The proliferation of sensors and GPS devices has dramatically increased the number of applications for spatial data, and the need for scalable geospatial analytics. In order to reduce the friction involved in aggregating disparate data sets that share geographic similarities the Unfolded team built a platform that supports working across raster, vector, and tabular data in a single system. In this episode Isaac Brodsky explains how the Unfolded platform is architected, their experience joining the team at Foursquare, and how you can start using it for analyzing your spatial data today.
Have you ever woken up to a crisis because a number on a dashboard is broken and no one knows why? Or sent out frustrating slack messages trying to find the right data set? Or tried to understand what a column name means?
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Unstruk Data offers an API-driven solution to simplify the process of transforming unstructured data files into actionable intelligence about real-world assets without writing a line of code – putting insights generated from this data at enterprise teams’ fingertips. The company was founded in 2021 by Kirk Marple after his tenure as CTO of Kespry. Kirk possesses extensive industry knowledge including over 25 years of experience building and architecting scalable SaaS platforms and applications, prior successful startup exits, and deep unstructured and perception data experience. Unstruk investors include 8VC, Preface Ventures, Valia Ventures, Shell Ventures and Stage Venture Partners.
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Datafold helps you deal with data quality in your pull request. It provides automated regression testing throughout your schema and pipelines so you can address quality issues before they affect production. No more shipping and praying, you can now know exactly what will change in your database ahead of time.
Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI, so in a few minutes you can get from 0 to automated testing of your analytical code. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold.
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- Modern data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days or even weeks. By the time errors have made their way into production, it’s often too late and damage is done. Datafold built automated regression testing to help data and analytics engineers deal with data quality in their pull requests. Datafold shows how a change in SQL code affects your data, both on a statistical level and down to individual rows and values before it gets merged to production. No more shipping and praying, you can now know exactly what will change in your database! Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Visit dataengineeringpodcast.com/datafold today to book a demo with Datafold.
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- Your host is Tobias Macey and today I’m interviewing Isaac Brodsky about Foursquare’s Unfolded platform for working with spatial data
- How did you get involved in the area of data management?
- Can you describe what the Unfolded platform is and the story behind it?
- What are some of the core challenges of working with spatial data?
- What are some of the sources that organizations rely on for collecting or generating those data sets?
- What are the capabilities that the Unfolded platform offers for spatial analytics?
- What use cases are you primarily focused on supporting?
- What (if any) are the datasets or analyses that you are consciously not investing in supporting?
- Can you describe how the Unfolded platform is implemented?
- How have the design and goals shifted or evolved since you started working on Unfolded?
- What are the new constraints or opportunities that are available after the merger with Foursquare?
- Can you describe a typical workflow for someone using Unfolded to manage their spatial information and build an analysis on top of it?
- What are some of the data modeling considerations that are necessary when populating a custom data set with Unfolded?
- What are some of the techniques that you needed to build to allow for loading large data sets into a users’s browser while maintaining sufficient performance?
- What are the most interesting, innovative, or unexpected ways that you have seen Unfolded used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Unfolded?
- When is Unfolded the wrong choice?
- What do you have planned for the future of Unfolded?
- 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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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