Working with BigQuery Java API Client: Made Easy

on API, Data Warehouse, Google BigQuery, Java • February 10th, 2022 • Write for Hevo

BigQuery Java API | Hevo Data

Today, data has become a core component of every business and how it operates. Companies utilize this information to understand their customers better and to boost their marketing campaigns. Truth be told, data holds many advantages for businesses, both big and small. However, you need appropriate tools to harness these merits and customize them to benefit your business. This is where data analytics comes into the picture. It lets you unlock the advantages of raw data and use it to grow your business. 

With this information in mind, this post will dive into the nuts and bolts of a section of data analytics- Queries. Better still, you will get acquainted with one of the most efficient data querying tools on the market today- Google BigQuery. More specifically, you will understand how to use one of the BigQuery client libraries: the BigQuery Java API. Have a read below. 

Table of Contents

What is Google BigQuery?

Unless you are new to the data world, you must have heard of Google BigQuery, given its high functionality and implementation by users worldwide. So what exactly is Google BigQuery? In simple terms, this is a Cloud-based Data Warehouse that provides users with SQL query functionality and large databases interactive analysis capability. It is worth noting that Google BigQuery was created based on Google’s Dremel technology and works on read-only data.

While working with Google BigQuery, you must have come across the term “Serverless Design”. What does this mean?
It implies that you don’t need a physical server to host and query your data. Instead, you harness Google’s Cloud Storage Architecture, which offers many benefits, including batch ingesting, where you load vast data amounts without overwhelming your physical resources. 

Know about the best Data Visualization Tools on Google BigQuery by following this guide- Best Google BigQuery Data Visualization Tools for 2022.

By now, you should have a rough idea of what Google BigQuery is. Now, what are the features that make it synonymous with successful companies all around the globe? Read on below. 

Key Features of Google BigQuery

Below are some of the top Google BigQuery features:

  • MultiCloud Functionality: This is one of the best features that separates Google BigQuery from other similar solutions in the market. It allows users to analyze data in more than one cloud as opposed to other solutions that make it hard to migrate from one cloud to the next. 
  • Built-In Machine Integration: This is used to design and execute Machine Learning Models using relatively straightforward SQL queries. 
  • Aggregate Functions: These summarize the rows of a group into a single value. for BIgQuery Tables.
  • Real-Time Analytics: You can input your latest data and analyze it immediately with real-time analytics. It offers high functionality, especially in cases where data analytics is high on demand. 
  • Highly Efficient Data Transfer Services: With this feature, you can seamlessly transfer data from external sources such as Youtube. 

Now that you have a rough idea of some of the top BigQuery features, we get to the core purpose of this blog, the BigQuery Java Client API. 

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What is Java?

Java is an object-oriented programming language that can be used to program almost any electronic device. Because of Java virtual machines, Java is platform-agnostic (JVMs). It is based on the “write once, run everywhere” principle. When a JVM is installed on the host operating system, it adapts to the environment and runs the program’s functions.

The developer must first download the JDK and set up the Java Runtime Environment before installing Java on a computer (JRE).

What are Java APIs?

APIs are critical software components that come packaged with the JDK. Classes, interfaces, and user interfaces are all examples of Java APIs. They allow developers to integrate multiple applications and websites while also providing real-time data.

The fundamental components of the Java API are depicted in the diagram below.

APIs-in-Java-API-Architecture.
Image Source

What is the need of Java APIs?

APIs are used by Java programmers to:

  • Streamline Operating Procedures: Twitter, Facebook, LinkedIn, and Instagram are examples of social media apps that offer users a variety of options on a single screen. This functionality is enabled by Java APIs.
  • Improve Business Techniques: Many companies release private data in order to generate new ideas, fix existing bugs, and find new ways to improve operations after releasing APIs to the public. The Twitter developer account is an example of an API that provides private API keys to programmers so they can access Twitter data and build applications.
  • Create Powerful Applications: APIs allow customers to manage their finances digitally with complete ease, and online banking has changed the industry forever.

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  • Faster Insight Generation: Hevo offers near real-time data replication so you have access to real-time insight generation and faster decision making. 
  • Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
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What is BigQuery Java API Client? 

Up to this point, it should be clear that Google BigQuery allows you to input data for querying and analysis. 

What about the tool that facilitates this process? 

This is where BigQuery Client APIs come into the picture. Simply put, BigQuery APIs allow users to work with complex datasets. Accordingly, you can create, analyze, and share complex datasets using these tools. To put it in another way, APIs allow collaboration between BigQuery users. Look at it this way, with a BigQuery API, you can grant other users access to your data. 

For more information on BigQuery APIs, do check our other blog on Understanding Google BigQuery APIs: 6 Critical Aspects. 

Today, there are seven known Google BigQuery API libraries available to users: 

  • C #
  • Go
  • Java
  • Node.js
  • PHP
  • Python 
  • Ruby

For the purpose of this blog, we will be using the BigQuery Java Client API Library. 

What exactly is BigQuery Java API Client Libary? This is a flexible and useful Java Client Library you can use to access any HTTP-based API on the web. 

Below are the top features of the BigQuery Java API Client Library:

  • Comprises a powerful OAuth library with a friendly and easy-to-use interface. 
  • A series of pre-designed libraries for Google APIs.
  • Supports protocol buffers. 
  • Easy to use Data Schemas that support XML and JSON models. 

In addition to the features outlined above, the BigQuery Java API client library supports the following Java environments.  

  • Java 7 and higher.
  • Google App Engine.
  • Android 1.6 and above. 

Now that you have an idea of what the BigQuery Java API client library is, let us get to the practical aspect of the tool. Specifically, we are going to illustrate how to create a BigQuery table using this API. Take a quick read below: 

Prerequisites for BigQuery Java API

Before you implement the strategy that we will outline in this post, you need to check whether the following factors are in check: 

  • You have an account on Google BigQuery.
  • The Java API Client Library is installed and configured properly.

How to Create a BigQuery Java API Client?

When you create a BigQuery Table using the BigQuery Java API Client, the first step is to create the Table Schema. In simple terms, a Table Schema provides a basis for creating the fields that will comprise your BigQuery Table Columns. Accordingly, they are defined as an array of TableFieldSchema objects as shown below:

ArrayList<TableFieldschema> fieldschema = new ArrayList<TableFieldschema>();

You need to be careful with the value types you use to populate your columns. Specifically, you should stick to the sentiments outlined in the BigQuery API documentation to input the correct type and corresponding mode. For instance, if you want to populate your table with repeated fields, you can use the REPEATED mode as outlined below: 

fieldschema add(new TableFieldschema().setName ("email").setType("STRING").setMode("REPEATED"));

Another example is when you want to create nested records. Here, you must specify the parents in a RECORD mode, then call “setFields” for each column. Below is what it should look like: 

fieldschema.add( new TableFieldschema().setName("location").setType("RECORD").setFields
new ArrayList<TableFieldSchema> 
{
add(new TableFieldschema().setName("city").setType ("STRING")); add(new TableFieldschema).setName("address").setType("STRING")); add(new TableFieldschema().setName("zipcode").setType "STRING"));
}
}
)
);

Once this is done, you need to set the entire schema as fields (fieldSchema) in the table as you will see down below: 

TableSchema schema = new TableSchema); 
schema. setFields (fieldschema);

Next, you need to set a table reference for our table as shown below: 

TableReference ref = new TableReference(); ref.setProjectId(PROJECT_ID); ref.setDatasetId("pubsub"); ref.setTableId("review_test");

Table content = new Table(); 
content.setTableReference (ref); 
content.setSchema schema);
client. tables().insert(ref.getProjectId(), ref.getDatasetId(), content).execute();

Putting all this together yields the following table. 

public static void main(String[] args) throws IOException, InterruptedException {
Bigquery client = createAuthorizedClient(); // As per the BQ sample code
ArrayList<TableFieldschema> fieldschema = new ArrayList<TableFieldschema>();
fieldschema .add(new TableFieldSchema().setName "username").setType "STRING").setMode "NULLABLE" fieldschema .add(new TableFieldSchema().setName "email").setType("STRING").setMode "REPEATED"); fieldschema.add( new TableFieldschema ().setName("location").setType ("RECORD").setFields
new ArrayList<TableFieldschema i
add(new TableFieldschema().setName "city").setType "STRING")); add(new TableFieldschema().setName("address").setType("STRING")); add(new TableFieldschema().setName("zipcode").setType ("STRING"));
}));
TableSchema schema = new TableSchema); schema. setFields (fieldschema);
TableReference ref = new TableReference ref.setProjectId("<YOUR_PROJECT_ID>"); ref.setDatasetId("<YOUR_DATASET_ID>"); ref.setTableId("<YOUR_TABLE_ID>");
Table content = new Table(); content.setTableReference(ref); content.setSchema (schema);
client. tables().insert(ref.getProjectId(), ref.getDatasetId(), content).execute();
}

That’s it! By following the process above, you will have created a BigQuery table using the BigQuery Java API Client.

Conclusion 

In this post, we showed you how to work with BigQuery Java API Library. The information was outlined in a stepwise format where you were first introduced to Google BigQuery and its features. Next, you got acquainted with a concise but comprehensive introduction to the BigQuery Java API Client and its features. Lastly, you learned how to use the BigQuery Java API Client tool to create a BigQuery table. With all this information, you stand at a better chance of utilizing the Google Bigquery Java API Client Library. 

With your Data Warehouse, Google BigQuery live and running, you’ll need to extract data from multiple platforms to carry out your analysis. However, integrating and analyzing your data from a diverse set of data sources can be challenging and this is where Hevo Data comes into the picture.

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Share your experience of learning about the BigQuery Java API Client in the comments below! We’d love to hear from you.

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