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#37 DoK Community: Running Data Replication Pipelines on Kubernetes with Argo // Stephen Bailey

1:02:21
 
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Manage episode 288298062 series 2865115
Data on Kubernetes Community에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Data on Kubernetes Community 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

Abstract of the talk…

Hundreds of data teams have migrated to the ELT pattern in recent years, leveraging SaaS tools like Stitch or FiveTran to reliably load data into their infrastructure. These SaaS offerings are outstanding and can accelerate your time to production significantly. However, many teams prefer to roll their own tools. One solution in these cases is to deploy singer.io taps and targets — Python scripts that can perform data replication between arbitrary sources and destinations. The Singer specification is the foundation for the popular Stitch SaaS, and it is also leveraged by a number of independent consultants and data projects. Singer pipelines are highly modular. You can pipe any tap to any target to build a data pipeline that fits your needs, making them a good fit for containerized workflows. This article walks through the workflow at a high level and provides some example code to get up and running with some shared templates. I also drill into reasons for choosing the Argo approach over other orchestration tools like Airflow or Dagster, and the implications from a team perspective.

Bio…

Stephen Bailey is Director of Growth Analytics at Immuta, where he strives to implement privacy best practices while delivering business value from data. He loves to teach and learn, on just about any subject. He holds a PhD in educational cognitive neuroscience from Vanderbilt and enjoys reading philosophy

  continue reading

243 에피소드

Artwork
icon공유
 
Manage episode 288298062 series 2865115
Data on Kubernetes Community에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Data on Kubernetes Community 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

Abstract of the talk…

Hundreds of data teams have migrated to the ELT pattern in recent years, leveraging SaaS tools like Stitch or FiveTran to reliably load data into their infrastructure. These SaaS offerings are outstanding and can accelerate your time to production significantly. However, many teams prefer to roll their own tools. One solution in these cases is to deploy singer.io taps and targets — Python scripts that can perform data replication between arbitrary sources and destinations. The Singer specification is the foundation for the popular Stitch SaaS, and it is also leveraged by a number of independent consultants and data projects. Singer pipelines are highly modular. You can pipe any tap to any target to build a data pipeline that fits your needs, making them a good fit for containerized workflows. This article walks through the workflow at a high level and provides some example code to get up and running with some shared templates. I also drill into reasons for choosing the Argo approach over other orchestration tools like Airflow or Dagster, and the implications from a team perspective.

Bio…

Stephen Bailey is Director of Growth Analytics at Immuta, where he strives to implement privacy best practices while delivering business value from data. He loves to teach and learn, on just about any subject. He holds a PhD in educational cognitive neuroscience from Vanderbilt and enjoys reading philosophy

  continue reading

243 에피소드

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