Google BigQuery is an Analytics Data Warehouse. It is fully managed, scaled in petabytes and low costed. Google BigQuery gives the user the infrastructure and hardware to query massive datasets, which are otherwise expensive and time consuming. This problem is solved from the company Google by enabling super-fast SQL queries against append-only tables. Thus Google BigQuery uses the processing power of googles infrastructure. The user stores his or her data on Google BigQuery and pays for the usage of googles processing power (for more information see pricing). The user can control the access of the data and give other users the ability to query and view the data. The user gains access to BigQuery by using the graphical user interface in the web, a command-line tool or using the BigQuery REST API with Java, .NET or Python. Furthermore, many third-party tools may be used to interact with BigQuery for loading and visualizing data. 1)
The user should understand the four main concepts of BigQuery
The user has three main ways to interact with BigQuery. Loading and exporting data: the user loads his data into BigQuery and can export it again from BigQuery if necessary.
Querying and viewing data: after the data is loaded into BigQuery, the user can query the loaded data and view it.
Managing data: the user manages the loaded data in BigQuery.
Google BigQuery has a flexible pricing system. 2)
|Storage||$0.02 per GB, per Month|
|Long Term Storage||$0.01 per GB, per month|
|Streaming Inserts||$0.01 per 200 MB|
|Queries||$5 per TB|
Example SQL Code to interact with BigQuery.
SELECT customer, id, COUNT(purchases) FROM mydata:db.customer WHERE purchases >= 1 GROUP BY purchases, customer