Making The Total Cost Of Ownership For External Data Manageable With Crux
Data Engineering Podcast
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The Costs of Managing Data Sources
Costs associated with managing these data sources are going to be a major concern, particularly as you scale the size of the organization. We're gathering stats. We've got over 25 thousand pipe lines live yet. That's a fairly big bunch. But it's fair to say, to day, i think it follows pretty much to the letter, what we see in the development cycle. Andly, i still run into people that think the soft way just grows on trees occasionally as well.That sort of thing has dissipated over time. When you're actualy are talking to practitioners, its very much more on education. It is an education and not taken by default.
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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.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- 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
- Crux
- Thomson Reuters
- Goldman Sachs
- JP Morgan
- Avro
- ESG == Environmental, Social, Government Data
- Selenium
- Google Cloud Platform
- Cadence
- Airflow
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
Sponsored By:
- Shipyard:  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](https://www.dataengineeringpodcast.com/shipyard) to get started automating powerful workflows with their free developer plan today!
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