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.
- 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
- 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?
- 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.
- 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@example.com) with your story.
- To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers
- Thomson Reuters
- Goldman Sachs
- JP Morgan
- ESG == Environmental, Social, Government Data
- Google Cloud Platform
Support Data Engineering Podcast