Bring Order To The Chaos Of Your Unstructured Data Assets With Unstruk

Summary Working with unstructured data has typically been a motivation for a data lake. The challenge is imposing enough order on the platform to make it useful. Kirk Marple has spent years working with data systems and the media industry, which inspired him to build a platform for automatically organizing your unstructured assets to make them more valuable. In this episode he shares the goals of the Unstruk Data Warehouse, how it is architected to extract asset metadata and build a searchable knowledge graph from the information, and the myriad ways that the system can be used. If you are wondering how to deal with all of the information that doesn’t fit in your databases or data warehouses, then this episode is for you. 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If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription Your host is Tobias Macey and today I’m interviewing Kirk Marple about Unstruk Data, a company that is building a data warehouse for unstructured data that ofers automated data preparation via metadata enrichment, integrated compute, and graph-based search Interview Introduction How did you get involved in the area of data management? Can you describe what Unstruk Data is and the story behind it? What would you classify as "unstructured data"? What are some examples of industries that rely on large or varied sets of unstructured data? What are the challenges for analytics that are posed by the different categories of unstructured data? What is the current state of the industry for working with unstructured data? What are the unique capabilities that Unstruk provides and how does it integrate with the rest of the ecosystem? Where does it sit in the overall landscape of data tools? Can you describe how the Unstruk data warehouse is implemented? What are the assumptions that you had at the start of this project that have been challenged as you started working through the technical implementation and customer trials? How has the design and architecture evolved or changed since you began working on it? How do you handle versioning of data, given the potential for individual files to be quite large? What are some of the considerations that users should have in mind when modeling their data in the warehouse? Can you talk through the workflow of ingesting and analyzing data with Unstruk? How do you manage data enrichment/integration with structured data sources? What are the most interesting, innovative, or unexpected ways that you have seen the technology of Unstruk used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on and with the Unstruk platform? When is Unstruk the wrong choice? What do you have planned for the future of Unstruk? Contact Info LinkedIn @KirkMarple on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Links Unstruk Data TIFF ROSBag HDF5 Media/Digital Asset Management Data Mesh SAN NAS Knowledge Graph Entity Extraction OCR (Optical Character Recognition) Cloud Native Cosmos DB Azure Functions Azure EventHub Azure Cognitive Search GraphQL KNative Schema.org Pinecone Vector Database Podcast Episode Dublin Core Metadata Initiative Knowledge Management The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast