Delphix Podcast
Accelerating CI/CD Cycles with Programmable Data Infrastructure with Delphix
Event Date: February 25, 2021
Hosted By: Delphix & Carahsoft
DevOps teams have built automated, repeatable processes for much of the development pipeline. Test data bottlenecks continue to plague otherwise efficient CI/CD workflows, yet are surprisingly overlooked when evaluating the speed of the value stream.
Legacy ticketing systems and coordination to provision and refresh test data create a tremendous drag on release velocity, and manual processes for de-identifying sensitive information are slow and error-prone. PII data can easily leak into test environments.
API-driven test data delivery and compliance improve the flow of the entire software lifecycle. Treating data like code eliminates data wait states, delivering and refreshing data over 90% faster than outdated processes. Integrating data automation into the CI/CD toolchain breaks the data bottleneck so continuous integration can truly scale.
In this podcast, Aaron Jensen, the Senior Solutions Engineer at Delphix and Arif Hajee, the Principal Solutions Engineer at Delphix explain how a programmable data infrastructure ensures test data can keep pace with high-velocity development and improve development productivity.
"With Delphix, the way we drive a little bit more data from an agility standpoint allows you to start working things in parallel. In this case, because we have the ability to virtualize individual databases and give teams or give individuals access to these virtual data libraries, you're removing those bottlenecks of having to synchronize specific steps within your cycles themselves and allow either developers or tests or anybody on the application team to start working in parallel." - Arif Hajee, Principal Solutions Engineer, Delphix
Podcast Teaser
Listen to the podcast to:
- Understand version-control test data just like code
- Learn how to refresh, bookmark, or rewind data with a single API call
- Shift-left with production-quality data
- Discover how to automate identification and mask sensitive data
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