Understanding Federated Query BigQuery Made Easy

You must have come across the term “data” one way or another. Better still, whichever industry you are affiliated with or whatever your interests are, you must have come across some news piece or something from a colleague regarding how data is changing the world. Well, this is true. Data is an invaluable tool to any company for several reasons. Let’s say you run a company. What if someone asked you some incredibly specific questions regarding your customers. With this in mind, this post dives into Big Data Analytics and Queries. Specifically, it is designed to make you understand Federated Query BigQuery.

Say, how many clients does your business have? How many of these customers are actively engaged with your business? How many clients are in specific regions? How many have you lost along the way? Now, these are not just questions you can answer from the top of your head, can you? This is where data, and more importantly, Data Analysis, comes in handy. With these tools, you can easily get access to such information. While these questions may seem easy, it is way more complicated than that, and with Big Data (data in the Exabytes scale) you can draw far more sophisticated results and even predict trends.

Table of Contents

• Introduction to Google BigQuery
  • Key Features of Google BigQuery
• What are BigQuery Federated Queries?
• Setting Up Federated Query BigQuery
  • Step 1: Adding External Data Source
  • Step 2: Input Source Details
  • Step 3: Connect to Instance
• Conclusion

Introduction to Google BigQuery

Since its inception way back in 2010, 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. So how exactly does BigQuery work? Perhaps 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 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.

The Google BigQuery platform is available in both on-demand and flat-rate subscription models. Although data storage and querying will be charged, exporting, loading, and copying data is free. It has separated computational resources from storage resources. You are only charged when you run queries. The quantity of data processed during searches is billed.

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 function, you can set up automatically updated reports that trigger mailings.   
• Aggregation Functions: With this feature, you can quickly get a summary of the data in a particular table.
• Security: When a third-party authorization exists, users can utilize OAuth as a standard approach to get the cluster. By default, all data is encrypted and in transit. Cloud Identity and Access Management (IAM) allows for fine-tuning administration.
• 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.
• Integrations: In addition to operational databases, the system supports integration with a wide range of data integration tools, business intelligence (BI), and artificial intelligence (AI) solutions. It also works with Google Workspace and Cloud Platform.

Now that you’re familiar with Google’s robust Data Warehouse, let’s dive straight into Federated Query BigQuery.

What are BigQuery Federated Queries?

When using data to make critical business decisions, you need information from different Data Marts, Warehouses, and Transactional Databases to draw the required statistics. Let’s take a situation where you work with BigQuery as your Data Warehouse and CloudSQL as your Relational Database. You need to find a bridge between these two systems, right? This is where Federated Query BigQuery comes into the picture.

Simply put, a Federated Query BigQuery is a way to send a query to an external Database and get the output in the form of a temporary table. These queries rely on the BigQuery Connection API to connect with an external Database. 

In our case, we would use the EXTRENAL_QUERY function to connect with CloudSQL. We would then query the data in this platform and get the results as temporary tables. It is worth noting that Federated Query BigQuery works with CloudSQL and Cloud Spanner. 

Below is a sample of a Federated Query BigQuery:

SELECT * FROM EXTERNAL_QUERY 
("test-fedquery-mysql", "SELECT customer_id, MIN(order date) AS first_order_date
 FROM orders
 GROUP BY customer_id;");

Setting Up Federated Query BigQuery

Below is how you would set up a Federated Query BigQuery.

Step 1: Adding External Data Source

Navigate to BigQuery, select “Add Data” and click “External Data Source“.

Step 2: Input Source Details

Key in the “External data source” credentials.

Step 3: Connect to Instance

Feel free to copy the Cloud SQL Instance ID from the SQL instance page under “Connection name“.

That’s it! By following the steps above. You have successfully set up a connection from Google BigQuery to an External Database using the Federated Query BigQuery.

Conclusion

In this post, you learnt what Google BigQuery is and some of the features it entails. More importantly, you learnt what Federated Query BigQuery is and how to implement them. Now, you stand a better chance to use Cloud SQL and Cloud Spanner together with Bug Query.

However, extracting data from a wide variety of sources and connecting to BigQuery is a tedious and time taking process but using a Data Integration tool like Hevo can perform this process with no effort and no time.

visit our website to explore hevo

Hevo Data with its strong integration with 100+ Sources & BI tools such as BigQuery, allows you to not only export data from sources & load data in the destinations, but also transform & enrich your data, & make it analysis-ready so that you can focus only on your key business needs and perform insightful analysis using BI tools.

Give Hevo Data a try and sign up for a 14-day free trial today. Hevo offers plans & pricing for different use cases and business needs, check them out!

Share your experience of working with Federated Query BigQuery in the comments section below.