As you already know, Google BigQuery uses Standard SQL statements to present the data that we request to the different databases with which we are working.
This language shares much of its syntax with other SQL languages, so it’s very easy to learn if you’ve already dealt with Oracle or MySQL, for example.
The article that I write today tries to make a small review of the basic sentences which are commonly used in Google BigQuery to dig into the available data.
Content
- What is BigQuery?
- SQL SELECT statement for BigQuery
What is BigQuery?
Perhaps you have already used Google’s BigQuery or perhaps you are getting information, in any case I would like to define what BigQuery is and what it is for.
BigQuery is a data analysis service scale provided as part of the Google Cloud platform.
It is a fully managed data warehouse service designed to be fast and easy to use.
BigQuery allows you to run fast and efficient SQL queries on very large data sets.
Users can load data, run queries and export the results with great ease.
Instead of needing to manage their own database infrastructure, users can take advantage of Google’s large-scale infrastructure to analyze their data.
Some of the key features of BigQuery include:
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Scale and performance: BigQuery can handle petabytes of data and thousands of concurrent users with ease.
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Flexibility: BigQuery allows users to store and analyze any type of data, structured or semi-structured, such as JSON, Avro, CSV, among others.
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Full SQL support: BigQuery uses a standard SQL dialect, making it easy for those already familiar with SQL to adapt.
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Security: Data in BigQuery is encrypted at rest and in transit, and users can control access to their data at the dataset, table, or column level.
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Integration with other Google Cloud services: BigQuery integrates with other Google Cloud services, such as Google Data Studio for visualizations, Google Cloud Storage for data warehousing, and Google Cloud Machine Learning for machine learning models.
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Usage-based pricing: BigQuery is billed based on the amount of data stored and the amount of data processed by the queries.
SQL SELECT statement for BigQuery
In BigQuery you will end up using standard SQL, like the SELECT SQL query.
In SQL, the SELECT query is one of the most fundamental. It is the one you use when you want to retrieve data from your database.
In its most basic form, it selects columns from a table. The general syntax looks like this:
SELECT column1, column2, ... FROM table_name;
Where column1, column2, … are the names of the columns you want to select, and table_name is the name of the table you want to select from.
For example, if you have a table called customers with columns customer_id, name, email, and phone, you can select all names and emails from the customers table with a query like this:
SELECT nombre, correo FROM clientes;
That will return something like this:
| name | |
|---|---|
| Juan | [email protected] |
| Ana | [email protected] |
| Luis | [email protected] |
You can also select all the columns of a table using * instead of specifying each column:
SELECT * FROM clientes;
This will return all the data from the customers table.
Also, SQL (and therefore BigQuery) is case sensitive in column and table names, so SELECT name FROM clients; not the same as SELECT Name FROM Customers; unless the columns and tables are actually capitalized that way.
Finally, it is worth mentioning that bigquery supports many advanced functions and operators in SELECT queries, allowing you to do things like filter results, sort results, group results, and much more. But for starters, this is the basic idea!