EP21 - Data as Code with Dremio Arctic: ML Experimentation & Reproducibility on the Lakehouse

As more data consumers require access to critical customer and operational data in the data lake, data teams need solutions that enable multiple users to leverage the same view of the data for a wide range of use cases without impacting each other. In this video of Gnarly Data Waves, we will discuss how the data as code capabilities in Dremio Arctic enable data scientists to: - Create a data science branch of the production branch for experimentation without creating expensive data copies or impacting production workloads - Easily work and collaborate cross-functionally with other data consumers and line of business experts - Quickly reproduce models and results by returning to previous branch states with tags and commit history See all upcoming episodes: https://www.dremio.com/gnarly-data-wa... Connect with us! Twitter: https://bit.ly/30pcpE1 LinkedIn: https://bit.ly/2PoqsDq Facebook: https://bit.ly/2BV881V Community Forum: https://bit.ly/2ELXT0W Github: https://bit.ly/3go4dcM Blog: https://bit.ly/2DgyR9B Questions?: https://bit.ly/30oi8tX Website: https://bit.ly/2XmtEnN#datalakehouse #analytics #datawarehouse #datalake #dataengineers #dataarchitects #governance #infrastructure #dremiocloud #dremiotestdrive #openlakehouse #opendatalakehouse #gnarlydatawaves #apacheiceberg #dremioarctic #datamesh #metadata #modernization #datasharing #migration #ETL #datasilos #selfservice #compliance #dataascode #branches #optimized #automates #datamovement #clustering #metrics #filtering #partitioning #tableformat #ApacheArrow #projectnessie #dremiosonar #optimization #automaticdata #scalability #enterprisedata #federated #catalogmigratortool #apachespark #ML #changedatacapture