Companies of all sizes and industries are trying to use the data that they and their customers generate to survive and thrive in the modern economy. As a result, they are relying on a constantly growing number of data sources being accessed by an increasingly varied set of users. In order to help data consumers find and understand the data is available, and help the data producers understand how to prioritize their work, SelectStar has built a data discovery platform that brings everyone together. In this episode Shinji Kim shares her experience as a data professional struggling to collaborate with her colleagues and how that led her to founding a company to address that problem. She also discusses the combination of technical and social challenges that need to be solved for everyone to gain context and comprehension around their most valuable asset.
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?
Our friends at Atlan started out as a data team themselves and faced all this collaboration chaos themselves, and started building Atlan as an internal tool for themselves. Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more.
Go to dataengineeringpodcast.com/atlan and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription.
Your data platform needs to be scalable, fault tolerant, and performant, which means that you need the same from your cloud provider. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform. With the combined power of the Kubernetes engine for flexible and scalable deployments, and features like dedicated CPU instances, GPU instances, and object storage you’ve got everything you need to build a bulletproof data pipeline. If you go to dataengineeringpodcast.com/linode today you’ll even get a $100 credit to use on building your own cluster, or object storage, or reliable backups, or… And while you’re there don’t forget to thank them for being a long-time supporter of the Data Engineering Podcast!
Hightouch is the leading Reverse ETL platform. Your data warehouse is your source of truth for customer data. Hightouch syncs this data to the tools that your business teams rely on. Hightouch has a catalog of flexible destinations including Salesforce, HubSpot, Zendesk, NetSuite, and ad platforms like Facebook or Google. Hightouch is built for data engineers and is a natural extension to the modern data stack with out-of-the-box integrations with your favorite tools like dbt, Fivetran, Airflow, Slack, PagerDuty, and DataDog.
It’s simple — connect your data warehouse, paste a SQL query, and use our visual mapper to specify how data should appear in downstream tools. No scripts, just SQL. Get started for free at dataengineeringpodcast.com/hightouch
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- You listen to this show to learn about all of the latest tools, patterns, and practices that power data engineering projects across every domain. Now there’s a book that captures the foundational lessons and principles that underly everything that you hear about here. I’m happy to announce I collected wisdom from the community to help you in your journey as a data engineer and worked with O’Reilly to publish it as 97 Things Every Data Engineer Should Know. Go to dataengineeringpodcast.com/97things today to get your copy!
- 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 managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads? Hightouch is the easiest way to sync data into the platforms that your business teams rely on. The data you’re looking for is already in your data warehouse and BI tools. Connect your warehouse to Hightouch, paste a SQL query, and use their visual mapper to specify how data should appear in your SaaS systems. No more scripts, just SQL. Supercharge your business teams with customer data using Hightouch for Reverse ETL today. Get started for free at dataengineeringpodcast.com/hightouch.
- Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. 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 Shinji Kim about SelectStar, an intelligent data discovery platform that helps you understand your data
- How did you get involved in the area of data management?
- Can you describe what SelectStar is and the story behind it?
- What are the core challenges that organizations are facing around data cataloging and discovery?
- There has been a surge in tools and services for metadata collection, data catalogs, and data collaboration. How would you characterize the current state of the ecosystem?
- What is SelectStar’s role in the space?
- Who are your target customers and how does that shape your prioritization of features and the user experience design?
- Can you describe how SelectStar is architected?
- How have the goals and design of the platform shifted or evolved since you first began working on it?
- I understand that you have built integrations with a number of BI and dashboarding tools such as Looker, Tableau, Superset, etc. What are the use cases that those integrations enable?
- What are the challenges or complexities involved in building and maintaining those integrations?
- What are the other categories of integration that you have had to implement to make SelectStar a viable solution?
- Can you describe the workflow of a team that is using SelectStar to collaborate on data engineering and analytics?
- What have been the most complex or difficult problems to solve for?
- What are the most interesting, innovative, or unexpected ways that you have seen SelectStar used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on SelectStar?
- When is SelectStar the wrong choice?
- What do you have planned for the future of SelectStar?
- 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 show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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- University of Waterloo
- Concord Systems