{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"The Good Tech Companies ","title":"Continuous Aggregate Refresh, Demystified: Invalidation, Lookback, and Late-Arriving Data","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/9c521be3\"></iframe>","width":"100%","height":180,"duration":857,"description":"\n        This story was originally published on HackerNoon at: https://hackernoon.com/continuous-aggregate-refresh-demystified-invalidation-lookback-and-late-arriving-data.\nLearn why late-arriving time-series data can leave aggregates stale and how TimescaleDB continuous aggregate refresh windows determine reconciling corrections.\nCheck more stories related to undefined at: https://hackernoon.com/c/undefined.\n            You can also check exclusive content about #timescaledb, #continuous-aggregate, #timescaledb-invalidation-log, #time-series-aggregation, #incremental-time-series-data, #time-series-view, #time-series-data, #good-company,  and more.\nThis story was written by: @tigerdata. Learn more about this writer by checking @tigerdata's about page,\n            and for more stories, please visit hackernoon.com.\nLate-arriving and corrected time-series data can make pre-computed dashboards report plausible but stale numbers. This article compares four aggregation strategies, from scheduled materialized views and insert-triggered views to streaming dataflows and TimescaleDB continuous aggregates. It explains how invalidation tracking works and why the start_offset and end_offset of a refresh policy determine whether late data is ever reconciled.\n        \n        ","thumbnail_url":"https://img.transistorcdn.com/HZ9CRzf5js9DK86xzUVMWBRbXYwg4dA8xVXJGVzpL6Y/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8xMTNl/MjgwMmI0ZmEzNThj/YmJiOWNiN2UyZmRm/MzY3My5qcGVn.webp","thumbnail_width":300,"thumbnail_height":300}