Combining The Simplicity Of Spreadsheets With The Power Of Modern Data Infrastructure At Canvas
Data Engineering Podcast
Shownotes Transcript
Summary
Data analysis is a valuable exercise that is often out of reach of non-technical users as a result of the complexity of data systems. In order to lower the barrier to entry Ryan Buick created the Canvas application with a spreadsheet oriented workflow that is understandable to a wide audience. In this episode Ryan explains how he and his team have designed their platform to bring everyone onto a level playing field and the benefits that it provides to the organization.
Announcements
Hello and welcome to the Data Engineering Podcast, the show about modern data management
When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their new managed database service you can launch a production ready MySQL, Postgres, or MongoDB cluster in minutes, with automated backups, 40 Gbps connections from your application hosts, and high throughput SSDs. Go to dataengineeringpodcast.com/linode) today and get a $100 credit to launch a database, create a Kubernetes cluster, or take advantage of all of their other services. And don’t forget to thank them for their continued support of this show!
Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan’s active metadata capabilities. Push information about data freshness and quality to your business intelligence, automatically scale up and down your warehouse based on usage patterns, and let the bots answer those questions in Slack so that the humans can focus on delivering real value. Go to dataengineeringpodcast.com/atlan) today to learn more about how Atlan’s active metadata platform is helping pioneering data teams like Postman, Plaid, WeWork & Unilever achieve extraordinary things with metadata and escape the chaos.
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.
Unstruk is the DataOps platform for your unstructured data. The options for ingesting, organizing, and curating unstructured files are complex, expensive, and bespoke. Unstruk Data is changing that equation with their platform approach to manage your unstructured assets. Built to handle all of your real-world data, from videos and images, to 3d point clouds and geospatial records, to industry specific file formats, Unstruk streamlines your workflow by converting human hours into machine minutes, and automatically alerting you to insights found in your dark data. Unstruk handles data versioning, lineage tracking, duplicate detection, consistency validation, as well as enrichment through sources including machine learning models, 3rd party data, and web APIs. Go to dataengineeringpodcast.com/unstruk) today to transform your messy collection of unstructured data files into actionable assets that power your business.
Your host is Tobias Macey and today I’m interviewing Ryan Buick about Canvas, a spreadsheet interface for your data that lets everyone on your team explore data without having to learn SQL
Interview
Introduction
How did you get involved in the area of data management?
Can you describe what Canvas is and the story behind it?
The "modern data stack" has enabled organizations to analyze unparalleled volumes of data. What are the shortcomings in the operating model that keeps business users dependent on engineers to answer their questions?
Why is the spreadsheet such a popular and persistent metaphor for working with data?
What are the biggest issues that existing spreadsheet software run up against as they scale both technically and organizationally?
What are the new metaphors/design elements that you needed to develop to extend the existing capabilities and use cases of spreadsheets while keeping them familiar?
Can you describe how the Canvas platform is implemented?
How have the design and goals of the product changed/evolved since you started working on it?
What is the workflow for a business user that is using Canvas to iterate on a series of questions?
What are the collaborative features that you have built into Canvas and who are they for? (e.g. other business users, data engineers <-> business users, etc.)
What are the situations where the spreadsheet abstraction starts to break down?
What are the extension points/escape hatches that you have built into the product for when that happens?
What are the most interesting, innovative, or unexpected ways that you have seen Canvas used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on Canvas?
When is Canvas the wrong choice?
What do you have planned for the future of Canvas?
Contact Info
@ryanjbuick) 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 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.
Visit the site) to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected])) with your story.
To help other people find the show please leave a review on Apple Podcasts) and tell your friends and co-workers
Links
Podcast Episode) about their data mesh implementation
dbt)
The intro and outro music is from The Hug) by The Freak Fandango Orchestra) / CC BY-SA)