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Getting started with Google BigQuery
Our platform enables you to quickly and easily connect to Google BigQuery.
This article will cover all of the basic requirements that will be needed to run BigQuery connectors.
Before we continue with a step by step guide on setting up Google BigQuery, we first need to introduce you to a few basic Google BigQuery concepts.
Basic BigQuery Concepts
Projects are top-level containers in the Google Cloud Platform. They store information about billing and authorized users, and they contain BigQuery data. Each project has a name and a unique ID.
A dataset is contained within a specific project. Datasets are top-level containers that are used to organize and control access to your tables and views. A table or view must belong to a dataset, so you need to create at least one dataset before you can start using Google BigQuery.
You will need to create a dataset within your project before you will be able to add any tables.
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.
Basic requirements for BigQuery
Please follow these steps in order to get started with BigQuery:
Select or create a Google Cloud Platform project.
Make sure that billing is enabled for your Google Cloud Platform project.
- BigQuery is automatically enabled in new projects. To activate BigQuery in a pre-existing project, go to Enable the BigQuery API.
If you would like to read up some more on this topic, please take a look at the official Google BigQuery Quickstart guide.