apache iceberg
for data engineers, ML engineers
Why this scored 13
every term, weightedIts fastest-moving item, measured against the pace of its own source
Whether that velocity is itself speeding up, as a per-hour rate
How many independent communities its own items come from
Decays to zero over 14 days, counted from when we first saw it
Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it
Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology
Signal history
7-day window (free)The evidence
The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.
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Production RAG on the Lakehouse with BigQuery Vector Search and Apache Iceberg
Combines BigQuery vector search with Iceberg for RAG on lakehouse, enabling scalable semantic queries. · for data engineers, ML engineers
devtoEmergingai33d ago - 222
Bidirectional Writeback for Apache Iceberg via Google Sheets: Serverless Lakehouse Console
Adds bidirectional write-back between Iceberg tables and Sheets for serverless lakehouse workflows. · for data engineers
devtoSteadydata48d ago