What type of API does Google BigQuery provide?
Google BigQuery uses a REST API. REST (Representational State Transfer) is an architectural style for designing networked applications, and it's widely used for building web services.
The BigQuery API allows developers to create, manage, share, and query data. It provides various REST resources for interacting with different aspects of BigQuery, such as datasets, jobs, models, projects, routines, tables, and more.
The BigQuery API is structured around REST resources. Some of the main resources include:
- v2.datasets
- v2.jobs
- v2.models
- v2.projects
- v2.routines
- v2.tables
To use the BigQuery API:
- Google recommends using their provided client libraries for the best experience.
- If you need to use your own libraries, you can make direct HTTP requests to the API endpoints.
Google provides comprehensive documentation for the BigQuery API, including:
- REST reference
- RPC reference
- Information on authentication and authorization
- Details on available operations for each resource
In summary, Google BigQuery offers a robust REST API that provides developers with powerful tools to interact with BigQuery's data platform.
Does the Google BigQuery API have webhooks?
The official Google BigQuery API does not have native webhook support. However, Google Cloud offers webhook functionality through other services that can be integrated with BigQuery.
Google Cloud Application Integration provides a Webhook trigger that can be used to invoke integrations based on events. This Webhook trigger is part of the Connector Event trigger system.
When setting up a Webhook trigger, you can configure:
- Listener Authentication: Options include No Authentication, Google Authentication, API Key Authentication, and Basic Authentication.
- Event Subscription: This is mandatory for the webhook connector.
- Private connectivity can be enabled for secured connectivity between your backend application and the connection.
While the Webhook trigger itself is not part of the BigQuery API, it can be used in conjunction with BigQuery:
- You can create integrations that solve complex business problems, such as starting the execution of an entire integration based on an event.
- The data received through webhooks can be processed and then loaded into BigQuery for analysis.
Examples of use cases include:
- GitHub Webhook: Can be triggered by events like pull requests, commits, or comments, and relay data to BigQuery.
- Slack Webhook: Can capture channel messages and send them to BigQuery.
- HubSpot Webhook: Can be triggered by object creation or meeting specific criteria, sending data to BigQuery.
In summary, while the official Google BigQuery API doesn't have native webhook support, you can use Google Cloud's Application Integration and Cloud Functions to create webhook endpoints that can process events and store data in BigQuery. This allows for real-time data ingestion and analysis in BigQuery triggered by external events.
Rate Limits and other limitations
Here are the key API rate limits for the Google BigQuery API:
BigQuery API (Core) Requests
-
Requests per day: Unlimited
-
Maximum tabledata.list bytes per minute:
- 7.5 GB in multi-regions
- 3.7 GB in all other regions
-
Maximum API requests per second per user per method: 100 requests
-
Maximum concurrent API requests per user: 300 requests
-
Maximum jobs.get requests per second: 1,000 requests
-
Maximum tabledata.list requests per second: 1,000 requests
-
Maximum tables.insert requests per second: 10 requests
Streaming Inserts
-
Maximum bytes per second per project:
- 1 GB/s in us and eu multi-regions
- 300 MB/s in other locations
-
Maximum rows per request: 50,000 rows (500 recommended)
-
Maximum row size: 10 MB
-
HTTP request size limit: 10 MB
BigQuery Storage Read API
-
Read data plane requests per minute per user: 25,000 requests
-
Read control plane requests per minute per user: 5,000 requests
Key Points
- Many limits are per-project or per-user
- Streaming insert limits are higher than other API limits
- The BigQuery Storage Read API can sustain higher throughput than tabledata.list
- Exceeding limits can result in throttling or quota errors
- Some limits can be increased by contacting Google Cloud support
It's important to note that these limits may change over time, so it's always best to check the official documentation for the most up-to-date information. Additionally, some limits may vary based on factors like project type, region, and usage patterns.
Latest API Version
Based on the search results, the most recent version of the Google BigQuery API is v2. Here are the key points:
API Version
- The BigQuery API is currently at version 2 (v2).
Key Details
-
The REST resources for the BigQuery API v2 include datasets, jobs, models, projects, routines, rowAccessPolicies, tabledata, and tables.
-
The API allows developers to create, manage, share and query data using BigQuery.
Latest Client Library Version
- The latest version of the Google BigQuery Python client library is 3.25.0, released on June 20, 2024.
Usage Example
Here's a basic example of using the BigQuery Python client library:
from google.cloud import bigquery
client = bigquery.Client()
# Perform a query
QUERY = (
'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` '
'WHERE state = "TX" '
'LIMIT 100')
query_job = client.query(QUERY) # API request
rows = query_job.result() # Waits for query to finish
for row in rows:
print(row.name)
This example demonstrates how to perform a simple query using the BigQuery client library.
Additional Information
- The last version of the library compatible with Python 2.7 and 3.5 is google-cloud-bigquery==1.28.0.
- BigQuery is designed for querying massive datasets quickly using Google's infrastructure.
It's important to note that while the API version is v2, the client libraries and other components may have their own version numbers that are updated more frequently.
How to get a Google BigQuery developer account and API Keys?
To get a developer account for Google BigQuery and create an API integration, you need to follow these steps:
1. Create a Google Cloud project
- Go to the Google Cloud Console [https://console.cloud.google.com/]
- Create a new project or select an existing project
2. Enable the BigQuery API
- Navigate to the APIs & Services > Library section in the Google Cloud Console
- Search for "BigQuery API" and enable it for your project
3. Set up billing
- Ensure billing is enabled for your project
- If you're offered a free trial, you can accept it, but you'll need to enter billing details for continued use after the trial
4. Create a service account
- Go to the IAM & Admin > Service Accounts page in the Google Cloud Console
- Create a new service account for your project
- Download the JSON key file for the service account, which you'll use for authentication
5. Set up permissions
- Grant the necessary permissions to your service account in the Google Cloud Console
- For BigQuery, you typically need roles like "BigQuery Data Editor" or "BigQuery Job User"
6. Use the BigQuery API
- Use the service account credentials to authenticate your API requests
- You can use client libraries provided by Google for various programming languages to interact with the BigQuery API
What can you do with the Google BigQuery API?
Based on the search results provided, here is a list of data models that can be interacted with using the Google BigQuery API, along with what is possible for each:
Datasets
- Create, manage, and organize datasets
- List available datasets in a Google Cloud project
- Act as containers for tables, views, and other data objects
Tables
- Create, manage, and query tables
- Get details about a table within a dataset (e.g., fields and data types)
- Store actual data, defined by a schema
- Support structured and semi-structured data
Jobs
- Submit and manage jobs (queries, load jobs, extract jobs, etc.)
Models
- Create and manage machine learning models
- Use BigQuery ML to build, train, and deploy ML models using SQL syntax
- Perform tasks like regression, classification, and forecasting
Projects
- Manage project-level resources and settings
Routines
- Create and manage user-defined functions and stored procedures
Row Access Policies
- Implement and manage row-level security
Views
- Create and manage views (virtual tables defined by SQL queries)
Data Transfer
- Schedule and manage data transfers from various sources
- Set up automated ingestion workflows
Connections
- Establish remote connections to interact with external data sources
Data Policies
- Manage column-level security and data masking policies
Query Execution
- Run SQL queries on datasets
- Perform interactive and ad-hoc queries
- Execute blazing fast queries on petabyte-scale datasets
Storage
- Leverage Google Cloud Storage for data storage
- Optimize storage for query performance
Integration
- Connect with other Google Cloud Platform services
- Integrate with third-party tools and libraries
Advanced Analytics
- Perform data visualization using tools like Data Studio or Looker
- Utilize Python libraries for advanced data manipulations and statistical analysis
- Integrate with Google Analytics for deeper insights
Real-time Processing
- Handle streaming data and real-time analytics
This list covers the main data models and interactions possible with the Google BigQuery API, based on the provided search results. The API offers a comprehensive set of features for data storage, management, analysis, and integration within the Google Cloud ecosystem.
