-3 years. What are the unique capabilities/use cases that Acceldata supports?
- Who are your target users and how does that focus influence the way that you have approached feature and design priorities?
- What are some of the ways that you are using the Acceldata platform to run Acceldata?
- Can you describe how the Acceldata platform is implemented?
- How have the design and goals of the system changed or evolved since you started working on it?
- How are you managing the definition, collection, and correlation of events across stages of the data lifecycle?
- What are some of the ways that performance data can feed back into the debugging and maintenance of an organization’s data ecosystem?
- What are the challenges that data platform owners face when trying to interpret the metrics and events that are available in a system like Acceldata?
- What are the most interesting, innovative, or unexpected ways that you have seen Acceldata used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Acceldata?
- When is Acceldata the wrong choice?
- What do you have planned for the future of Acceldata?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
- If you’ve learned something or tried out a project from the show then tell us about it! Email email@example.com) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Support Data Engineering Podcast