Cloud data warehouse to
power your data-driven innovation
BigQuery is a completely serverless and cost-effective
enterprise data warehouse that works across clouds and
scales with your data, with BI, machine learning and AI
built in.
BigQuery ML enables
data scientists and data analysts to build and
operationalize ML models on planet-scale structured,
semi-structured, and now unstructured data directly inside
BigQuery, using simple SQL—in a fraction of the time. Export
BigQuery ML models for online prediction into Vertex AI or
your own serving layer. Learn more about the
models we currently support.
BigQuery Omni is a
fully managed,
multicloud analytics
solution that allows for cost-effective and secure data
analysis across clouds and shares results within a single
pane of glass. Within BigQuery Analytics Hub, securely
exchange data assets
internally and across organizations without data movement
and enhance analysis
with commercial, public, and Google datasets.
Real-time analytics with
built-in query acceleration
BigQuery has built-in
capabilities that ingest streaming data and make it
immediately available to query, along with native
integrations to streaming products like Dataflow. Analyze
large datasets interactively with BigQuery BI Engine,
an in-memory analysis service
that offers sub-second query response time and high
concurrency. BI Engine natively integrates with
Looker Studio
and works with many BI tools, including
Connected Sheets.
Query all data types
with BigQuery: structured, semi-structured, and
unstructured. Use BigLake to explore and
unify different data types
and build advanced models. Centrally discover, manage,
monitor, and govern data across
data lakes,
data warehouses, and data marts with consistent controls
with Dataplex, an
intelligent data fabric
that enables organizations to provide access to trusted
data.
BigQuery geospatial
uniquely combines the serverless architecture of BigQuery
with native support for geospatial analysis, so you can
augment your analytics workflows with location intelligence.
Simplify your analyses, see spatial data in fresh ways, and
unlock entirely new lines of business with support for
arbitrary points, lines, polygons, and multi-polygons in
common geospatial data formats.
Connected Sheets allows
users to analyze billions of rows of live BigQuery data in
Google Sheets without requiring SQL knowledge. Users can
apply familiar tools—like pivot tables, charts, and
formulas—to easily derive insights from big data. Learn more
about Connected Sheets in
the getting started guide.
Real-time change data
capture and replication
Synchronize data across
heterogeneous databases, storage systems, and applications
reliably and with minimal latency
with Datastream.
Datastream integrates with purpose-built and
extensible Dataflow templates to
pull change streams written to Cloud Storage, and create
up-to-date replicated tables in BigQuery for real-time
analytics.
Standard SQL
BigQuery supports a
standard SQL dialect that is ANSI 2011 compliant, which
reduces the need for code rewrites. BigQuery also provides
ODBC and JDBC drivers at no cost to ensure your current
applications can interact with its powerful engine.
Materialized Views
Accelerate query
performance and reduce costs within your environment
with BigQuery materialized views.
It is easy to set up, effortless to use, and best of all
it's real time, allowing you to quickly get answers to your
questions.
Petabyte scale
Get great performance
on your data, while knowing you can scale seamlessly to
store and analyze petabytes to exabytes of data with
ease.
Google Cloud
public datasets offer
a powerful data repository of more than 200 high-demand
public datasets from different industries. Google provides
free storage for all public datasets, and customers can
query up to 1 TB of data per month at no cost.
How It Works
BigQuery's serverless architecture lets you use SQL
queries to analyze your data. You can store and
analyze your data within BigQuery or use BigQuery to
assess your data where it lives. To test how it works
for yourself, query data—without a credit card—using
the BigQuery sandbox.
Demo:
Solving business challenges with an end-to-end
analysis in BigQuery
Common Uses
Transfer data into BigQuery
Bring any data
into BigQuery
Make analytics easier by
bringing together data from multiple sources into
BigQuery. You can upload data files from local
sources, Google Drive, or Cloud Storage buckets,
use BigQuery Data Transfer Service (DTS), Cloud
Data Fusion plugins, replicate data from
relational databases with Datastream for BigQuery,
or leverage Google's industry-leading data
integration partnerships.
Solve for today’s analytics
demands and seamlessly scale your business by
moving to Google Cloud’s enterprise data
warehouse. Streamline your migration path from
Netezza, Oracle, Redshift, Teradata, or Snowflake
to BigQuery using the free and fully managed
BigQuery Migration Service.
Gain a competitive advantage by
responding to business events in real time with
event-driven analysis. Built-in streaming
capabilities automatically ingest streaming data
and make it immediately available to query. This
allows you to stay agile and make business
decisions based on the freshest data. Or use
Dataflow to enable fast, simplified streaming data
pipelines for a comprehensive solution.
Learn how BigQuery and BigQuery ML
can help you build an e-commerce recommendation
system, predict customers' lifetime value, and design
propensity to purchase solutions.
Analyze and gain deeper insights
into your logging data with BigQuery. You can store,
explore, and run queries on generated data from
servers, sensors, and other devices simply using
GoogleSQL. Additionally, you can analyze log data
alongside the rest of your business data for broader
analysis all natively within BigQuery.
Marketing data warehouses let
you deliver timely, targeted, and tailored
advertising experiences that increase marketing
performance. BigQuery offers seamless data
connectors into Google Ads, Campaign Manager, and
other marketing platforms for a holistic view of
your business. Build advanced marketing audiences
with Google Analytics and BigQuery's built-in ML
to drive higher ROI.
From data ingestion to visualization, many partners have
integrated their data solutions with BigQuery. Listed above
are partner integrations through
Google Cloud Ready - BigQuery.
BigQuery is Google Cloud’s fully managed and
completely serverless enterprise data
warehouse. BigQuery supports all data types,
works across clouds, and has built-in machine
learning and business intelligence, all within a
unified platform.
An enterprise data warehouse is a system used for
the analysis and reporting of structured and
semi-structured data from multiple sources. Many
organizations are moving from traditional data
warehouses that are on-premises to cloud data
warehouses, which provide more cost savings,
scalability, and flexibility.
BigQuery offers robust security, governance, and
reliability controls that offer high availability
and a 99.99% uptime SLA. Your data is protected
with encryption by default and customer-managed
encryption keys.
There are a few ways to get started with
BigQuery. New customers get $300 in free credits
to spend on BigQuery. All customers get 10 GB
storage and up to 1 TB queries free per month, not
charged against their credits. You can get these
credits by signing up for the BigQuery free trial.
Not ready yet? You can use the
BigQuery sandbox
without a credit card to see how it works.
The
BigQuery sandbox
lets you try out BigQuery without a credit card.
You stay within BigQuery’s free tier
automatically, and you can use the sandbox to run
queries and analysis on public datasets to see how
it works. You can also bring your own data into
the BigQuery sandbox for analysis. There is an
option to upgrade to the free trial where new
customers get a $300 credit to try BigQuery.
Companies of all sizes use BigQuery to
consolidate siloed data into one location so you
can perform data analysis and get insights from
all of your business data. This allows companies
to make decisions in real time, streamline
business reporting, and incorporate machine
learning into data analysis to predict future
business opportunities.
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