Manage Your Unstructured Data Assets Across Cloud And Hybrid Environments With Komprise
Data Engineering Podcast
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Structured Data Analytics - What's Next?
A lot of our customers are on a journey towards doing more machine learning, or doing more automation. I feel like most data lakes are canogata swamps and it's too hard to really figure out what you have. We're investing a lot in providing creating blueprints, example use cases. In a way, o a piz in providing training how to use them.
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Summary
There are a wealth of options for managing structured and textual data, but unstructured binary data assets are not as well supported across the ecosystem. As organizations start to adopt cloud technologies they need a way to manage the distribution, discovery, and collaboration of data across their operating environments. To help solve this complicated challenge Krishna Subramanian and her co-founders at Komprise built a system that allows you to treat use and secure your data wherever it lives, and track copies across environments without requiring manual intervention. In this episode she explains the difficulties that everyone faces as they scale beyond a single operating environment, and how the Komprise platform reduces the burden of managing large and heterogeneous collections of unstructured files.
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 Krishna Subramanian about her work at Komprise to generate value from unstructured file and object data across storage formats and locations
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Komprise is and the story behind it?
- Who are the target customers of the Komprise platform?
- What are the core use cases that you are focused on supporting?
- How would you characterize the common approaches to managing file storage solutions for hybrid cloud environments?
- What are some of the shortcomings of the enterprise storage providers’ methods for managing storage tiers when trying to use that data for analytical workloads?
- Given the growth in popularity and capabilities of cloud solutions, how have you approached the strategic positioning of your product to capitalize on the market?
- Can you describe how the Komprise platform is architected?
- What are some of the most complex considerations that you have had to engineer for when dealing with enterprise data distribution in hybrid cloud environments?
- What are the data replication and consistency guarantees that you are able to offer while spanning across on-premise and cloud systems/block and object storage? (e.g. eventual consistency vs. read-after-write, low latency replication on data changes vs. scheduled syncing, etc.)
- How do you determine and validate the heuristics that you use for understanding how/when to distribute files across storage systems?
- How does the specific workload that you are powering influence the specific operations/capabilities that your customers take advantage of?
- What are the most interesting, innovative, or unexpected ways that you have seen Komprise used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Komprise?
- When is Komprise the wrong choice?
- What do you have planned for the future of Komprise?
Contact Info
- @cloudKrishna 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.
- Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
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Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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