What is BigQuery?

BigQuery is Google’s fully-managed and serverless data warehouse that was designed to handle massive amounts of data. But what is BigQuery and how can it help companies work with large data sets and big data?

In this article, we’ll provide a comprehensive and easy-to-understand overview of what BigQuery is, its main features, and common use cases.

What is BigQuery?

BigQuery is Google Cloud enterprise data warehouse that enables you to store, visualize and analyze data. As a fully-managed data warehouse, it allows you to focus on data visualization and analysis instead of data storage. Therefore, it helps you get insights from your data easily and quickly without having to worry about storing your data.

With BigQuery, you can also collaborate with your team by assigning read and write permissions to specific users. In addition, you can rest assured that you will be able to keep your sensitive data safe and secure. BigQuery prioritizes data security and governance to ensure data privacy and integrity.

Since it supports the standard SQL dialect, BigQuery allows you to write queries using SQL syntax. By using this powerful programming language, you can organize, manage and manipulate data in BigQuery.

How does BigQuery work?

BigQuery stores data in columns and presents it in tables, rows, and columns. We can break down how BigQuery works into different parts.

Ingestion: you can ingest data into BigQuery in different ways. For instance, you can upload files in different formats, such as JSON and CSV. You can also stream data from a tool like Dataflow into BigQuery.

Storage: once data is ingested, it is stored in columns. Each column in the table is stored separately across multiple nodes.

Querying: users can write SQL queries to retrieve and organize data from the tables.

Advantages of using BigQuery

There are many benefits of using BigQuery. Some of these advantages include:

Fast queries even for extremely large datasets: BigQuery allows you to analyze large and complex datasets fast and with ease, which makes it a very effective and scalable solution.

Scalability: it’s a very scalable tool that can process up to hundreds of petabytes, which consists of 1024 terabytes.

No infrastructure to manage: since it’s a cloud-based fully-managed service, businesses don’t need to worry about investing in infrastructure.

Ease of use: BigQuery is relatively easy to use, especially for users who can use SQL. The interface is simple and user friendly.

Security: BigQuery encrypts all data before it is written to disk and automatically decrypts it when it’s read by authorized users.  

Use cases 

Here are some common use cases and applications where BigQuery can be used:

Real-time big data analytics: it allows data analysts to organize and analyze large datasets in real time using SQL.

Reporting: it enables users to create real-time dashboards and business intelligence reports on the fly using data visualization tools.

Machine Learning: BigQuery enables users to develop machine learning models in by using SQL queries.

Geospatial analytics:  it allows users to process, visualize and analyze geographic data in BigQuery. 

Data warehousing: it serves as a data warehouse to store, consolidate and centralize data from all your sources.

Businesses can also utilize BigQuery across different departments:


  • Analyze marketing campaigns
  • Conduct customer segmentation using K-means clustering
  • Optimize ad targeting using audience insights
  • Predict customer lifetime value
  • Generate marketing reports


  • Identify high value customers based on purchase history
  • Analyze sales patterns and seasonality
  • Optimize pricing and product packages
  • Track sales conversion across multiple channels


  • Financial reporting with real-time analytics
  • Analyze budgets, forecasts, and expenses
  • Improve fraud detection 
  • Accurate risk analysis


  • Optimize manufacturing quality control
  • Identify supply chain bottlenecks
  • Optimize operations to reduce costs
  • Identify trends and patterns throughout the supply chain


  • Analyze employee retention and attrition
  • Derive insights from recruitment data
  • Correlate compensation with performance
  • Improve talent acquisition

Research and development:

  • Extract insights from unstructured data
  • Use predictive analytics to forecast future product improvements
  • Understand customer behavior to identity trends and patterns
  • Apply machine learning techniques to scientific data

Key components of BigQuery

Here are some of the main components that are part of BigQuery.

Tables: these are the basic containers for storing and managing data in BigQuery. Tables can be partitioned and clustered.

Datasets: a container for grouping related tables and applying access control settings at the collection level.

Storage: this is where data is stored. BigQuery uses a scalable distributed storage system where data is stored in a columnar format.

Jobs: when you use BigQuery, it performs a range of tasks to load data, export data, query data, or copy data. These jobs can be scheduled or run on-demand.

BigQuery ML: with BigQuery ML, you can create and run machine learning (ML) models by using GoogleSQL queries. 

BigQuery API: it’s a platform where customers can create, manage, and query data. It provides a REST-based API that can be accessed using Java or Python.


Now that you know what BigQuery is, how it works, some of its main benefits, and some use cases, it’s time to start using this powerful tool to make the most of your data.

If you’re a spreadsheet user, check out this article on how to connect BigQuery to Google Sheets and transfer data between these two powerful tools.

Manage large datasets with ease

Use Sheetgo to connect BigQuery with Google Sheets and handle extensive data efficiently

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