What Is BigQuery? How the Google Cloud Tool Works and How to Store Data in It

Imagine Excel with millions of rows of data. It would drive you mad, but there is a tool that can find its way around it without any problem. BigQuery is a data warehouse from Google. It can work with huge amounts of data quickly and efficiently. You do not have to buy expensive servers or install complicated software. Everything runs online through a browser.

BigQuery is often the starting and key database for laying the foundations of sophisticated marketing reporting and, by extension, Business Intelligence. Our clients Ochutnej Ořech and Super Zoo have also reached for it, and part of their reporting runs on BigQuery too.

In this article we explain in simple terms what BigQuery can do, how it works, how to get data into it and why you should at least consider it.

Summary for Those Who Don't Have Time to Read the Whole Article

• BigQuery is an online data warehouse from Google. It works as a huge database in the cloud where you can store and analyse billions of rows of data.
• For basic work all you need is SQL (the language for working with databases) and the ability to find your way around the Google Cloud Console. You do not have to be an IT expert.
• BigQuery can work with different types of data, not only tables but also images, text or videos. For advanced analyses you can also use artificial intelligence. Google is investing massively in this area. First it came with BQML (BigQuery Machine Learning). Now there is an even simpler way to get your hands on fairly sophisticated predictive and machine learning models.
• You pay only for what you use. No flat fees for unused servers. The first 10 GB of storage and 1 TB of analysis per month are free.
• BigQuery works great with other Google tools, including Google Analytics, Google Sheets, Looker Studio and other services.

What Is BigQuery?

BigQuery is a cloud data warehouse that Google developed in 2010 as part of its Google Cloud platform. It grew out of Google's internal technology called Dremel, which they themselves used to analyse the huge amounts of data from their services. Today it is one of the most widely used tools in the world for working with big data.

The main magic of BigQuery is that it can process a really large amount of data within a few seconds. While an ordinary database would choke on millions of rows, BigQuery copes even with billions. With our clients we have not hit BigQuery's limits so far, which cannot be said of other tools. And the best part? You do not have to worry about technical matters – no installation, no server maintenance, no nightmares about outages. You simply log in through your browser and can start working.

BigQuery has developed significantly over the last few years. It is no longer just a data warehouse, but a comprehensive platform for data analysis. You can not only store and search data in it, but also create predictions using machine learning, analyse images and text, or monitor data in real time.

What Is BigQuery Good For?

BigQuery is not a universal tool for everything, but in some areas it has no competition.

Business Analysis and Reporting

BigQuery excels at analysing sales data, customer behaviour or the performance of marketing campaigns. It can process billions of transactions and show you trends and patterns within seconds.

Many e-shops and online businesses start their data journey with BigQuery. It is a solid foundation on which you can build all of your data analytics without having to spend hundreds of thousands. With BigQuery you have a professional tool from the start that will grow with you.

Artificial Intelligence and Prediction

Thanks to BigQuery ML you can predict the future of your business – from sales through churn rate (the percentage of users who have stopped being your customers) to fraud detection.

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Real-Time Analysis

You monitor the current performance of your e-shop, the response to campaigns or data from IoT sensors. BigQuery can analyse data as soon as it arrives.

Secure Data Sharing (Data Clean Rooms)

You work with partners on data without sharing sensitive information. Ideal for marketing analyses or joint projects.

Web Analytics and Google Analytics

Connecting to Google Analytics lets you combine data about website visitors with your internal data. You can, for example, find out how many offline sales your online campaigns generate, or how different customer segments behave across all channels.

How Does BigQuery Work?

If you are wondering how BigQuery can process such a huge amount of data so quickly, the answer lies in its unique "architecture".

Separate Storage and Compute

BigQuery works differently from traditional databases. Data is stored separately from the computing power that needs to process it. In practice this means that you can have petabytes of data stored (that is millions of gigabytes) and still run fast analyses.

Thanks to this approach:

• You can optimise the data storage costs separately (for example by switching from logical to physical storage billing) or optimise the size of queries that, for example, run regularly. By orchestrating these queries suitably you can significantly reduce the regular costs of daily calculations.
• You can store a practically unlimited amount of data.
• Your analyses are fast even as more data is added.
• Several people can work at the same time without slowing each other down.

Underlying Technology

BigQuery uses several advanced technologies that ensure its speed:

• Dremel – The brain of the whole system, which splits your query into thousands of small tasks that then run simultaneously on different computers.
• Colossus – A huge storage system where all your data is safely stored.
• Jupiter – A super-fast network that connects all parts of the system.
• Borg – The conductor that manages the whole operation and allocates power where it is needed.

Do not worry, you do not have to work with these technologies at all. BigQuery uses them automatically in the background.

Columnar Data Format

BigQuery stores data in a smarter way than ordinary databases. Instead of storing it by rows (as in Excel), it stores data by columns. When you ask, for example, what the average age of your customers is, BigQuery does not have to go through the whole table but reads only the age column. This speeds up analysis dramatically, especially for large tables.

How to Get Data into BigQuery

There are several ways to load data into BigQuery. The choice depends on how much data you have and how often you need to update it.

Uploading Data Files

The simplest way to start. BigQuery can read common file formats:

• CSV files (tables similar to Excel),
• JSON files (a format used on the web),
• Avro, Parquet, ORC (special formats for big data).

The procedure is simple:

1. Open BigQuery in the Google Cloud Console.
2. Create a dataset (a folder for your tables).
3. Click "Create table".
4. Upload a file from your computer or Google Cloud Storage.
5. Set the column names.
6. Click "Create".

Streaming Data in Real Time

What if you need data that is generated continuously? For example, to track what visitors do on a website, or to monitor IoT sensors? BigQuery can receive data continuously, practically in real time.

Streaming options:

• Storage Write API – the most modern way for a continuous flow of data,
• Google Pub/Sub – for message-based systems,
• Dataflow – when you need to transform or filter the data first.

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Working with Data Without Moving It

Sometimes you do not want to or cannot move data. BigQuery can also work with data that stays in its original place:

• Google Cloud Storage (large files in the cloud),
• Google Drive (including Google Sheets tables),
• Bigtable, Spanner and other Google databases.

You do not have to copy the data, you can work with it right away.

Data Transfer Service

For regular automatic data transfers BigQuery offers the Data Transfer Service. You set it up once and then it runs on its own. It can regularly copy data from:

• Google services (Google Ads, YouTube, Google Analytics),
• Competing platforms (Amazon S3, Redshift),
• Enterprise systems (Teradata, Oracle).

Especially for Google Analytics: BigQuery runs on the same technology as the data storage in Google Analytics. You can therefore connect web analytics directly to the data warehouse and combine data about visitors with your internal data on sales, customers or stock levels.

Working with Data in BigQuery

Once you have the data in BigQuery, the more interesting part begins, working with it. BigQuery offers several ways to analyse data, visualise it and even create predictions from it.

SQL Queries

To work with data you use the SQL language (the standard way of communicating with databases). You do not have to be an expert, basic queries are intuitive:

SELECT product, SUM(sales) AS total_sales
FROM `my-project.dataset.sales`
WHERE date BETWEEN '2025-01-01' AND '2025-06-30'
GROUP BY product
ORDER BY total_sales DESC
LIMIT 10

This query shows you the 10 best-selling products for the first half of the year. You can see that SQL is almost like ordinary English.

Connecting to Analytics Tools

BigQuery works great with visualisation and analytics tools:

• Looker and Looker Studio – for creating interactive dashboards.
• Google Sheets – you can display data from BigQuery directly in spreadsheets.
• Tableau, Power BI – professional business intelligence tools.
• Python, R – for advanced statistical analyses.
• Google Analytics – direct integration for web analytics.

Machine Learning Directly in BigQuery

BigQuery machine learning is a feature that lets you create predictive models directly with SQL. You do not have to be a data scientist or a programmer. You can:

• Forecast future sales based on historical data.
• Segment customers into groups by behaviour.
• Detect suspicious transactions automatically.
• Analyse sentiment in reviews or comments.
• Predict which customers will leave for a competitor.

All with SQL statements that differ only a little from ordinary queries.

Data Management and Security

Data in BigQuery is not just freely accessible to anyone. The system offers tools for organising, securing and sharing data.

Organising Data

In BigQuery data is organised hierarchically:

• Project – the highest level, tied to your account.
• Dataset – a folder for related tables.
• Table – the data itself.

Access Control

You have full control over who can work with your data. You can set who may:

• Only read data,
• edit data,
• run analyses,
• manage the whole system.

Sharing Data

Do you need to share data with colleagues or partners? BigQuery allows you to:

• Share whole datasets,
• share specific tables,
• create "views" that show only selected data.

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How Much Does BigQuery Cost?

BigQuery offers two ways to pay.

Pay as You Go (On-demand)

• You pay $6.25 for every TB of data processed.
• The first TB per month is free!
• Suitable for irregular analyses.
• You pay only when you actually use BigQuery.

Payment for Reserved Capacity (Capacity-based)

• From $0.04 per slot per hour.
• More advantageous with regular use.
• Better cost predictability.
• Suitable for large companies with a constant load.

For data storage you pay:

• Active data: from $0.02 per GB per month.
• Archive data (not used for more than 90 days): from $0.01 per GB per month.

The free tier includes:

• 10 GB of storage for free.
• 1 TB of queries per month for free.

That is enough for testing and smaller projects.

Why Do We Recommend BigQuery?

BigQuery has several major advantages over the competition. Here are the main reasons to choose BigQuery for working with data:

• Speed and scalability without worries – BigQuery can handle petabytes of data and still stays fast.
• Ease of use – Unlike traditional data warehouses you do not need a team of IT specialists. Basic knowledge of SQL and the Google Cloud Console is enough. BigQuery takes care of the rest.
• Great integration with the Google ecosystem – BigQuery works perfectly with all Google tools, from Google Sheets through Google Analytics to Looker Studio.
• Reasonable price – You pay only for what you actually use. No unnecessary fees for unused capacity. You can start for free and grow as needed.
• Large community and support – There is a huge community of users around BigQuery. You will find plenty of tutorials, examples and experts who will help you.
• Constant innovation – Google regularly adds new features (support for new formats, better AI models, geographic analyses).

Do You Need Help with BigQuery?

If the article caught your interest but you do not know where to start, or you already use BigQuery and want to get the most out of it, we are here for you. As Archetix we specialise in data analysis and help companies get control over their data.

What we can do for you:

• Set up BigQuery for your needs.
• Connect your web analytics to BigQuery.
• Create dashboards and reports so you have an overview of how your business is developing in one place.
• Uncover hidden relationships in your data.
• Teach you to work with BigQuery effectively so that you are self-sufficient.

Book a no-obligation consultation with us. We will be happy to help you.

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