How to use BigQuery?

Firms in areas such as marketing, e-commerce and SaaS work daily with millions to billions of records. Every click, purchase and user interaction generates new data of enormous value. The problem arises, though, the moment this data has to be processed. Traditional databases often cannot cope. It is exactly here that BigQuery enters the process.

What is BigQuery?

BigQuery is a modern solution for analysing data in the cloud. It is a tool from Google that lets you work with large volumes of data without needing to manage servers or infrastructure.  BigQuery belongs to the category of so-called cloud data warehouse solutions. Put simply, it is a database system meant for processing and analysing large volumes of data, and it works entirely in the cloud. That means you do not have to worry about servers, infrastructure or their maintenance.

A simple definition of BigQuery

From a practical point of view, BigQuery is designed above all for so-called big data analysis. It lets firms work with enormous datasets that traditional databases could not process effectively. Thanks to that it has become a popular solution for marketing teams, analysts and technology firms needing to gain insights from data quickly. 

One of BigQuery's key qualities is its "serverless" character. The system automatically adapts to the volume of data and the queries' difficulty. This approach markedly simplifies work and lets you focus on the analysis itself rather than on technical infrastructure.

BigQuery's key qualities

A great advantage is also the speed of processing data. In ideal conditions BigQuery can process billions of rows within seconds. It matters to emphasise, though, that this performance depends on a properly designed data structure and optimised queries. If tables or queries are designed unsuitably, processing can take longer and at the same time needlessly raise costs. 

BigQuery uses SQL as the main language for working with data. That means if you already have experience of databases you can find your way around it quickly and start working without needing to learn a new programming language. The SQL approach makes BigQuery available not only to developers but to analysts and marketers who need to work with data daily.

How does BigQuery work?

BigQuery is designed to handle processing enormous volumes of data quickly and effectively. How it works rests on a modern cloud architecture that differs markedly from traditional database systems. The key to its performance is the combination of separating storage and computing power, distributed data processing and an intelligent pricing model.

BigQuery's architecture

The basis of how BigQuery works is its architecture, which separates storage (storing data) and compute (calculations). In practice that means your data is stored independently of the power used to process it. This approach allows flexible scaling. That means that if you need to process a larger amount of data or a more complex query, BigQuery automatically uses more computing resources without your having to set anything. At the same time it eliminates the problem we know from traditional databases, where power and storage often grow together, which leads to higher costs and lower efficiency. 

An important part is also distributed data processing. BigQuery divides the data and the queries themselves into smaller parts and processes them in parallel on several servers. Thanks to that it can markedly speed analysis up even with very large datasets. This principle is one of the reasons BigQuery suits big data analysis and complex analytical tasks.

Dremel

Behind this performance stands so-called Dremel, a technology developed by Google specially for analytical queries. Dremel represents the technological core BigQuery is built on, and it is exactly thanks to it that this tool can process enormous volumes of data so fast. How it works is based on a specific tree architecture, where a query is not evaluated on one server but broken down across several levels. 

At the top is a control node that divides the query into smaller parts, which are then processed in parallel on thousands of worker nodes. The results are then gradually aggregated back, which allows extremely fast evaluation even of very complex queries. Dremel at the same time uses so-called columnar storage. That means that with a query only the columns that are genuinely needed are loaded rather than the whole table. The result is not only faster processing but lower costs too. 

Another important quality is the ability to work with nested and repeated data structures similar to the JSON format. Thanks to that, complex JOIN operations need not be used so often, which simplifies data modelling and at the same time raises queries' performance. It is exactly thanks to this technology that BigQuery works not merely as an ordinary database but as an advanced system for analysing data, able to process even the largest datasets without needing to manage infrastructure.

The pricing model

When working with BigQuery, though, it matters greatly to grasp its pricing model too. Unlike a classic "pay-per-query" approach, BigQuery works in on-demand mode on a pay-per-bytes-scanned principle. That means you do not pay for the number of queries but for the amount of data a query goes through. One query processing 10 TB of data, for instance, can be markedly more expensive than thousands of small queries working with only a minimal volume of data. So optimising queries, a properly designed table structure (e.g. partitioning or clustering) and working with selecting particular columns are key to managing costs effectively. 

Besides that, BigQuery charges for storing the data itself too, that is, so-called storage pricing. The price for storage is, though, relatively low compared with computing operations. In practice that means keeping a large amount of data is affordable, but costs arise above all in actively processing it. Since 2023 there has been a new BigQuery Editions model (Standard, Enterprise, Enterprise+) built on capacity slots with flat-rate pricing. For firms with a predictable, recurring workload it can be more advantageous than on-demand.

What is BigQuery used for (use cases)?

BigQuery finds wide use across various areas of business, above all where it is necessary to work with a large amount of data and gain relevant information from it quickly. Thanks to its scalability and performance it has become the standard for data analytics in modern firms.

Marketing and digital analytics

One of BigQuery's most frequent uses is marketing. Firms use it, for instance, to process data from Google Analytics 4, which can be exported directly into BigQuery. That lets you work with detailed data about users' behaviour without the restrictions ordinary in standard analytics tools. In practice that means you can analyse campaigns' performance, track customers' conversion paths or identify the most effective marketing channels. BigQuery thus gives a deeper view of data that helps optimise marketing strategies and raise the return on investment.

Business intelligence and reporting

BigQuery is a powerful tool for business intelligence and reporting. Firms use it to create clear dashboards and reports showing key metrics in real time. Combined with tools such as Looker Studio or Power BI you can create interactive visualisations that help management make informed decisions. The advantage is that BigQuery can process data practically immediately, so reports can be current and accurate without marked delay.

Product analytics

Another important area is product analytics. BigQuery lets you analyse users' behaviour on a site or in apps, which is key for improving products and services. Using the data you can track how users move around an app, where problems arise or at which steps they leave. So-called funnel analyses will help you identify weak points in the customer journey and optimise them. The result is a better user experience and higher conversion.

Machine learning and predictions

BigQuery is used in advanced analytics and machine learning too. Using the BigQuery ML tool you can create and train models directly on the data without needing to export it into other systems. That opens possibilities for predicting customers' behaviour, such as the likelihood of churn, estimating future sales or segmenting customers. Thanks to that you can make proactive decisions and react better to how the market develops.

How to use BigQuery step by step?

If you want to start working with BigQuery you need not build your own infrastructure or install complicated solutions. The whole process is designed so that you can quickly start storing, processing and analysing data in the cloud. All that matters is proceeding systematically.

1. Creating a Google Cloud account

The first step is creating an account in Google Cloud. If you already use some of Google's services, for instance Google Analytics or Google Ads, working in this environment may be simpler still for you. Once registered you gain access to the Google Cloud Console, where all cloud services including BigQuery are managed. At this stage you also need to set up billing. Although BigQuery offers a free limit for storing and processing data, activating a billing account is a standard part of the setup for full use. The good news is that to start with you can test the system at relatively low cost.

2. Setting up a project and a dataset

Once the account is created, setting up a project follows. A project in Google Cloud works as the main container under which all services, settings and data are grouped. Within the project you then create a dataset, which in BigQuery is a logical structure for organising tables and data. You can picture a dataset as a folder in which individual tables will be stored. Already when creating it, it is suitable to think about the data structure, the storage region and any access rights for the team. Setting the project and dataset up properly matters not only for order in the data but for security and effective cost management too.

3. Importing data (CSV, API, streaming)

Once you have the project and dataset ready you can start uploading data. BigQuery supports several ways of importing. The simplest is uploading data from files, for instance in CSV or JSON format. This way suits above all testing or working with smaller datasets. With more advanced solutions, data is often imported automatically through an API or written into BigQuery continuously in the form of streaming. That is ideal, for instance, when processing data from web applications, marketing platforms or internal systems. The choice of import method depends on how often you need to update the data and what volume you work with.

4. Running your first SQL query

Once the data is uploaded you can start analysing it. BigQuery uses SQL, so if you have at least basic experience of databases you will find your way around very quickly. The first query tends to be simple. You can gradually move to more complex queries covering aggregations, joining tables or time analyses. At this stage it matters to think about optimisation too. Since BigQuery charges by the volume of data processed, a well-written SQL query will help you keep costs under control and at the same time speed the results up.

5. Connecting with BI tools (Looker Studio, Power BI)

Analysing data itself is only one part of the work. Great value comes only at the moment when you can visualise the results comprehensibly and share them with the team or the firm's management. That is why BigQuery is often connected with BI tools such as Looker Studio or Power BI. These tools let you create dashboards, charts and reports drawing data directly from BigQuery. Thanks to that you always have current information available without needing to export data manually. Connecting with BI tools is an important step if you want to turn data into a practical tool for decision-making rather than merely a technical source of information.

BigQuery's advantages for firms

BigQuery brings firms several fundamental advantages that make it one of the most attractive solutions for analysing data in the cloud. It is designed to handle growing volumes of data, give high performance and at the same time be available to various types of organisation.

Scalability without limits

One of BigQuery's greatest advantages is its ability to scale without needing manual intervention. You do not have to deal with server capacity, upgrading infrastructure or outages under high load. BigQuery automatically adapts performance according to the volume of data and queries' difficulty. That means that whether you work with thousands or billions of rows, the system can adapt without slowing down markedly. For firms that is an enormous advantage above all in a period of growth, when the volume of data rises quickly.

The speed of processing data

BigQuery is designed for fast analytics. Thanks to distributed processing and parallel calculations it can process a large amount of data in a short time. With properly optimised tables and queries it is possible to analyse billions of rows within seconds. This speed lets firms react to data almost in real time, which is key, for instance, when optimising marketing campaigns or in operational decision-making.

Integration with the Google ecosystem

BigQuery is naturally connected with other tools within the Google Cloud ecosystem. You can simply connect it with tools such as Google Analytics 4, Google Ads or Looker Studio. Thanks to that you can centralise data from various sources in one place and then analyse or visualise it without complicated integration. For marketing and analytics teams that means faster implementation and more effective work with data.

Flexibility for start-ups and enterprise alike

BigQuery suits various types of firm regardless of their size. Start-ups will appreciate above all the simple start and low entry costs, while large companies will use its power and scalability when working with extensive data. A great advantage is also the so-called free tier, which lets you process roughly 1 TB of data a month free and use 10 GB of storage without charge. That gives firms the option of testing the solution or starting to work with data without a marked financial investment.

"Today's problem is not a lack of information but its chaos. Firms have data broken up across CRMs, ads and Excel files that do not talk to each other. In such an environment it is impossible to build a working AI model. BigQuery is not the only route for us - solutions such as Snowflake or AWS have their place. We chose it as our standard, though, because in the Google ecosystem it works without friction. It lets us join a user's behaviour with their buying history and marketing costs in one place. Only when data is unified this way can we tackle predictive marketing that genuinely raises profit. The aim is not the technology itself but the ability to make the right business decision based on facts."

Jakub Gombár AI Team Leader & Co-Founder

BigQuery's drawbacks and risks

Although BigQuery brings plenty of advantages, it matters to know its limits and potential risks too. Grasping these aspects properly will help you avoid needless costs and use its possibilities more effectively in practice.

Costs with unoptimised queries

One of BigQuery's most frequent drawbacks is the costs connected with badly designed queries. Since BigQuery works on the principle of paying for the volume of data processed (pay-per-bytes-scanned), every query going through a large amount of data can markedly raise costs. If a query scans whole tables instead of particular columns or filters, for instance, that can lead to ineffective use of resources. 

Without optimisation such as partitioning, clustering or the proper use of WHERE conditions, costs can quickly get out of control. So it matters to pay attention to the quality of SQL queries and the data structure.

Dependence on Google Cloud

Using BigQuery also means a certain degree of dependence on the Google Cloud ecosystem. All data, processes and integrations are tied to Google's infrastructure, which can be a drawback for some firms. This so-called vendor lock-in situation can complicate any move to another solution in future. Migrating data and processes can be demanding on time and money, above all if BigQuery is deeply integrated into company systems.

The need for technical knowledge

Although BigQuery is user-friendly, using it effectively demands certain technical knowledge. The basis is a grasp of SQL, but understanding data architecture, optimising queries and working with large datasets matters too. For non-technical teams the start can be more demanding, above all if they have no experience of databases or analytics. In such cases collaboration with a data analyst or training the team is often needed so that BigQuery's potential is used to the full.

Specific examples of using BigQuery

1. Owning your data (the end of GA4's 14-month memory)

The standard GA4 interface keeps detailed data about users' behaviour for only 14 months. If you want today to compare customers' behaviour during a campaign two years ago, you simply will not find this data in GA4 any more.

The benefit: BigQuery serves as a lifetime archive. Everything that happens on your site stays there for good.

In practice: you have an overview of how buying behaviour or lead quality develops over years, not merely months.

2. Search Console without limits

The ordinary Search Console interface will show you only the first 1,000 keywords. If you have a large blog or e-shop, you lose most of the data from the "long tail" (specific searches), which often brings the best conversions.

The benefit: in BigQuery you have every single search query your site appeared for stored, regardless of the number of rows.

In practice: you identify the exact questions people enter into Google and can create content for them that genuinely earns, instead of merely guessing at subjects.

3. Joining marketing with real money (CRM integration)

This is one of most firms' greatest weaknesses. Marketing sees a conversion on the site, but the salesperson in the CRM sees that the lead was irrelevant. These systems ordinarily do not talk to each other.

The benefit: in BigQuery you connect data from Google Ads or Facebook with data from your CRM (e.g. Pipedrive or HubSpot).

In practice: at the end of the month you see a report that says not merely "it cost €1,000 and brought 20 leads" but "it cost €1,000 and brought 3 closed deals worth €15,000". That is a genuine overview.

4. Reports that do not freeze (Looker Studio)

If you connect Looker Studio directly to GA4, reports are often slow, fall over because of API limits or the data takes too long to load.

The benefit: BigQuery works as an intermediate layer. Looker Studio does not pull data from GA4's slow interface but from a fast database.

In practice: a dashboard for management loads in a second and the data in it is clean, pre-processed and visually consistent.

5. Google Sheets on steroids

Many marketers want to work in spreadsheets, but you simply cannot put millions of rows from analytics into them without Sheets ceasing to work.

The benefit: using the Connected Sheets feature you work with data from BigQuery directly in Google Sheets while using the cloud's power.

In practice: you create pivot tables and analyses from enormous volumes of data in an environment you know well, without needing to program.

BigQuery vs other tools

When choosing a tool for analysing data in the cloud, firms often decide between several solutions. Among the best-known alternatives to BigQuery are Snowflake and Amazon Redshift. Each of these tools has its specifics, advantages and drawbacks, and so it matters to grasp the differences between them.

BigQuery vs Snowflake

BigQuery and Snowflake are both modern cloud data warehouse solutions and offer similar possibilities in analytics. The main difference lies in the approach to infrastructure and managing performance. BigQuery is a fully serverless solution, which means you do not have to worry about any performance settings or resource management. Everything works automatically in the background. 

Snowflake does offer a high degree of automation too, but it allows more detailed control over computing resources (so-called virtual warehouses). In terms of price, BigQuery uses a pay-per-bytes-scanned model, while Snowflake combines payments for computing power and storage. That means BigQuery can be more advantageous with occasional analyses, while Snowflake may better suit a stable, predictable workload.

BigQuery vs Redshift

Amazon Redshift is a data warehouse from AWS that was one of the first cloud solutions of its kind. Compared with BigQuery, though, it demands more infrastructure management. BigQuery, conversely, abstracts these tasks away and gives a simpler user environment. On the other hand, Redshift can be a suitable choice for firms that already use the AWS ecosystem and want greater control over performance and configuration. BigQuery in turn stands out above all in speed of deployment and simplicity of use.

When to choose BigQuery

BigQuery is the ideal choice above all in situations where you want to start working with data quickly without needing to manage infrastructure. It suits firms needing a scalable solution that work with large volumes of data and want to use the Google ecosystem's advantages. It suits teams that prefer a simple setup and do not want to invest time in managing servers or performance too. At the same time it is a good choice for projects where the volume of data and analytical needs change dynamically, since it can adapt automatically.

BigQuery represents one of the most modern tools for analysing data in the cloud and is ever more often becoming the standard for firms wanting to make decisions on the basis of data. In an environment where the volume of information keeps growing and the speed of reaction decides success, the ability to work with data effectively is a key competitive advantage. 

If you are only starting with BigQuery, you need not fear the complexity. Thanks to the free tier you can try it without big investment and gradually build your know-how. What matters is starting and gradually extending your use. On the other hand, if you already work with data and are looking for a way of moving analytics to a higher level, BigQuery can open new possibilities for you. From advanced analyses to machine learning.