{"id":"clm_A210C22C5B3D","entity":"google-cloud","predicate":"customer","value":"Copel","value_json":{"feed":"google-cloud","item":"https://cloud.google.com/blog/products/data-analytics/reverse-etl-bigquery-exports-to-alloydb/","tags":{"v":2,"ai":false,"by":"claude-haiku-5-5","on":"2026-11-13","deal":{"kind":"customer","counterparty":"Copel","counterparty_type":"industrial_energy"},"themes":["developer_platform"]},"check":{"on":"2026-11-14","url":"https://cloud.google.com/blog/products/data-analytics/reverse-etl-bigquery-exports-to-alloydb/","pages":1,"quote":"To demonstrate the benefits of this feature, we are sharing how Copel , a Brazilian electric power provider, eliminated a massive orchestration bottleneck and scaled their smart meter telemetry by migrating to high-performance, built-in Reverse ETL.","outcome":"checked"},"evidence":"Bridging the gap between analytical systems (OLAP) and transactional databases (OLTP) is notoriously complex. Synchronizing data between the two systems, often called Reverse ETL (Extract, Transform, Load), traditionally requires heavy middleware, staging buckets, and complex orchestration, especial","extracted_by":"claude-haiku-5-5","primary_source":true},"valid_from":"2026-11-13","valid_to":null,"published":"2026-11-13","recorded":"2026-11-13","citation":{"plain":"AsOf. \"Google Cloud Platform customer: Copel\", as of 2026-11-13. Claim clm_A210C22C5B3D, https://asof.is/claim/clm_A210C22C5B3D, accessed 2026-11-14. Source: https://cloud.google.com/blog/products/data-analytics/reverse-etl-bigquery-exports-to-alloydb/.","url":"https://asof.is/claim/clm_A210C22C5B3D","accessed":"2026-11-14"},"confidence":0.7,"status":"valid","assertable":true,"superseded_by":null,"supersedes":[],"first_seen_at":"2026-11-13T21:20:19.122517+00:00","updated_at":"2026-11-14T10:05:14.317617+00:00","sources":[{"url":"https://cloud.google.com/blog/products/data-analytics/reverse-etl-bigquery-exports-to-alloydb/","tier":"primary","publisher":"Google Cloud","published_at":"2026-11-13T00:00:00+00:00"}],"evidence":{"by":"page-read","on":"2026-11-14","quote":"To demonstrate the benefits of this feature, we are sharing how Copel , a Brazilian electric power provider, eliminated a massive orchestration bottleneck and scaled their smart meter telemetry by migrating to high-performance, built-in Reverse ETL.","url":"https://cloud.google.com/blog/products/data-analytics/reverse-etl-bigquery-exports-to-alloydb/","excerpt":"Bridging the gap between analytical systems (OLAP) and transactional databases (OLTP) is notoriously complex. Synchronizing data between the two systems, often called Reverse ETL (Extract, Transform, Load), traditionally requires heavy middleware, staging buckets, and complex orchestration, especial"},"legend":{"dates":"valid_from: when the claim took effect (a period figure: the day its period closed). published: the earliest day a source on the record published it (a filing's filing date, a post's date; null when no source carries one). recorded: the day AsOf first held it. A figure can take effect weeks before it is published.","citation":"citation: how to cite the row (plain: one sentence with the value, its as-of date, the claim id and URL, the day answered and the primary source; url: the claim page; accessed: the day answered). APA and BibTeX forms are on the claim page.","why_not":{},"checked_by":{"page-read":"its source page states the value and the date, as read by an AI model"}}}