Having analyzed your big data on BigQuery, you’d want to share it with your team members and boss. BigQuery API allows you to send your result queries to your co-workers in a text format. When you send your analytic reports in a text format, your colleagues will only be able to view them.
But what if one of your bosses is in charge of editing the analytical report before storage? Of course, you can permit a team member to become a BigQuery Editor for your dataset on the Cloud Console.
However, granting them access to your dataset from the Cloud Console will mean that they have Editor access on all datasets. This will prove counter-effective if you want to conceal the information on other datasets from them.
This is where the BigQuery API comes in. The BigQuery allows you to grant access permission to only one dataset at a time. This article will explore the key types of BigQuery API and their purpose. You’ll also get examples of situations where each API is used.
Table of Contents
Introduction to Google BigQuery API
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Every time you input your complex datasets into BigQuery, the system collects your data, analyses the data, and transmits the result queries to you. The tool responsible for this collection of data and transmission of results is the BigQuery API.
The BigQuery API enables a group of users to create, analyze, share and manage complex datasets. Using this API, you can also perform secondary functions like granting non-creators access to edit your data or transferring the result queries to your company’s storage system.
Like other APIs, the BigQuery API conceals essential programming data, only revealing what each user needs to process their data on the system.
You can access the BigQuery API service through Google client libraries such as Java and C++. However, if your application does not permit you to use other client libraries, you would have to access the BigQuery API manually, and use HTTP commands.
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Understanding Key Google BigQuery API Tools
The following information will help you use the API service without client libraries:
- BigQuery Discovery Document
- Service Endpoint
- REST Resource Function
1. BigQuery Discovery Document
This is a tool that describes BigQuery API at the surface level, concealing crucial programming data. The discovery document also shows you how to activate BigQuery API for your needs. BigQuery stores its discovery document in the link below:
https://bigquery.googleapis.com/discovery/v1/apis/bigquery/v2/rest
2. Service Endpoint
The service endpoint states the official URL of BigQuery API. The network address of BigQuery API is:
https://bigquery.googleapis.com
3. REST Resource Function
This section will show how to input a valid command for the BigQuery API. You can use these functions to manage your BigQuery projects, datasets and models, and jobs. In this section, we’ll only address the REST resource functions for datasets.
REST Resource Functions for Datasets
| Function | Command | Purpose |
| Get | GET /bigquery/v2/projects/{projectId}/datasets/{datasetId} | This function tells the API to get information about the dataset indicated in your dataset Id. |
| Delete | DELETE /bigquery/v2/projects/{projectId}/datasets/{datasetId} | This command deletes the dataset indicated in the dataset Id. |
| List | GET /bigquery/v2/projects/{projectId/Datasets | When you use this command, the system will list all the datasets in the indicated project. Only users who have been granted the position of READER will be able to view these datasets. |
| Insert | POST /bigquery/v2/project/{projectId}/Datasets | Use this command to create a new dataset in your project. |
| Update | PUT / bigquery/v2/project/{projectId}/datasets/{datasetId} | This command lets you add new information to an existing dataset |
| Patch | PATCH / bigquery/v2/project/{projectId}/datasets/{datasetId} | You can also use the ‘patch’ resource to include new information in your dataset. |
Prerequisites for Leveraging Google BigQuery APIs
Here are a few prerequisites required to utilize BigQuery API to its fullest:
Installing Google Client Library
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Before you can use any BigQuery API, you have to install a Google client library. Let’s assume that you’ve decided to use the Python client library. To use this client library, you must have installed Python on your computer. You also need to set up a code editor like Visual Studio code on your system.
Once you have installed Python and Visual Studio Code, you can copy BigQuery’s installation code for the Python client library, and post it on Visual Studio Code’s terminal. The installation code for Python is :
pip install –upgrade google-cloud-bigquery
Creating a Google BigQuery Service Account
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After setting up your client library, you’ll also have to create a BigQuery service account. Your service account gives you the access to authorize and access data in an API.
Follow these steps to create a service account:
- Step 1: Go to the Google Cloud Console
- Step 2: Select ‘APIs and Services’. Then, click on ‘Credentials.’
- Step 3: Next, choose ‘Create Credentials’.
- Step 4: Click on ‘Service Account to set up a new service account.
BigQuery is a very secure service. So, the system won’t grant you access to your service account until you can produce your service account key.
Here’s how to set up a service account key:
- Step 1: Locate the Keys tab in your new service account and click on it. This will reveal a drop-down menu.
- Step 2: Select ‘Add Key’ in the menu. Then, click on ‘Create new key’.
- Step 3: You’ll see a list of key types. Choose ‘JSON’. Your computer will download the JSON file automatically. In addition, you’ll also receive authentication credentials that you can copy to your Visual Studio Code terminal to create an environmental variable.
Key Types of Google BigQuery APIs and their Use Cases
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There are five key types of BigQuery API. They are
- Core API
- Storage API
- Data Transfer API
- Reservation API
- Connection API
1. Core API
The Core API lets users interact with core BigQuery resources like datasets and jobs. That said, you cannot create data or update the information in a dataset if you don’t have the owner’s access. You need the original creator of the dataset to permit you to become a dataOwner on GoogleCloud.
2. Storage API
The Storage API allows you to scan large amounts of data on other websites or applications.
3. Data Transfer API
This API makes it possible for you to transfer data from one application or website into BigQuery. You can use the API to get data from Google services like Campaign Manager and YouTube or third-party applications like Amazon Redshift and Teradata.
4. Reservation API
The Reservation API helps developers manage dedicated BigQuery resources such as the BigQuery BI memory analysis machine, and slots, which BigQuery uses to analyze queries.
5. Connection API
The purpose of the Connection API is to build a connection with an external data source. With this API, users can query data in the external data source without moving it to BigQuery. Examples of such remote data sources are Bigtable, Google Drive, and Cloud SQL.
Practical Examples of BigQuery API Use Cases
- Core API Example: Querying a dataset for job applicants’ names and grades will require you to use the Core API.
- Storage API Example: If you want to find the pages on your website with the highest views, you will use the Storage API.
- Data Transfer API Example: Transferring data from your YouTube channel to BigQuery.
- Reservation API Example: Buying BigQuery slots will require you to use the Reservation API.
- Connection API Example: You will use the Connection API if you want to query data from CloudSQL without copying the data to BigQuery.
Conclusion
Using a real-time example, we have worked with BigQuery API. In addition, we have discussed the data software’s key types and use cases. Now is the time to implement lessons learned in this tutorial for efficient business teamwork and collaboration.
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