Summary
Data pipelines are the core of every data product, ML model, and business intelligence dashboard. If you're not careful you will end up spending all of your time on maintenance and fire-fighting. The folks at Rivery distilled the seven principles of modern data pipelines that will help you stay out of trouble and be productive with your data. In this episode Ariel Pohoryles explains what they are and how they work together to increase your chances of success.
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 Ariel Pohoryles about the seven principles of modern data pipelines
Interview
Introduction
How did you get involved in the area of data management?
Can you start by defining what you mean by a "modern" data pipeline?
At Rivery you published a white paper identifying seven principles of modern data pipelines:
Zero infrastructure management
ELT-first mindset
Speaks SQL and Python
Dynamic multi-storage layers
Reverse ETL & operational analytics
Full transparency
Faster time to value
What are the applications of data that you focused on while identifying these principles?
How do the application of these principles influence the ability of organizations and their data teams to encourage and keep pace with the use of data in the business?
What are the technical components of a pipeline infrastructure that are necessary to support a "modern" workflow?
How do the technologies involved impact the organizational involvement with how data is applied throughout the business?
When using managed services, what are the ways that the pricing model acts to encourage/discourage experimentation/exploration with data?
What are the most interesting, innovative, or unexpected ways that you have seen these seven principles implemented/applied?
What are the most interesting, unexpected, or challenging lessons that you have learned while working with customers to adapt to these principles?
What are the cases where some/all of these principles are undesirable/impractical to implement?
What are the opportunities for further advancement/sophistication in the ways that teams work with and gain value from data?
Contact Info
LinkedIn (https://www.linkedin.com/in/ariel-pohoryles-88695622/)
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__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.the