What Is BigQuery?

In the world of digital technology, the term big data describes the large quantity of data available for businesses to use. Its growing volume calls for tools to store, organize, and access information in that data. BigQuery is one of many data analysis tools used to analyze large data sets quickly. 

More than 8,000 companies worldwide use BigQuery, including those utilizing machine learning, big data, artificial intelligence, and more [1]. It offers integrated features, serverless architecture, and inherent flexibility to aid data scientists, analysts, engineers, and other professionals manage and use data more efficiently.

Read on to delve deeper into the nuances of BigQuery, including strategies to enhance your proficiency in using this tool.

What is BigQuery?

BigQuery is a cloud-based, fully managed data warehouse. Designed in 2011 to help process and analyze massive amounts of data in a fast and scalable manner, BigQuery allows users to run complex structured query language (SQL) queries on large data sets.

BigQuery eliminates the administrative needs of traditional data warehouses. Google lets you store and process these large data sets on its infrastructure. BigQuery can process data from multiple sources much faster than other systems.

What is BigQuery used for?

You can use BigQuery for a wide range of data analytics and processing tasks. Some common use cases for BigQuery include:

• Data warehousing: BigQuery can store and analyze structured and semi-structured data. It centralizes a large volume of data for efficient querying and reporting.

• Business intelligence (BI): BigQuery enables organizations to examine their data more comprehensively while facilitating advanced analytics and the generation of greater data-driven insights. 

• Ad hoc querying: BigQuery lets you quickly run ad hoc SQL queries on vast data sets. 

• Real-time analytics: BigQuery integrates with streaming data platforms like Apache Kafka, enabling real-time data ingestion and analysis. 

• Machine learning: BigQuery provides integration with BigQuery ML, allowing you to build and deploy machine learning models using your data in BigQuery. 

• Log analysis: BigQuery can take in and analyze logs generated by various systems, such as web servers and Internet of Things (IoT) devices. This can help identify patterns, troubleshoot issues, and gain insights into user behavior.

• Data exploration: Data analysts and data scientists can easily query and manipulate large data sets to understand patterns, relationships, and anomalies.

• Data backup and archiving: BigQuery can store historical or infrequently accessed data in BigQuery's long-term storage and query it when needed.

The tool may be useful to organizations dealing with large, complex data sets requiring fast and flexible analysis capabilities. It helps that BigQuery works on a serverless architecture, so you don’t need the infrastructure onsite to easily handle terabytes and even petabytes of data.

A real-world application of BigQuery

Purdue University’s Joint Transportation Research Program (JTRP) works with data from 11 states to help governments and public agencies make data-driven decisions on traffic signal timings, road and street systems, and infrastructure investments. Faced with billions of data records, the JTRP could no longer count on its on-premises servers for the scale and speed of analysis it needed.

Migrating to BigQuery gave the team the “ability to ingest large volumes of data and perform analytics quickly.” After migration, a query could take just seven minutes instead of the 90 it might have taken in the past [2].

Read more: What Is Data Analysis? (With Examples)

BigQuery features

BigQuery offers users with large data sets access to several key features:

• Scalability: BigQuery handles petabytes of data and can scale processing power to achieve your objectives.

• Speed: BigQuery can execute queries on large data sets with low latency. It stores data in columns as well, which helps compress and speed up data scanning.

• Advanced analytics: BigQuery supports a range of analytical functions, including approximate aggregation and machine learning capabilities.

• Security and governance: BigQuery lets you control access, audit, and encrypt your data both at rest and in transit.

• Cost-effective: BigQuery operates on a pay-as-you-go model, charging for the amount of data your queries process and storage usage.

Pros and cons of BigQuery

When selecting your data analysis tool, you’ll want to weigh both strengths and weaknesses. BigQuery comes with pros and cons, including the following. 

Pros

• Compatibility: BigQuery works with other data sets and visualization tools, including Google Analytics.

• Storage: You can store terabytes of data on BigQuery. 

• Speed: BigQuery can process large volumes of data in seconds.

• Out-of-box access: Users don’t need to install or configure anything or operate or maintain any infrastructure.

Cons

• Requires SQL: You must know SQL before you can use BigQuery, which requires additional training for anyone unacquainted with it. 

• Reliance on Google: You have to use the Google Cloud platform. Additionally, Google stores the data locally, which can cause latency issues if you’re querying US data from Asia, for example.

• Processing limitations: You can only make a certain number of table updates per day, and the data size per request can be limited. BigQuery may not be your best choice if your data sets frequently change. 

Next steps with BigQuery

Part of Google Cloud’s data analytics platform, BigQuery aids in the development of cloud-native data warehouses for enterprises. Should you wish to learn practices and processes used by data analysts just starting their careers at the junior or associate level in their day-to-day jobs, including BigQuery, consider pursuing the Google Data Analytics Professional Certificate on Coursera. You’ll have the opportunity to learn key analytical skills and tools, including best practices, using SQL, and methods for data cleaning, organizing, and visualization, as well as how to present your findings.

You may also enroll in the Exploring ​and ​Preparing ​your ​Data with BigQuery course offered by Google Cloud on Coursera. Intended for beginners, this seven-module course is designed to help you become familiar with key big data tools on Google Cloud.