Analyze Big Data in the cloud with BigQuery. Run fast, SQL-like queries against multi-terabyte datasets in seconds. Scalable and easy to use, BigQuery gives you real-time insights about your data.
Explore correlation between:
SELECT actor, repository_language, COUNT(repository_language) as pushes FROM [githubarchive:github.timeline] WHERE type='PushEvent' and repository_language == '' or repository_language == '' and PARSE_UTC_USEC(created_at) >= PARSE_UTC_USEC('2012-01-01 00:00:00') and PARSE_UTC_USEC(created_at) < PARSE_UTC_USEC('2013-01-01 00:00:00') GROUP BY actor, repository_language ORDER BY pushes DESC LIMIT 1000
List the top 10 repositories by total number of forks
SELECT repository_url, MAX(repository_forks) as total_number_of_forks FROM [githubarchive:github.timeline] WHERE PARSE_UTC_USEC(created_at) >= PARSE_UTC_USEC("-- 00:00:00") AND PARSE_UTC_USEC(created_at) < PARSE_UTC_USEC("-- 00:00:00") GROUP BY repository_url ORDER BY total_number_of_forks DESC LIMIT 10
Search for the number of occurrences of a token across all commits per language:
SELECT repository_language, COUNT(*) AS cntlang FROM [githubarchive:github.timeline] WHERE repository_language != '' AND payload_commit_msg != '' AND PARSE_UTC_USEC(created_at) > PARSE_UTC_USEC('2014-03-09 00:00:00') AND REGEXP_MATCH(payload_commit_msg, r'(?i)\b()\b') GROUP BY repository_language ORDER BY cntlang DESC
All behind the scenes
Your queries can execute asynchronously in the background, and can be polled for status. Access a history of your jobs and queries alongside the rest of your Cloud Platform resources in the Google Developers Console.
Import data with ease
Bulk load your data using Google Cloud Storage or stream it in bursts of up to 100,000 rows per second. You can tackle real-time, high-volume logging or transaction recording and have your data available for analysis quickly and easily.
Affordable big data
When we say “Big Data”, we really mean it. The first 1 TB of data processed each month is free. We will store and query thousands of terabytes for you. Our pricing structure is simple and transparent, and we give you full visibility and control. You only pay for the storage you need and the queries you run.
The right interface
Separate interfaces for administration and developers will make sure that you have access to the tools you need. Control access at both the project and dataset level using the Google Developers Console. BigQuery helps teams of any size work the way they want to work.
Learn how the data-crunching capabilities of BigQuery are being put to use by other companies.
"We are always looking for ways to maximize return and minimize investment. BigQuery is the perfect combination. It’s an on-demand, scalable resource."Read Interactions Marketing's story
"We explored several data analytics solutions. Nothing comes remotely close to the sheer power of Google BigQuery. It made large-scale data collection and crunching possible with little effort, which has translated to a significant business advantage."Read redBus's story
The Pricing Calculator provides you with a simple tool that can help you get a sense of what an application running on Google Cloud Platform could cost.
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When charging in local currency, Google will convert the prices listed into applicable local currency pursuant to the conversion rates published by leading financial institutions.
BigQuery uses a columnar data structure, which means that for a given query, you are only charged for data processed in each column, not the entire table. The first 1 TB of data processed per month is at no charge.
|Storage||$0.020 per GB / month1,4|
|Interactive Queries||$5 per TB processed2,3,4|
|Batch Queries||$5 per TB processed2,3,4|
|Streaming Inserts||$0.01 per 100,000 rows until August 12, 2015. After August 12, 2015, $0.01 per 200 MB, with individual rows calculated using a 1 KB minimum size.|
2 Charges rounded up to the nearest MB; minimum 1 MB data processed per each table referenced by a query
3 The first 1 TB of data processed per month is at no charge
4 Charges are based on the uncompressed data size.