Making The Total Cost Of Ownership For External Data Manageable With Crux - Episode 308

Summary

There are extensive and valuable data sets that are available outside the bounds of your organization. Whether that data is public, paid, or scraped it requires investment and upkeep to acquire and integrate it with your systems. Crux was built to reduce the total cost of acquisition and ownership for integrating external data, offering a fully managed service for delivering those data assets in the manner that best suits your infrastructure. In this episode Crux CTO Mark Etherington discusses the different costs involved in managing external data, how to think about the total return on investment for your data, and how the Crux platform is architected to reduce the toil involved in managing third party data.

Datafold LogoDatafold 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.


Shipyard is an orchestration platform that helps data teams build out solid data operations from the get-go by connecting data tools and streamlining data workflows. Shipyard offers low-code templates that are configured using a visual interface, replacing the need to write code to build workflows while enabling engineers to get their work into production faster. If a solution can’t be built with existing templates, engineers can always automate scripts in the language of their choice to bring any internal or external process into their workflows.

Observability and alerting are built into the Shipyard platform, ensuring that breakages are identified before being discovered downstream by business teams. With a high level of concurrency, scalability, and end-to-end encryption, Shipyard enables data teams to accomplish more without relying on other teams or worrying about infrastructure challenges, while also ensuring that business teams trust the data made available to them. Go to dataengineeringpodcast.com/shipyard to get started automating powerful workflows with their free developer plan today!


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!


Atlan LogoHave 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.


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.
  • Tired of deploying bad data? Need to automate data pipelines with less red tape? Shipyard is the premier data orchestration platform built to help your data team quickly launch, monitor, and share workflows in a matter of minutes. Build powerful workflows that connect your entire data stack end-to-end with a mix of your code and their open-source, low-code templates. Once launched, Shipyard makes data observability easy with logging, alerting, and retries that will catch errors before your business team does. So whether you’re ingesting data from an API, transforming it with dbt, updating BI tools, or sending data alerts, Shipyard centralizes these operations and handles the heavy lifting so your data team can finally focus on what they’re good at — solving problems with data. Go to dataengineeringpodcast.com/shipyard to get started automating with their free developer plan today!
  • Your host is Tobias Macey and today I’m interviewing Mark Etherington about Crux, a platform that helps organizations scale their most critical data delivery, operations, and transformation needs

Interview

  • Introduction
  • How did you get involved in the area of data management?
  • Can you describe what Crux is and the story behind it?
  • What are the categories of information that organizations use external data sources for?
  • What are the challenges and long-term costs related to integrating external data sources that are most often overlooked or underestimated?
    • What are some of the primary risks involved in working with external data sources?
  • How do you work with customers to help them understand the long-term costs associated with integrating various sources?
    • How does that play into the broader conversation about assessing the value of a given data-set?
  • Can you describe how you have architected the Crux platform?
    • How have the design and goals of the platform changed or evolved since you started working on it?
    • What are the design choices that have had the most significant impact on your ability to reduce operational complexity and maintenance overhead for the data you are working with?
  • For teams who are relying on Crux to manage external data, what is involved in setting up the initial integration with your system?
    • What are the steps to on-board new data sources?
  • How do you manage data quality/data observability across your different data providers?
    • What kinds of signals do you propagate to your customers to feed into their operational platforms?
  • What are the most interesting, innovative, or unexpected ways that you have seen Crux used?
  • What are the most interesting, unexpected, or challenging lessons that you have learned while working on Crux?
  • When is Crux the wrong choice?
  • What do you have planned for the future of Crux?

Contact Info

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.
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Links

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

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