BigQueryでマテリアライズドビューが提供開始、プレビューとして。複雑なクエリをより高速に実行可能 Google Cloudのデータウエアハウス向けデータベースであるBigQueryに「マテリアライズドビュー」機能がプレリリース版として追加されました。 一般にリレーショナルデータベースにおける「ビュー」とは、仮想的なテーブルのようなものとされます。これによって、BIツールを使ってデータベースへ検索を実行する際などに、複雑なテーブル構成を隠蔽して分かりやすい構成のビューだけを見せたり、特定のユーザーにアクセスを許すビューだけを見せる、といったことが可能になります。 通常のビューは仮想的な存在であり実体を持たないため、ビューに対するクエリは実際にはビューを構成するテーブルに対するクエリに分解され、実行され、結果が返されます。 一方、マテリアライズドビューはテーブルのように実体を持つビューと
Send feedback Stay organized with collections Save and categorize content based on your preferences. Create a table definition file for an external data source This page describes how to create a table definition file for an external data source. An external data source is a data source that you can query directly even though the data is not stored in BigQuery. A table definition file contains an
Send feedback Lexical structure and syntax Stay organized with collections Save and categorize content based on your preferences. A GoogleSQL statement comprises a series of tokens. Tokens include identifiers, quoted identifiers, literals, keywords, operators, and special characters. You can separate tokens with comments or whitespace such as spaces, backspaces, tabs, or newlines. Identifiers Iden
Send feedback Stay organized with collections Save and categorize content based on your preferences. BigQuery audit logs overview Cloud Audit Logs are a collection of logs provided by Google Cloud that provide insight into operational concerns related to your use of Google Cloud services. This page provides details about BigQuery specific log information, and it demonstrates how to use BigQuery to
Send feedback Stay organized with collections Save and categorize content based on your preferences. Specify nested and repeated columns in table schemas This page describes how to define a table schema with nested and repeated columns in BigQuery. For an overview of table schemas, see Specifying a schema. Define nested and repeated columns To create a column with nested data, set the data type of
Send feedback Stay organized with collections Save and categorize content based on your preferences. Load and query data with the bq tool Learn how to create a dataset, load sample data, and query tables with the bq command-line tool. To follow step-by-step guidance for this task directly in the Google Cloud console, click Guide me: Guide me Before you begin Sign in to your Google Cloud account. I
Send feedback Stay organized with collections Save and categorize content based on your preferences. The CREATE MODEL statement To create a model in BigQuery, use the BigQuery ML CREATE MODEL statement. This statement is similar to the CREATE TABLE DDL statement. When you run a query that contains a CREATE MODEL statement, a query job is generated for you that processes the query. For information
Send feedback Use BigQuery ML to predict penguin weight Stay organized with collections Save and categorize content based on your preferences. In this tutorial, you use a linear regression model in BigQuery ML to predict the weight of a penguin based on the penguin's demographic information. A linear regression is a type of regression model that generates a continuous value from a linear combinati
Send feedback Stay organized with collections Save and categorize content based on your preferences. Enable the BigQuery sandbox The BigQuery sandbox lets you explore limited BigQuery capabilities at no cost to confirm whether BigQuery fits your needs. The BigQuery sandbox lets you experience BigQuery without providing a credit card or creating a billing account for your project. If you already cr
Send feedback Stay organized with collections Save and categorize content based on your preferences. Create and use clustered tables This document describes how to create and use clustered tables in BigQuery. For an overview of clustered table support in BigQuery, see Introduction to clustered tables. Create clustered tables You can create a clustered table by using the following methods: Create a
Send feedback Stay organized with collections Save and categorize content based on your preferences. Loading ORC data from Cloud Storage This page provides an overview of loading ORC data from Cloud Storage into BigQuery. ORC is an open source column-oriented data format that is widely used in the Apache Hadoop ecosystem. When you load ORC data from Cloud Storage, you can load the data into a new
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