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Interview with Materialize - Consistency
Manage episode 386301341 series 3509937
Hubert Dulay에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Hubert Dulay 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.
In this podcast, we interview Arjun Narayan, Frank McSherry, and Nikhil Benesch from Materialize. Ralph and I are writing a book on streaming databases and seeking expert insights from Materialize on topics rarely discussed in the field. We begin by exploring the distinction between operational and analytical workloads, highlighting the importance of real-time or near-real-time results for operational tasks. We further delve into the significance of consistency in operational workloads and the challenges of using eventually consistent systems. The guests caution against relying on eventually consistent stores and databases, stressing the value of consistency in certain domains like payments.
We focus on the concept of time in differential data flow, explaining how revisions provide a better understanding of time in this context. Consistency is highlighted as crucial in temporal joins, especially for mathematical operations and data enrichment. Overall, we emphasize the importance of real-time workloads, consistency, and integration in operational systems.
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