Accelerate Development Of Enterprise Analytics With The Coalesce Visual Workflow Builder
Data Engineering Podcast
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Data Mesh
One thing i'm in a passionat abo is the decentraization paraline, which is another words, they call it data mesh. I really very passionate about that idea because it just seems, going back in all my years of experience in this, if i look at this particular paradine, it makes a lot of sense. There's underlying four ciples. One is, make the domain responsible for building the pipe lines and producing high quality data. The other domains are the people who want to consume that data, expect certain quality. And finally, some kind of governance an all of this. That's deferently on our minds as well, to support those data sc
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Summary
The flexibility of software oriented data workflows is useful for fulfilling complex requirements, but for simple and repetitious use cases it adds significant complexity. Coalesce is a platform designed to reduce repetitive work for common workflows by adopting a visual pipeline builder to support your data warehouse transformations. In this episode Satish Jayanthi explains how he is building a framework to allow enterprises to move quickly while maintaining guardrails for data workflows. This allows everyone in the business to participate in data analysis in a sustainable manner.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Satish Jayanthi about how organizations can use data architectural patterns to stay competitive in today’s data-rich environment
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what you are building at Coalesce and the story behind it?
- What are the core problems that you are focused on solving with Coalesce?
- The platform appears to be fairly opinionated in the workflow. What are the design principles and philosophies that you have embedded into the user experience?
- Can you describe how Coalesce is implemented?
- What are the pitfalls in data architecture patterns that you commonly see organizations fall prey to?
- How do the pre-built transformation templates in Coalesce help to guide users in a more maintainable direction?
- The platform is currently tied to Snowflake as the underlying engine. How much effort will it be to expand your integrations and the scope of Coalesece?
- What are the most interesting, innovative, or unexpected ways that you have seen Coalesce used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Coalesce?
- When is Coalesce the wrong choice?
- What do you have planned for the future of Coalesce?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- 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 hosts@dataengineeringpodcast.com) with your story.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Links
- Coalesce
- Data Warehouse Toolkit
- Wherescape
- dbt
- Type 2 Dimensions
- Firebase
- Kubernetes
- Star Schema
- Data Vault
- Data Mesh
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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