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How Observability is Advancing Data Reliability and Data Quality
Manage episode 328869358 series 2954151
Damien Deighan and Philipp Diesinger, Damien Deighan, and Philipp Diesinger에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Damien Deighan and Philipp Diesinger, Damien Deighan, and Philipp Diesinger 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.
Modern Data Infrastructures and platforms store huge amounts of multidimensional data. But - data pipelines frequently break and a machine learning algorithm's performance is only as good as the quality and reliability of the data itself.
In this episode we are joined by Lior Gavish and Ryan Kearns of Monte Carlo, to talk about how the new concept of Data Observability is advancing Data Reliability and Data Quality at Scale.
Episode Summary
- A overview of Data Reliability/Quality and why it is so critical for organisations
- The limitations of traditional approaches in the area of Data Reliability
- Data observability and why it is different to traditional approaches to Data Quality
- The 5 Pillars of Data Observability
- How to improve data reliability/quality at scale and generate trust in data with stakeholders.
- How observability can lead to better outcomes for Data Science and engineering teams?
- Examples of data observability use cases in industry
- Overview of O’Reilly’s upcoming book, The Fundamentals of Data Quality.
25 에피소드
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