What’s Big Query?
- April 30, 2020
Yes. You have an opportunity to store and query massive datasets on Google Cloud Platform. Big Query which is an is an enterprise data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google’s infrastructure.
Google BigQuery is a cloud-based enterprise data warehouse that offers rapid SQL queries and interactive analysis of massive datasets. BigQuery was designed on Google’s Dremel technology and is built to process read-only data.
The platform utilizes a columnar storage paradigm that allows for much faster data scanning as well as a tree architecture model that makes querying and aggregating results significantly easier and more efficient. Additionally, BigQuery is serverless and built to be highly scalable thanks to its fast deployment cycle and on-demand pricing.
Storing and querying massive datasets can be time consuming and expensive without the right hardware and infrastructure. Simply move your data into BigQuery and Google Cloud can handle the hard work. You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.
Access is control by default Google Cloud Access Credentials policy. There are lots of opportunity to access BigQuery by using the Cloud Console, by classic web UI, by using a command-line tool, or by making calls to the BigQuery REST API or by using a variety of client libraries such as Java, .NET, or Python. There are also a variety of third-party tools that you can use to interact with BigQuery, such as visualizing the data or loading the data.
BigQuery is fully-managed. To get started, you don’t need to deploy any resources, such as disks and virtual machines. Get started now by running a web query or using the command-line tool.
A BigQuery table contains individual records organized in rows. Each record is composed of columns (also called fields).
Every table is defined by a schema that describes the column names, data types, and other information. You can specify the schema of a table when it is created, or you can create a table without a schema and declare the schema in the query job or load job that first populates it with data.
BigQuery supports the following table types:
- Native tables: tables backed by native BigQuery storage.
- External tables: tables backed by storage external to BigQuery.
- Views: Virtual tables defined by a SQL query.
BigQuery tables are subject to the following limitations:
- Table names must be unique per dataset.
- The Cloud Console and the classic BigQuery web UI support copying only one table at a time.
- When copying tables, the destination dataset must reside in the same location as the table being copied. For example, you cannot copy a table from an EU-based dataset to a US-based dataset.
- When copying multiple source tables to a destination table by using the CLI, the API, or the client libraries, all source tables must have identical schemas.
- You can only delete one table at a time by using the Cloud Console, the classic BigQuery web UI, the command-line tool, the API, or the client libraries.
- When exporting table data, the only supported destination is Cloud Storage.
- As you approach 50,000 or more tables in a dataset, enumerating them becomes slower. Enumeration performance suffers whether you use an API call or the classic BigQuery web UI. Currently, the BigQuery web UI in the Cloud Console allows you to display only 50,000 tables per dataset.To improve classic BigQuery web UI performance, you can use the
?minimalparameter to limit the number of tables displayed to 30,000 tables per project. You add the parameter to the classic BigQuery web UI URL in the following format: