Do Away With Data Integration Through A Dataware Architecture With Cinchy
Summary
The reason that so much time and energy is spent on data integration is because of how our applications are designed. By making the software be the owner of the data that it generates, we have to go through the trouble of extracting the information to then be used elsewhere. The team at Cinchy are working to bring about a new paradigm of software architecture that puts the data as the central element. In this episode Dan DeMers, Cinchy’s CEO, explains how their concept of a "Dataware" platform eliminates the need for costly and error prone integration processes and the benefits that it can provide for transactional and analytical application design. This is a fascinating and unconventional approach to working with data, so definitely give this a listen to expand your thinking about how to build your systems.
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 Dan DeMers about Cinchy, a dataware platform aiming to simplify the work of data integration by eliminating ETL/ELT
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Cinchy is and the story behind it?
- In your experience working in data and building complex enterprise-grade systems, what are the shortcomings and negative externalities of an ETL/ELT approach to data integration?
- How is a Dataware platform from a data lake or data warehouses? What is it used for?
- What is Zero-Copy Integration? How does that work?
- Can you describe how customers start their Cinchy journey?
- What are the main use case patterns that you’re seeing with Dataware?
- Your platform offers unlimited users, including business users. What are some of the challenges that you face in building a user experience that doesn’t become overwhelming as an organization scales the number of data sources and processing flows?
- What are the most interesting, innovative, or unexpected ways that you have seen Cinchy used?
- When is Cinchy the wrong choice for a customer?
- Can you describe the technical architecture of the Cinchy platform?
- How do you establish connections/relationships among data from disparate sources?
- How do you manage schema evolution in source systems?
- What are some of the edge cases that users need to consider as they are designing and building those connections?
- What are some of the features or capabilities of Cinchy that you think are overlooked or under-utilized?
- How has your understanding of the problem space changed since you started working on Cinchy?
- How has the architecture and design of the system evolved to reflect that updated understanding?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Cinchy?
- What do you have planned for the future of Cinchy?
Contact Info
- @dandemers on Twitter
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.
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Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA