Google BigQuery data warehouse is a platform that allows businesses to store, manage and analyse large datasets in real-time. In a marketing context, it offers instant access to customer data, scalability, and the ability to handle complex queries.
The benefits of using a BigQuery data warehouse include identifying growth opportunities and optimising marketing strategies for better ROI. With its ability to handle large volumes of data, BigQuery enables marketers to integrate data from multiple sources and gain valuable insights into customer behaviour.
In a nutshell, Google BigQuery data warehouses offer a cost-effective, scalable and powerful solution for marketing professionals seeking to access, manage, and analyse large datasets to improve their marketing strategies.
In this article, we will delve into five primary advantages of using Google BigQuery for marketing.
What is Google BigQuery?
A data warehouse is a large, centralised repository of data from various sources within an organisation. It is designed to support business intelligence activities, such as data mining and analysis, by providing a single source of truth for decision-making purposes.
As we’ve established, Google BigQuery is a cloud-hosted data warehousing service that allows businesses to store, manage, and analyse large datasets without the need for hardware or software setup and management. The service is fully-managed, ensuring easy accessibility for businesses of all sizes.
With its powerful processing capabilities, BigQuery data warehouse is a popular choice for marketing professionals who need to process large volumes of data quickly and efficiently.
What Are the Key Features of BigQuery?
With so many data warehouse solutions available on the market, you may be asking yourself why we’re adamant BigQuery is the best one out there.
One of the many reasons a user might choose a BigQuery data warehouse over other solutions is its ease of implementation.
BigQuery is a fully-managed cloud solution that eliminates the need for hardware or software setup and management, making it easy to implement and use. It can be used to create data marts, which are smaller, more focused subsets of data for specific business needs, as well as to query and analyse data stored in data lakes.
It also integrates seamlessly with other Google Cloud Platform services, such as Google Analytics and Google Ads, providing a unified solution for data analysis and optimization.
It offers cost-effectiveness as users only pay for the amount of data processed, making it affordable for businesses of all sizes. Additionally, its security features, such as encryption and access controls, ensure that data is protected and compliance requirements are met.
Overall, a BigQuery data warehouse combines speed, scalability, ease of implementation, affordability, and security make it a compelling choice for businesses seeking a powerful data warehousing solution.
The Google BigQuery architecture is designed to handle large volumes of data and complex queries in real-time. It’s built on top of a number of low-level infrastructure technologies developed by Google, including:
- Colossus: A distributed file system that provides scalable and reliable storage for BigQuery data.
- Dremel: A distributed query engine that allows BigQuery to process large datasets quickly and efficiently.
- Jupiter: A cluster management system that provides BigQuery with the ability to scale up or down based on demand.
- Borg: A container management system that allows BigQuery to run queries in isolated environments.
BigQuery’s data model is based on the concept of tables. Tables can be created from a variety of data sources, including CSV, JSON, and AVRO files, and can be joined together to create more complex queries. Each table in BigQuery is divided into multiple partitions, which allows for faster and more efficient querying of data.
When a query is submitted into the BigQuery data warehouse, it is first analysed and optimised by the query optimizer. The optimizer selects the most efficient way to execute the query, taking into account factors such as data distribution, query complexity, and available resources.
The query is then broken down into smaller sub-queries, which are executed in parallel across multiple nodes in the BigQuery cluster.
As the query runs, intermediate results are stored in temporary tables, which are deleted once the query is complete. The final results are returned to the user in the form of a table, which can be further processed or exported as needed—voila!
5 Advantages of Using Google BigQuery for Marketing Agencies
The advantages of using Google BigQuery for marketing agencies are countless. Read on to find out more about the five major benefits the solution has to offer:
#1. Enhance Business Intelligence Capabilities
Google BigQuery allows you to analyse large volumes of data from multiple sources, such as CRM systems, social media platforms, and advertising networks, in real-time. This offers valuable insights into customer behaviour, preferences, and trends, which can be used to optimise marketing strategies and campaigns.
#2. Single Source of Truth
With BigQuery, you can store, analyse and view data from all your sources in a single, cohesive data warehouse. This eliminates the need to export data from different platforms and merge them manually, saving you time, effort, and resources.
#3. Scalability And Real Time Performance:
With BigQuery, marketing agencies can easily scale their data processing capabilities as the volume grows. Its ability to perform real-time analysis allows marketing agencies to quickly react to changes in customer behaviour and optimise their marketing strategies accordingly, resulting in more effective campaigns and thus, increased profit.
Google BigQuery security measures include data encryption at rest and in transit, access controls, auditing, and compliance certifications. To top it all off, integration with other Google Cloud Platform services, such as Identity and Access Management (IAM), also enables marketing agencies to manage and control user access and permissions to their data.
#5. Fault tolerance:
Google BigQuery provides built-in fault tolerance capabilities to ensure data availability in the event of system failures. This is achieved through automatic replication and distribution of data across multiple servers and data centres.
Additionally, BigQuery’s serverless architecture and fully managed infrastructure eliminate the need for marketing agencies to worry about hardware failures or software updates. This means that even in the event of a failure, marketing agencies can continue to access and analyse their data without any interruption.
BigQuery Use Case Scenarios
As a marketer, how can you leverage the power of Google BigQuery to unlock the advanced analysis, modelling, and activation possibilities it has to offer?
To give you a clearer picture of what this might look like, we’ve outlined three use case scenarios below.
#1. Personalization and Segmentation
Marketing agencies can use BigQuery to segment their audiences and personalise their marketing campaigns. For instance, they can use BigQuery to analyse customer data such as purchase history, browsing behaviour, and demographic information to identify high-value customers, predict their future behaviours, and create targeted campaigns that speak to their specific interests.
To implement BigQuery for personalization and segmentation, the agency needs to integrate customer data from different sources into BigQuery, such as transactional databases, CRM, and web analytics.
#2. Ad Campaign Optimization
Marketing agencies can use BigQuery to optimise their ad campaigns and measure their effectiveness. For example, they can use BigQuery to analyse clickstream data and track user behaviour across different channels, such as search, social, and display ads.
By identifying patterns and trends in user behaviour, the agency can optimise their ad campaigns, improve targeting, and reduce ad spend waste. To implement BigQuery for ad campaign optimization, the agency needs to integrate ad data from different sources, such as Google Ads, Facebook Ads and other ad networks, into BigQuery.
#3. E-commerce Analytics
By implementing BigQuery, marketers can analyse sales data to identify top-performing products, predict demand, and optimise pricing strategies. BigQuery also allows you to analyse customer behaviour such as shopping cart abandonment, product recommendations, and customer feedback, which in turn can help improve customer retention and loyalty.
To implement BigQuery for e-commerce analytics, the agency needs to integrate data from different sources, such as sales data, product data, and customer data, into BigQuery.
What Types of Queries Does Google BigQuery Support?
Although most digital marketing agencies would benefit from data warehousing, the truth is that many still feel intimidated by the tech. This means that account managers continue to spend countless hours pulling and cleaning data for reporting, which in turn affects the profitability of their agency.
One of the key features of BigQuery is its ability to support interactive and automated queries. With its distributed query processing engine, BigQuery can execute queries on large volumes of data in a matter of seconds—allowing users to interactively explore their data and extract insights without having to spend hours combing through all their sources.
In addition to interactive queries, BigQuery also supports automated queries through its integration with Google Cloud Functions and Google Cloud Composer. This allows users to schedule and automate queries based on specific events or time intervals, an incredibly useful feature when it comes to generating regular reports, triggering data pipelines, or updating dashboards.
BigQuery also supports sharing and collaboration through its integration with Google Cloud Storage and Google Drive. Users can easily share datasets and queries with others, control access permissions, and collaborate on projects—ideal for those working in larger teams.
Google BigQuery Interaction Methods
Marketing agencies can integrate BigQuery with web user interfaces, APIs, and TensorFlow to enable more efficient data processing and optimise their campaigns.
By doing so, you’ll be able to easily access and visualise data for analysis and reporting, as well as build dashboards and data visualisations in order to quickly identify patterns, trends, and insights.
TensorFlow is an open-source machine learning framework that can be used to train and deploy models that can predict user behaviour, optimise marketing campaigns, and more. Using BigQuery with TensorFlow, marketers can leverage machine learning to develop predictive models and improve targeting.
Google BigQuery Performance
BigQuery’s tree architecture is optimised for performance and scalability, making it a powerful tool for processing and analysing large volumes of data.
Its serverless service model, SQL and programming language support, and real-time analytics capabilities make it a versatile solution for data-driven organisations seeking to derive valuable insights from their data.
Here’s how it performs based on the tree architecture criteria:
- Serverless Service: BigQuery is a serverless service, which means that it does not require any infrastructure management or maintenance. This makes it easy to scale up or down as needed without worrying about capacity planning or provisioning. Additionally, serverless architecture makes BigQuery highly available and fault-tolerant.
- SQL and Programming Language Support: BigQuery supports standard SQL queries, which makes it easy for users to write and execute queries. Moreover, BigQuery supports a wide range of programming languages such as Python, Java, and R, allowing users to develop custom applications and integrations with ease.
- Real-time Analytics: BigQuery’s columnar storage and distributed query processing engine enable real-time analytics and processing of large datasets. Users can analyse data in real-time using tools such as Data Studio, which provides live dashboards and reports that are updated as data is added to BigQuery.
How Does BigQuery Pricing Work?
One of the best things about BigQuery data warehouse is that its pricing is designed to be flexible and scalable, allowing users to pay only for what they use. BigQuery pricing is based on two main factors: storage and query.
This is based on the amount of data stored in the service. There are two types of storage: long-term and active storage.
Long-term storage is the cost of storing data that has not been accessed for 90 days or longer and is charged at a lower rate than active storage. Active storage includes storing data that has been accessed within the last 90 days. The current cost of active storage is $0.02 per GB per month.
Query pricing is based on the amount of data processed by a specific query. When you run a query in BigQuery, the amount of data scanned will determine the cost. BigQuery provides users with 1 TB of free data processing each month. If you exceed this limit, you will be charged for any additional data processed.
What Makes BigQuery Different From Other Enterprise Data Warehouse Alternatives?
BigQuery is a superior enterprise data warehouse alternative due to its scalability, speed, and cost-effectiveness. It can handle massive amounts of data and process queries in seconds, making it ideal for organisations of all sizes.
We’ve created a table so you can easily compare the advantages of BigQuery against other popular data warehouse alternatives below.
BigQuery vs NoSQL + MapReduce
|Scalability||BigQuery is a fully managed service that can easily scale storage and compute resources.||NoSQL can scale horizontally by adding more nodes to the cluster.||MapReduce can scale horizontally by adding more nodes to the cluster.|
|Query Language||BigQuery uses a SQL-like query language that is easy to learn and use.||NoSQL typically uses proprietary query languages that may require specialised programming skills.||MapReduce requires developers to write custom MapReduce jobs to query data, which can be time-consuming and complex.|
|Performance||BigQuery is optimised for fast, interactive queries over large datasets.||NoSQL can provide high performance for simple queries but may struggle with complex queries or large datasets.||MapReduce can be slow for ad-hoc queries or interactive data analysis due to its batch-oriented processing model.|
|Cost||BigQuery offers a pay-as-you-go pricing model that can be more cost-effective than running and managing your own NoSQL or MapReduce clusters.||NoSQL can be cost-effective, but managing your own cluster can be expensive and time-consuming.||MapReduce can be cost-effective, but managing your own cluster can be expensive and time-consuming.|
|Management||BigQuery is a fully managed service that handles the underlying infrastructure, security, and maintenance of the service.||NoSQL requires you to manage your own cluster, including infrastructure, security, and maintenance.||MapReduce requires you to manage your own cluster, including infrastructure, security, and maintenance.|
|Integration||BigQuery integrates with other Google Cloud services, such as Dataflow and Dataproc, making it easy to process and analyse data using a variety of tools and services.||NoSQL can integrate with other tools and services, but may require additional configuration or development work.||MapReduce can integrate with other tools and services, but may require additional configuration or development work.|
BigQuery Marketing Data Warehouse by Acuto
Every digital marketing agency out there has its own unique strategy. And as an agency, your data is spread across various channels: Google Ads, Data Studio, Analytics, as well as social media.
We help you set up and manage a BigQuery data warehouse tailored to your business, so you can sit back and focus on what you’re great at. Our data warehouse services have helped agencies from the planning phase all the way through to implementation. We keep working with them to implement data and automation solutions that set their agencies apart.
Google BigQuery data warehouse offers several advantages for marketing agencies, so let’s quickly recap some of the endless benefits.
- Ability to process and analyse large volumes of data quickly
- Data-driven decision-making and improved campaign performance
- Machine learning capabilities for predictive analytics
- Cost-effective pay-as-you-go pricing model
- Integration with other Google marketing tools such as Google Analytics and Google Ads
- Seamless data transfer and analysis
- Real-time data processing and analysis for faster insights
- Ability to create personalised and targeted marketing campaigns