BigQuery Client API Libraries Simplified: A Comprehensive Guide 101

on bigquery datasets, BigQuery Functions, Data Warehouse, Google BigQuery, Tutorials • October 7th, 2021 • Write for Hevo

Big Data is an essential component of any modern corporation. Using this tool, organizations can generate valuable insights that can be used for Marketing and Campaigns. However, not long ago, getting access to Big Data was an expensive affair. Everything seemed reserved for the super business moguls, from the equipment to the space to the expertise suitable for handling such data. However, with the advent of the Cloud, Big Data access is no longer a troublesome affair for companies. Today, all you need is access to a reputable Cloud service provider, and everything falls in line from there. 

Google is a household name when it comes to Cloud service, and one of the most successful tools created by the company is Bigquery. First introduced in 2010, Google Bigquery is an enterprise-level Cloud-native data warehouse. This post will dive into Bigquery Client Libraries. By the end, you should have a basic idea of what these tools are and how they can benefit your business. Read along to learn more about BigQuery Client Libraries!

Table of Contents

Introduction to Google BigQuery

Since its inception way back in 2010, Google BigQuery has grown to become a full-scale Data Warehouse popular with some of the biggest companies in the world. The tool offers seamless data querying capabilities on the petabyte scale, a feature that makes it indispensable for businesses. The most notable feature is that it is entirely serverless, meaning you do not have to install any software or database. Everything is done over the Cloud. The service handles all the software required for data processing. Furthermore, it has a relatively simple pricing policy where for every 1 TB of data processed, you pay $5. 

One of the most significant advantages of using Google BigQuery is that you do not need to understand how the architecture works. In fact, one might even argue that this is the entire premise of the tool. However, it is worth noting that you need to comprehend several processes such as Authentication and Loading the data. Nevertheless, these are relatively simple procedures that do not demand any form of technical expertise. 

Key Features of Google BigQuery

Below are some of the top Google BigQuery features that have made it the ideal cloud-native tool for companies all over the world: 

  • Date functions: It may sound a bit too standard. However, it’s a handy feature when converting dates from multiple sources to a single format for advanced analytics. Moreover, with the date functions, you can set up automatically updated reports that trigger mailings.   
  • Aggregate Functions: With this feature, you can quickly get a summary of the data in a particular table. You can learn more about Google BigQuery Aggregate Functions by clicking here.
  • Window Functions: Similar to Aggregate Functions, these carry out data summary calculations. The only difference is that they do not deal with the entire set but rather a specified one.  

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Working with Google BigQuery APIs

By now, you should have a basic idea of what Google BigQuery is and the purpose it serves. Now that you have this idea, you are ready to learn about how to interact with Google BigQuery APIs.

You can use a standard dataset to learn how BigQuery API works. Assuming you have the software ready, select a dataset and play around with it for educational purposes. You can click on the Add Data button to view the list of available datasets. For purposes of this tutorial, you will be using the ‘bigquery-public-data: stackoverflow dataset’. 

Available Datasets
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Now, you will be required to write a query that will find out the most popular programming language as shown below: 

Query to find out the most popular programming language
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Once the query is executed, the results will be as follows: 

Query Results
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Here is a close-up view of the results:

Results (BigQuery Client Library)
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In the next section of this article, you will learn about the most popular BigQuery Client API Libraries.

BigQuery Client API Libraries

Some of the most popular BigQuery Client API Libraries are listed below:

1) Node.js Client Library

This is one of the most popular and versatile BigQuery Client API Library that you can utilize for Cloud functions. Given below are the steps required to install and implement the Node.js BigQuery Client library.

Prerequisites

  • Create a Cloud Platform project.
  • Ensure you have enabled the Google BigQuery API to install the BigQuery Client Library .
  • Follow the steps above to enable authentication.

Installing the Node.js Client Library

Run the following command in your Cloud platform to install the Node.js BigQuery Client Library:

npm install@google-cloud/bigquery

That’s it. Once you follow the steps listed above, you will have successfully installed the Node.js BigQuery Client library.

Using the Node.js Client Library

Now that you have successfully installed the Node.js BigQuery Client library, its time to learn how to leverage it. The following code will create a dataset inside the Node.js BigQuery Client library.

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

async function createDataset() {
  // Creates a client
  const bigqueryClient = new BigQuery();

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

For more information on samples of the Node.js BigQuery Client library, you can follow this link.

2) Java Client Library

This is a bit more complicated when compared to the Node.js BigQuery Client library. First, if you don’t have a Java environment installed in your system, you can follow the prompts outlined here to install the tool. The installation process for Java BigQuery Client library is different depending on the software in use. Installation steps for some of the tools are listed below:

Maven Users

For Maven users, you need to navigate to your pom.xml file and add the code laid out below:

<!--  Using libraries-bom to manage versions.
See https://github.com/GoogleCloudPlatform/cloud-opensource-java/wiki/The-Google-Cloud-Platform-Libraries-BOM -->
<dependencyManagement>
  <dependencies>
    <dependency>
      <groupId>com.google.cloud</groupId>
      <artifactId>libraries-bom</artifactId>
      <version>23.0.0</version>
      <type>pom</type>
      <scope>import</scope>
    </dependency>
  </dependencies>
</dependencyManagement>

<dependencies>
  <dependency>
    <groupId>com.google.cloud</groupId>
    <artifactId>google-cloud-bigquery</artifactId>
  </dependency>

Gradle Users

For Gradle users, you need to add the following code to your dependencies: 

implementation platform('com.google.cloud:libraries-bom:23.0.0')

implementation 'com.google.cloud:google-cloud-bigquery'

SBT Users

Similar to Gradle, you need to add the following code to your dependencies:

libraryDependencies += "com.google.cloud" % "google-cloud-bigquery" % "2.1.7"

You can follow this link for more information on the installation process, especially for users with IDEs.

Conclusion

As can be inferred from the examples and steps above, BigQuery utilizes SQL-like queries to analyze data several terabytes in size in several seconds. With BigQuery, data size is no longer an issue of concern, as you can query data in several seconds. In this article, you learned about Google BigQuery and the salient features that it offers. You also learned about various types of BigQuery Client API libraries and how to work with APIs in Google BigQuery.

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Share your experience of learning about the Google BigQuery Client API Libraries. Let us know in the comments section below!

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