In today's digital age, data has become an increasingly valuable asset. Businesses are always looking for ways to extract insights from their data to improve decision-making and gain a competitive edge. One powerful tool for analyzing massive amounts of data is Google BigQuery. By integrating BigQuery into your Next JS site, you can streamline data collection and enhance your data analysis capabilities. In this article, we will guide you through the process of sending data from your Next JS site to Google BigQuery.

Understanding the basics of Next JS and Google BigQuery

Before we dive into the technical details, let's take a moment to define Next JS and Google BigQuery.

What is Next JS

Next JS is a popular React framework for building server-side rendered web applications. Its powerful features include server-side rendering, static site generation, and API routes. With Next JS, you can build fast, SEO-friendly web applications that are easy to maintain and scale.

But what exactly is server-side rendering? Server-side rendering is the process of rendering web pages on the server instead of the client. This means that when a user requests a page, the server sends back a fully rendered HTML page, which can improve SEO and performance.

Static site generation, on the other hand, is the process of generating HTML files at build time instead of runtime. This can lead to faster load times as the HTML files can be cached by CDNs and search engines.

API routes allow you to create serverless API endpoints within your Next JS application. This can be useful for handling form submissions, fetching data from external APIs, and more.

What is Google BigQuery

Now, let's talk about Google BigQuery. Google BigQuery is a cloud-based data warehouse that enables businesses to analyze massive amounts of data quickly and efficiently.

But what exactly is a data warehouse? A data warehouse is a large, centralized repository of data that is used for analysis and reporting. It is designed to support business intelligence activities, such as data mining, online analytical processing, and decision support.

With BigQuery, you can store and analyze petabytes of data using a SQL-like interface. This means that you can use familiar SQL syntax to query and analyze your data, making it easy for analysts and data scientists to get started with the platform.

BigQuery also integrates with popular data visualization tools from Google, such as Looker Studio and Looker. This means that you can easily create interactive dashboards and reports to share with your team.

Setting Up Your Next JS Project

Before we can start sending data to Google BigQuery, we first need to set up our Next JS project. Setting up a Next JS project involves several steps, including installing Next JS and configuring your project structure. In this article, we will walk through the steps involved in setting up your Next JS project.

Installing Next JS

The first step in setting up your Next JS project is to install Next JS. Next JS is a React framework that provides server-side rendering, automatic code splitting, and other features that make it easy to build production-ready web applications. To install Next JS, first, you need Node.js. Once you have Node.js installed, then you can use the following command to create the boilerplate code for the Next JS app:

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npx create-next-app@latest my-app

This command will create a new Next JS project in a folder named "my-app." Once the installation is complete, you can navigate to the "my-app" directory and start the development server using the following command:

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cd my-app

npm run dev

Configuring Your Project Structure

Next JS allows you to organize your project in a variety of ways. In this article, we will use a simple project structure that includes a single page and a component. This structure includes the following directories/files:

1. pages/: This directory contains your application's pages. Each file corresponds to a route based on its file name. For example, pages/index.js is the entry point of your application (i.e., it corresponds to the / route).
2. app/: This directory works alongside the pages directory to allow for incremental adoption.
3. public/: Folder containing static assets to be served.
4. package.json: File containing the list of project dependencies and scripts.
5. next.config.js: Configuration file for Next.js

With this project structure in place, we are ready to start building our Next JS application. In the next section, we will explore how to set up Google BigQuery.

Setting up Google BigQuery

Now that we have our Next JS project set up, we can start integrating Google BigQuery with our site. Let's walk through the steps involved in setting up BigQuery integration.

Creating a Google Cloud Platform account

The first step in integrating Google BigQuery with your Next JS site is to create a Google Cloud Platform account. You can do this by following these steps:

1. Go to the Google Cloud Console (console.cloud.google.com).
2. If you don't have an account, click "Create Account" and follow the prompts.
3. If you already have an account, click "Sign In" and enter your email and password.

Setting up a BigQuery dataset and table

Once you have a Google Cloud Platform account, enable Google BigQuery API, and then go ahead with setting up a BigQuery dataset and table. You can do this using the following steps:

1. Go to the BigQuery console (console.cloud.google.com/bigquery).
2. Click "Create Dataset."
3. Enter a name for your dataset and click "Create."
4. Click on the dataset you just created.
5. Click "Create Table."
6. Enter a name for your table and select the schema for your table.
7. Click "Create Table."

You may create the dataset and tables programmatically as well. We will cover that part in upcoming sections of this tutorial.

Obtaining your Google Cloud API Key

The final step in setting up BigQuery integration is to obtain your Google Cloud API key. You can do this using the following steps:

1. Go to the Google Cloud Console and navigate to the "APIs & Services" page.
2. Click "Create Credentials" and select "API Key."
3. Copy the API key and save it somewhere safe.

Implementing data collection in your Next JS application

Now that we have our BigQuery integration set up, we can start collecting data in our Next JS application. Let's walk through the steps involved in implementing data collection.

Creating a data collection function

The first step in implementing data collection is to create a function that will collect data from your site. You can do this using the following code:

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function collectData() { // your code here}

This function can be used to collect data from your site whenever a user performs an action.

Adding event listeners to capture user interactions

The next step in implementing data collection is to add event listeners to capture user interactions. You can do this using the following code:

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document.addEventListener('click', collectData);document.addEventListener('submit', collectData);

This code adds event listeners to capture whenever a user clicks on or submits a form on your site.

Storing collected data in a JSON format

The final step in implementing data collection is to store your collected data in a JSON format. You can do this using the following code:

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var data = {

'event_type': 'click',

'event_data': {

'location': 'https://www.example.com',

'time': new Date().toISOString(),

'user_agent': navigator.userAgent,

}

};

var json_data = JSON.stringify(data);

This code stores the collected data in a JSON format and can be sent to BigQuery using the code we will cover in the next section.

Sending data from Next JS to Google BigQuery

Now that we have collected data in our Next.js app, we can send it to Google BigQuery for analysis. Let's walk through the steps involved in sending data to BigQuery.

Then you can use BigQuery APIs to interact with our Google BigQuery instance. We will use the Node.js package provided by Google Cloud `@google-cloud/bigquery`.

Installing and Configuring the Google Cloud SDK

The first step in sending data to BigQuery is to install and configure the Google Cloud SDK for BigQuery. You can do this using the following command:

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npm install @google-cloud/bigquery

Once the package is installed and you authenticate with your service account, you can then write code to use the SDK to configure your BigQuery dataset. In the following example, we will create a new dataset programmatically:

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// Imports the Google Cloud client library
const { BigQuery } = require('@google-cloud/bigquery');

async function createDataset() {
  // Initialize the client
  const bigqueryClient = new BigQuery();

  // Create the dataset
  const [dataset] = await bigqueryClient.createDataset(“my_dataset”);
  console.log(`Dataset ${dataset.id} created.`);
}
createDataset();

Once the dataset is created, you can go ahead and create the tables as required:

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 async function createTable() {
    // Creates a new table named "my_table" in "my_dataset".
    const datasetId = "my_dataset";
    const tableId = "my_table";
    const schema = 'Name:string, Age:integer, Weight:float, IsMagic:boolean';

    // For all options, see https://cloud.google.com/bigquery/docs/reference/v2/tables#resource
    const options = {
      schema: schema,
      location: 'US',
    };

    // Create a new table in the dataset
    const [table] = await bigquery
      .dataset(datasetId)
      .createTable(tableId, options);

    console.log(`Table ${table.id} created.`);
  }
  createTable();

The source code for the SDK and code samples for various BigQuery data operations are available at https://github.com/googleapis/nodejs-bigquery.

Writing a function to send data to BigQuery

The next step in sending data to BigQuery is to write a function that will send your data to BigQuery. You can do this using the following code:

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const { BigQuery } = require('@google-cloud/bigquery'); 
async function sendDataToBigQuery(jsonData) { 
const projectId = 'YOUR_PROJECT_ID'; 
const datasetId = 'my_dataset'; 
const tableId = 'my_table'; 
const bigqueryClient = new BigQuery({projectId: projectId}); 
const rows = JSON.parse(jsonData); 
try { 
await bigqueryClient.dataset(datasetId).table(tableId).insert(rows);
console.log(`Inserted ${rows.length} rows`); 
} catch(err) { 
console.error('ERROR:', err); 
});
}

This code creates a BigQuery client and sends the collected data to your BigQuery table.

Handling Errors and Retries

The final step in sending data to BigQuery is to handle errors and retries. You can do this using the following code:

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function sendData(jsonData) {

try {

sendDataToBigQuery(jsonData);

} catch (err) {

console.error(err);

setTimeout(function() {

sendData(jsonData);

}, 5000);

}

}

This code retries sending the data to BigQuery if an error occurs.

Make sure to read the latest Google Cloud BigQuery SDK docs for more information.

Conclusion

By integrating Google BigQuery with your Next JS site, you can streamline data collection and enhance your data analysis capabilities. In this article, we walked through the process of sending data from Next JS to BigQuery. We covered the basics of Next JS and BigQuery, setting up your Next JS project, integrating BigQuery with your Next JS site, implementing data collection, and sending data to BigQuery. With this information, you should be well-equipped to start collecting and analyzing data in your Next JS site. Happy coding! Check out RudderStack's Next JS to Google BigQuery integration.