Wednesday, April 24, 2024

Hasura + BigQuery for Dynamic GraphQL APIs : Data Revolution

Using Hasura to power a GraphQL API over your BigQuery dataset

Google Cloud BigQuery is a crucial tool for building a data warehouse that can handle massive data sets with simplicity and scalability. Assume that you have established data pipelines to manage the datasets and that you have standardized on utilizing BigQuery.

Choosing the most effective way to make this data accessible to apps would be the next step. For this, APIs are often the best option. What you wanted to do was looking at a service that would make it simple for me to establish an API around my data sources, in this instance BigQuery.

They learn how to utilize Hasura, an open-source program, in this blog article. Hasura enabled me to build an API around my BigQuery dataset.

The simplicity of using an API to publish your domain data is the primary factor in favor of using Hasura. Hasura is compatible with several data sources, such as BigQuery, AlloyDB, and Google Cloud SQL. Through metadata setup, you have control over the model, relationships, validation, and permission logic.

Your GraphQL and REST APIs are generated by Hasura using this information. It offers a low-code data to API experience without sacrificing any of the speed, security, or flexibility that you want from your data API.

Although Hasura is free software, it is also available in fully managed versions on a number of cloud service providers, such as Google Cloud.


A Google Cloud Project is required. Please make a note of the project’s ID, since we will need it for subsequent usage in the Hasura settings.

Google Trends dataset – BigQuery dataset

Having a GraphQL API centered on our BigQuery dataset is our ultimate objective. So, a BigQuery dataset is what we must have built up. The Google Trends database that BigQuery’s Public Datasets initiative makes accessible is the one they have selected. This is an intriguing dataset that provides the top 25 overall or top 25 growing searches from Google Trends over the last 30 days, both domestically and internationally.

You copied the dataset and tables from the bigquery-public-data dataset and generated a sample dataset in BigQuery for their Google Cloud project called “google trends.”

The international top terms, which enable me to see trends across nations backed by the publicly accessible Google Trends information, is what you are interested in.

Account for services

Before we get to the Hasura setup, please be aware that creating a service account with the appropriate permissions is necessary for the interface between Hasura and Google Cloud. They will provide Hasura access to that service account so it can use BigQuery’s appropriate operations to set up and get the results.

In Google Cloud, creating a service account is simple and may be done using the Google Cloud Console.

You must export the service account using its credentials (JSON) file once it has been established. They will need that file in the next part, so please keep it secure.

Hasura arrangement

First, you have to register with Hasura. After signing it, choose New Project, Free Tier, and Google Cloud to host the Hasura API Layer, as seen below. Additionally, before clicking the Create Project button, you must choose which Google Cloud region to host the Hasura service in.

create Project
image credit to google

Configuring the data link

  • After the project is built, you must set up the Data Source that Hasura needs to setup and communicate with, as well as the communication between Hasura and Google Cloud.
  • Once the data connection has been established properly, you may choose which tables need monitoring. You can see that Hasura searched the metadata to locate the tables in the dataset by going to the Datasource settings.
  • After doing this, the table will be marked as monitored, and we can test the queries using the GraphQL Test UI.
  • Using the wonderful Explorer UI that the API tab offers, you can easily construct the GraphQL query.
  • This was very easy, and in a matter of minutes, my apps could be served by a GraphQL layer.
Agarapu Ramesh was founder of the Govindhtech and Computer Hardware enthusiast. He interested in writing Technews articles. Working as an Editor of Govindhtech for one Year and previously working as a Computer Assembling Technician in G Traders from 2018 in India. His Education Qualification MSc.


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