When using the vacuum container method, it is important to use a(n) ________.

BigQuery is a fully managed enterprise data warehouse that helps you manage and analyze your data with built-in features like machine learning, geospatial analysis, and business intelligence. BigQuery's serverless architecture lets you use SQL queries to answer your organization's biggest questions with zero infrastructure management. BigQuery's scalable, distributed analysis engine lets you query terabytes in seconds and petabytes in minutes.

BigQuery maximizes flexibility by separating the compute engine that analyzes your data from your storage choices. You can store and analyze your data within BigQuery or use BigQuery to assess your data where it lives. Federated queries let you read data from external sources while streaming supports continuous data updates. Powerful tools like BigQuery ML and BI Engine let you analyze and understand that data.

BigQuery interfaces include Google Cloud console interface and the BigQuery command-line tool. Developers and data scientists can use client libraries with familiar programming including Python, Java, JavaScript, and Go, as well as BigQuery's REST API and RPC API to transform and manage data. ODBC and JDBC drivers provide interaction with existing applications including third-party tools and utilities.

As a data analyst, data engineer, data warehouse administrator, or data scientist, the BigQuery ML documentation helps you discover, implement, and manage data tools to inform critical business decisions.

Get started with BigQuery

You can start exploring BigQuery in minutes. Take advantage of BigQuery's free usage tier or no-cost sandbox to start loading and querying data.

1. BigQuery's sandbox: Get started in the BigQuery sandbox, risk-free and at no cost.
2. Google Cloud console quickstart: Familiarize yourself with the power of the BigQuery Console.
3. Public datasets: Experience BigQuery's performance by exploring large, real-world data from the Public Datasets Program.

Explore BigQuery

BigQuery's serverless infrastructure lets you focus on your data instead of resource management. BigQuery combines a cloud-based data warehouse and powerful analytic tools.

BigQuery storage

BigQuery stores data using a columnar storage format that is optimized for analytical queries. BigQuery presents data in tables, rows, and columns and provides full support for database transaction semantics (ACID). BigQuery storage is automatically replicated across multiple locations to provide high availability.

• Learn about common patterns to organize BigQuery resources in the data warehouse and data marts.
• Learn about datasets, BigQuery's top-level container of tables and views.
• Load data into BigQuery using:
  • Stream data with the Storage Write API.
  • Batch-load data from local files or Cloud Storage using formats that include: Avro, Parquet, ORC, CSV, JSON, Datastore, and Firestore formats.
• BigQuery Data Transfer Service automates data ingestion.

For more information, see Overview of BigQuery storage.

BigQuery analytics

Descriptive and prescriptive analysis uses include business intelligence, ad hoc analysis, geospatial analytics, and machine learning. You can query data stored in BigQuery or run queries on data where it lives using external tables or federated queries including Cloud Storage, Bigtable, Spanner, or Google Sheets stored in Google Drive.

• ANSI-standard SQL queries (SQL:2011 support) including support for joins, nested and repeated fields, analytic and aggregation functions, multi-statement queries, and a variety of spatial functions with geospatial analytics - Geographic Information Systems.
• Create views to share your analysis.
• Business intelligence tool support including BI Engine with Looker Studio, Looker, Google Sheets, and 3rd party tools like Tableau and Power BI.
• BigQuery ML provides machine learning modeling and predictive analytics.
• Query data outside of BigQuery with external tables and federated queries.

For more information, see Overview of BigQuery analytics.

BigQuery administration

BigQuery provides centralized management of data and compute resources while Identity and Access Management (IAM) helps you secure those resources with the access model that's used throughout Google Cloud. Google Cloud security best practices provide a solid yet flexible approach that can include traditional perimeter security or more complex and granular defense-in-depth approach.

• Intro to data security and governance helps you understand data governance, and what controls you might need to secure BigQuery resources.
• Jobs are actions that BigQuery runs on your behalf to load, export, query, or copy data.
• Reservations let you switch between on-demand pricing and flat-rate pricing.

For more information, see Introduction to BigQuery administration.

BigQuery resources

Explore BigQuery resources:

• Release notes provide change logs of features, changes, and deprecations.

• Pricing for analysis and storage. See also: BigQuery ML, BI Engine, and Data Transfer Service pricing.

• Locations define where you create and store datasets (regional and multi-region locations).

• Smart analytics reference patterns provides links to sample code and technical reference guides for common analytics use cases, including best practices for developing common analytics features.

• Stack Overflow hosts an engaged community of developers and analysts working with BigQuery.

• BigQuery Support provides help with BigQuery.

• Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale by Valliappa Lakshmanan and Jordan Tigani, explains how BigQuery works and provides an end-to-end walkthrough on how to use the service.

APIs, tools, and references

Reference materials for BigQuery developers and analysts:

• SQL query syntax for details about using Google Standard SQL.
• BigQuery API and client libraries present overviews of BigQuery's features and their use.
• BigQuery code samples provide hundreds of snippets for client libraries in C#, Go, Java, Node.js, Python, Ruby. Or view the sample browser.
• DML, DDL, and user-defined functions (UDF) syntax lets you manage and transform your BigQuery data.
• bq command-line tool reference documents the syntax, commands, flags, and arguments for the bq CLI interface.
• ODBC / JDBC integration connect BigQuery to your existing tooling and infrastructure.

BigQuery roles and resources

BigQuery addresses the needs of data professionals across the following roles and responsibilities.