Create a table definition file for an external data source | BigQuery | Google Cloud
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
Lexical structure and syntax | BigQuery | Google Cloud
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
Analyzing Audit Logs Using BigQuery | BigQuery | Google Cloud
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
レプリケーション構成の例 | Bigtable Documentation | Google Cloud
フィードバックを送信 コレクションでコンテンツを整理 必要に応じて、コンテンツの保存と分類を行います。 レプリケーション構成の例 このページでは、Bigtable レプリケーションの一般的な使用例について説明し、以下の使用例をサポートするために使用できる設定を紹介します。 バッチ分析ワークロードを他のアプリケーションから分離する 高可用性（HA）の作成 ほぼリアルタイムのバックアップを提供する 高可用性とリージョンの復元力を維持する ユーザーの近くにデータを保存する このページでは、他のユースケースに使用する設定を決定する方法についても説明します。 このページを読む前に、Bigtable レプリケーションの概要を理解する必要があります。 インスタンスにクラスタを追加する場合は、その前に、レプリケートされたテーブルの garbage-collection policies を変更する際に適用
Load and query data with the bq tool | BigQuery | Google Cloud
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
Specify nested and repeated columns in table schemas | BigQuery | Google Cloud
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
The CREATE MODEL statement | BigQuery | Google Cloud
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
Enable the BigQuery sandbox | Google Cloud
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
Use BigQuery ML to predict penguin weight | Google Cloud
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
Create and use clustered tables | BigQuery | Google Cloud
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
Loading ORC data from Cloud Storage | BigQuery | Google Cloud
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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地理空間分析の概要 | BigQuery | Google Cloud
パラメータ化されたクエリの実行 | BigQuery | Google Cloud
クラスタ化テーブルの作成と使用 | BigQuery | Google Cloud
テーブル スキーマでネストされた列と繰り返し列を指定する | BigQuery | Google Cloud
ローカル データソースから BigQuery にデータを読み込む | BigQuery | Google Cloud
BigQuery ML でペンギンの体重を予測する | Google Cloud
BigQuery で承認済みビューを作成する | BigQuery | Google Cloud
API のパフォーマンスに関するヒント | BigQuery | Google Cloud
Datetime functions | BigQuery | Google Cloud