{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/cf82e6a7ab824d8dbd572d9371ccf6dc\" frameborder=\"0\" width=\"1280\" height=\"960\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":960,"width":1280,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":960,"thumbnail_width":1280,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/cf82e6a7ab824d8dbd572d9371ccf6dc-1692117390401.gif","duration":300.16666666666686,"title":"Investigating a Failing DBT Test Part 3: Confirm the nature of the problem","description":"In this video, I analyze the issue of duplicate data in Air Table records. By examining the source record ID and URL, I confirm that the duplicates are caused by individual feeds pointing to multiple records. I also discover variations in the source record ID, some being null and others having different values. To understand the scale of the issue, I observe that most of the duplicates are from 2022, indicating that it is historical data. This suggests that it is not a recurring bug but rather a data handling challenge. I also explore the prevalence of the issue by checking the number of implicated feeds. Overall, this investigation helps us understand the nature and scope of the duplicate data problem."}