BrianFagioli writes: Google has quietly retired the ZetaSQL name and rebranded its open source SQL analysis and parsing project as GoogleSQL. This is not a technical change but a naming cleanup meant to align the open source code with the SQL dialect already used across Google products like BigQuery...

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Іван, дата-аналітик в TENTENS Tech, ділиться своїм списком корисних практик у GoogleSQL, що допоможуть писати запити швидше, зручніше та читабельніше.

ZetaSQL is now GoogleSQL. The SQL dialect is being unified under one banner for the open-source community and Google Cloud users.

An introductory guide to BigQuery as a serverless data warehouse, covering GoogleSQL, pricing, Google Sheets integration, and Python/CLI usage.

面向初学者，为您梳理将 BigQuery 作为无服务器数据仓库使用的方法，涵盖 GoogleSQL、定价、Sheets 联动以及 Python/CLI 的使用。

Google has renamed its open-source ZetaSQL project to GoogleSQL, unifying the branding for the SQL dialect, analysis, and parsing libraries.

spansql へのレクイエム、または Go における Spanner GoogleSQL 方言パーサの現状この記事は apstndb Advent Calendar の1日目の記事です。この記事では Spanner GoogleSQL 方言を処理する Go ライブラリの選択肢の現状について共有しつつ、 Spanner コミュニティが進むべきだと考える方向性を示します。対象読者は下記のような方を

BigQueryをサーバーレスなデータウェアハウスとして、GoogleSQL、料金、Sheets連携、Python/CLI利用まで初心者向けに整理します。

Embeddings, representing data's meaning, are crucial for comparing and analyzing data. BigQuery introduced vector search to simplify this process, offering an accessible solution for all users. Before BigQuery's native support, vector search involved complex, multi-step processes for data professionals. BigQuery's vector search focuses on simplicity, being serverless, and easy to use. It offers automatic index refresh, integration with GoogleSQL and Python, and guaranteed data consistency. Users pay only for what they use, and strong security measures are in place. Vector search improves LLM applications, semantic search, customer 360, and anomaly detection. Enhanced batch processing capabilities allow for large-scale clustering and comprehensive anomaly detection. Updates included TreeAH, asynchronous index training, stored columns, and partitioned indexes. Index model rebuilds ensure the accuracy of vector search results over time. The future aims to index all enterprise data effectively for AI and analytics.

In a recent paper titled "SQL Has Problems. We Can Fix Them", a research team at Google proposed the introduction of a new Pipe Syntax in SQL. Google's solution to address perceived limitations in SQL is currently available in the GoogleSQL and ZetaSQL dialects, but it has received mixed feedback from the community.