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Google Bigquery

What is Google BigQuery?

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)

BigQuery fundamentals

The user should understand the four main concepts of BigQuery

Projects:

  • top-level containers in Google Cloud Platform
  • store information about billing and authorization
  • contain BigQuery data
  • has a name and an ID
  • each project is billed separat

Tables:

  • contain the users data in BigQuery
  • has a schema that describes field names, types and more information
  • support tables, view and external tables

Datasets:

  • allow the user to control the access to the tables
  • allow the user to organize the tables
  • at least one dataset is needed
  • data from BigQuery is shared with others by the help of ACL on datasets

Jobs:

  • are actions, constructed by the user to load, export, query and copy data
  • can be executed asynchronously
  • BigQuery saves the history of all jobs
  • access via the Google Cloud Platform Console

Functionality

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.

Pricing

Google BigQuery has a flexible pricing system. 2)

Action Cost
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
Loading data Free
Copying data Free
Exporting data Free
Metadata operations Free

SQL Example

Example SQL Code to interact with BigQuery.

SELECT
  customer,
  id,
  COUNT(purchases)
FROM
  mydata:db.customer
WHERE
  purchases >= 1
GROUP BY
  purchases,
  customer

Partnerships and Integrations

  • looker 3)
  • tableau 4)
  • Qlik 5)
  • talend 6)
  • Google Analytics 360 Suite 7)
  • snapLogic 8)
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  • Bookmark "Google Bigquery" at myAOL
  • Bookmark "Google Bigquery" at Slashdot
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concepts/google-bigquery.txt · Last modified: 2016/05/19 22:35 by Philipp T