Operational Analytics At Speed With Minimal Busy Work Using Incorta

Summary

A huge amount of effort goes into modeling and shaping data to make it available for analytical purposes. This is often due to the need to simplify the final queries so that they are performant for visualization or limited exploration. In order to cut down the level of effort involved in making data usable, Matthew Halliday and his co-founders created Incorta as an end-to-end, in-memory analytical engine that removes barriers to insights on your data. In this episode he explains how the system works, the use cases that it empowers, and how you can start using it for your own analytics today.

Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
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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 Matthew Halliday about Incorta, an in-memory, unified data and analytics platform as a service

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you describe what Incorta is and the story behind it?
• What are the use cases and customers that you are focused on?
  • How does that focus inform the design and priorities of functionality in the product?
• What are the technologies and workflows that Incorta might replace?
  • What are the systems and services that it is intended to integrate with and extend?
• Can you describe how Incorta is implemented?
  • What are the core technological decisions that were necessary to make the product successful?
  • How have the design and goals of the system changed and evolved since you started working on it?
• Can you describe the workflow for building an end-to-end analysis using Incorta?
  • What are some of the new capabilities or use cases that Incorta enables which are impractical or intractable with other combinations of tools in the ecosystem?
• How do the features of Incorta influence the approach that teams take for data modeling?
• What are the points of collaboration and overlap between organizational roles while using Incorta?
• What are the most interesting, innovative, or unexpected ways that you have seen Incorta used?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on Incorta?
• When is Incorta the wrong choice?
• What do you have planned for the future of Incorta?

Contact Info

• LinkedIn
• @layereddelay on Twitter
• Website

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

• Incorta
• 3rd Normal Form
• Parquet
  • Podcast Episode
• Delta Lake
  • Podcast Episode
• Iceberg
  • Podcast Episode
• PrestoDB
• PySpark
• Dataiku
• Angular
• React
• Apache ECharts

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA