Google BigQuery: The Best Marketing Data Warehouse

To implement advanced analytics in a business, having access to a robust dataset is very important. After deciding to collect your data, the next critical step is determining its storage solution. So the question is - do you opt for a conventional data warehouse or a data lake?

In this article, we'll explore why Google BigQuery stands out as the best choice for a marketing team, offering advanced features and capabilities that are well-suited for modern marketing analytics.

Note: This post has recently been updated in June 2024.

A data warehouse is a centralized cloud-based repository for storing and analyzing diverse cross-channel marketing data, including the capability to analyze data from various marketing channels.

It enables marketing and analytics teams to consolidate and integrate data from various sources, such as advertising platforms like Facebook Ads and Google Ads, web analytics tools like Google Analytics 4, and CRM systems like HubSpot and Salesforce.

The key advantages of utilizing a data warehouse in marketing include cost-effective and scalable storage, coupled with the convenience of centralizing and blending data from multiple streams for streamlined analysis.

Data within a marketing data warehouse is organized into structured tables, facilitating quick and efficient querying of specific data for reports or in-depth analysis.

Most data warehouses, including BigQuery, employ SQL as the query language, enhancing their accessibility and ease of use.

Data warehouses are fundamentally composed of two main elements: storage and computing.

First, storage: Data warehouses provide the capacity to store and centralize data from numerous sources over a period of time.

This capability liberates organizations from the limitations of individual marketing platforms’ data retention policies*(e.g., Google Analytics 2-14 months).*

All necessary cross-channel data for historical analysis and benchmarking is neatly consolidated in one location, offering cost-efficient storage solutions that scale with the growth of the dataset so you can avoid data sampling** and data cardinality limitations.**

Second, computing: Beyond storage, data warehouses are equipped to process large volumes of data.

Unlike on-premise solutions, which can’t easily expand their hardware capabilities, cloud-based data warehouses like BigQuery allow for rapid provisioning of additional resources with just a few clicks.

This flexibility is crucial for analytics, as it ensures the ability to query large datasets (e.g., website sessions) for real-time insights and decision-making swiftly.

To better understand how a data warehouse works, let’s compare a traditional data warehouse to a data lake.

The difference between data warehouses and data lakes can be likened to choosing between two distinct sets of building tools for constructing a castle.

Imagine you want to build a castle with towers. You can choose between two distinct sets of building tools:

• cubes of the same size in various colors;
• 250-piece LEGO set with bricks of all shapes, sizes, and colors.

If you opt for simplicity and use basic colored cubes, you’re looking at a data warehouse approach. But before you can store data, it needs preprocessing to fit into a structure.

In other words, you need to:

• spend time preprocessing the data
• build your castle exclusively from uniform cubes

It’s particularly useful for businesses in their beginning stages (or if you are just starting our establish a data analytics system), where data needs are straightforward and manageable. ​However, data warehouses limit businesses in creative flexibility.

But if you want to build a Disney castle with turrets, windows, weather vanes, and trebuchets, you need a LEGO set (i. e. a data lake).

The beauty of a data lake is its capacity to accommodate raw, unstructured data from a multitude of sources - be it advertising services, mobile apps, CRM systems, or even vending machines.

This variety allows you to selectively extract and utilize data to tailor reports and analyses that precisely meet your business needs. Moreover, a data lake is designed to not only store but also process this vast array of data efficiently, enhancing your ability to secure and analyze data within the Google Data Lake ecosystem.

Additionally, with a data lake, you don’t need to spend time preprocessing data.

You just need to set up connectors between data sources and the data lake once. Then you can create any reports. The most exciting thing is that a data lake allows you to create dashboards with real-time updates — precisely what you need to instantly respond to critical changes in your metrics and KPIs!

Let’s get a clear vision of the differences between Data Warehouses and Data Lakes.

Data Storages play a significant role in current business strategies, especially in the era of data:

Establishing a Unified Data Source: Scattered marketing data can hinder a team's efficiency.

Data warehouses solve this by amalgamating all data into a single, unified source. This consolidation simplifies access to key metrics like Customer Acquisition Cost (CAC), Return on Investment (ROI), and Return on Ad Spend (ROAS), enhancing decision-making efficiency for marketers.

Rapid Insight Generation: Setting up a cloud-based data warehouse, like Google BigQuery, is straightforward and doesn't require costly hardware or physical data centers.

Enhanced Analytics Features: Data warehouses support complex query processing and seamlessly integrate with data visualization or BI tools. They enable real-time data transfer to popular analytics platforms like Looker Studio, Power BI, and Tableau without additional setup, streamlining the analysis process.

Complete Control Over Historical Data: Data warehouses provide independence from the data retention limitations of platforms like Facebook, Google, or HubSpot. They ensure secure storage of all historical marketing data in one place, granting complete access and control for comprehensive analysis.

Cost-Effective and Scalable Storage with Minimal Maintenance: Cloud-based data warehouses offer an affordable solution for both small and large businesses, with elastic storage that scales with your business's growth.

If you're looking to consolidate your marketing & sales data in one place - use OWOX BI Pipeline

If you want to build reports based on Google BigQuery data in your favorite Google Sheets or you want to upload data from Google Sheets to Google BigQuery, try out a free OWOX BI BigQuery Reports Extension.

​This Google Sheets Extension is used by over 150,000 customers for many reasons:

• It’s designed for collaboration between data and business teams;
• Starts free;
• Your data is 100 % safe and secure;
• No limitations;
• Dynamic filters that even non-technical users can use to adjust data retrievals.

There are so many data warehouse solutions on the market, but for marketing, there’s only one best option — Google BigQuery.

Let’s briefly describe what Google BigQuery is and why it’s the best data warehouse solution for storing marketing data.

Google BigQuery acts as a fully managed, petabyte-scale data warehouse, using the processing power of Google’s infrastructure. Google Cloud Storage and BigQuery together make Google Cloud Platform a scalable data lake that can store structured and unstructured data.

The fully-managed Google BigQuery data warehouse empowers lightning-fast SQL queries by harnessing the processing capabilities of Google's infrastructure.. It simplifies analytics by handling large datasets with ease, offering real-time insights through its high-speed streaming insertion capabilities. BigQuery is scalable, cost-effective, and integrates seamlessly with other Google Cloud services, making it ideal for big data exploration and business intelligence.

Google BigQuery's architecture is intricately designed to manage immense data volumes and execute complex queries swiftly. It is built upon a suite of advanced infrastructure technologies developed by Google, which ensures both efficiency and scalability. Here’s a detailed breakdown:

Colossus: Google's distributed file system, Colossus, underpins BigQuery by providing highly scalable and reliable storage. It handles large datasets by distributing them across numerous servers.

Dremel: At the heart of BigQuery's rapid data processing capabilities is Dremel, a powerful distributed query engine. This allows BigQuery to perform fast, efficient analyses of massive datasets.

Jupiter: This cluster management system dynamically adjusts resource allocation, enabling BigQuery to scale based on the demands of the workload.

Borg: Borg is a sophisticated container management system that isolates and securely runs BigQuery queries, ensuring efficient use of resources and consistent performance.

Tables and Data Sources: BigQuery's data model centers around tables that are constructed from various sources like CSV, JSON, and AVRO files. These tables are partitioned to enhance query performance by enabling more targeted data scanning.

Query Execution: When a query is issued, the BigQuery optimizer first analyzes and optimizes it, choosing the most efficient execution path based on data distribution, query complexity, and resource availability.

Parallel Processing: The optimized query is split into sub-queries, which are executed concurrently across the nodes of the BigQuery cluster, leveraging the power of Dremel.

Intermediate Storage: During execution, intermediate results are temporarily stored in BigQuery’s fast-access storage. These results are managed efficiently to minimize latency and maximize throughput.

One key advantage of choosing BigQuery is its straightforward implementation.

As a fully managed cloud service, BigQuery removes the need for complex hardware or software setups.

It simplifies the creation of data marts for focused data analysis on specific business areas and allows for efficient querying and analysis of data stored in Google BigQuery data warehouses.

Google BigQuery enables the analysis of substantial amounts of data from diverse sources like CRM systems, social media channels, and advertising networks in real time. This capability provides critical insights into customer behaviors, preferences, and trends, facilitating the refinement and optimization of marketing strategies and campaigns.

It's difficult to imagine a marketer who doesn't work with Google Ads, Google Analytics 4, Google Sheets, Looker Studio, YouTube, and other Google products. Google is a real monster of marketing and advertising. And Google BigQuery is part of Google's infrastructure. In simple words, this means - native connectors (eg. Google Ads Data Transfer).

Google is continuously developing its cloud services platform, including BigQuery. So you don't need to worry that this service will be abandoned and cease to be supported and updated. Among its other advantages, Google BigQuery is simple and fast, and many specialists can work with it. It also comes with ready-made sets of SQL queries to get useful insights from your collected data.

Google BigQuery works with machine learning (ML) and artificial intelligence (AI), which help you analyze and automate your marketing by segmenting audiences, searching for useful insights, and doing many more things to make your life easier.

Google BigQuery offers robust fault tolerance to maintain data accessibility, even in system failures.

This is accomplished by automatically replicating and distributing data across numerous servers and data centers, ensuring data is always available. BigQuery takes the burden of hardware malfunctions and software updates off marketing agencies.

Consequently, agencies can consistently access and analyze their data without disruptions, even during system failures.

Google BigQuery's blend of swift performance, scalability, straightforward implementation, cost-effectiveness, and robust security features establishes it as a formidable choice for businesses in need of a potent data warehousing solution.

The bottom line is that Google BigQuery is a fully managed serverless data warehouse that enables safe and scalable analysis of petabytes of data.

For over a decade, Google BigQuery has been developing, improving, and providing marketers and analysts with a convenient interface and extensive capabilities.

If you're already sold on BigQuery, you can immediately jump to this article's takeaways or read other articles about setting up and working with BigQuery. If you're still on the fence, here are some reasons why you should give BigQuery a try.

Let's take a closer look at why Google BigQuery is the best choice for today's marketers.

1. Integrations. BigQuery is part of the Google Cloud Platform (the leader in Data Management for Analytics according to Forrester Research), which means native integrations with other Google products, including Google Analytics 4 and Google Ads.
2. SQL Interface: Leveraging BigQuery's SQL interface, businesses can efficiently query unstructured data, allowing for quicker insights. This familiarity with SQL expedites the analysis process. You can use SQL queries with ease and at any scale.
3. No physical servers. Using the BigQuery cloud service doesn't require you to attach anything. In addition, no matter where your employees work, they'll always have secure access to data. BigQuery has expanded its use of remote functions (UDFs) to process unstructured data. This involves the use of object tables, which support signed URLs, allowing Cloud Functions or Cloud Run to process this data effectively.
4. Data security. All data in BigQuery is protected according to Google's standards. BigQuery is advancing its security features, particularly in governing unstructured data. This includes the implementation of row-level security in object tables, ensuring controlled access, and enhancing data security.
5. Facilitating Data Sharing: The platform's data-sharing capabilities have been extended to unstructured data. This allows businesses to share critical data with partners and stakeholders securely and efficiently, maintaining data integrity and confidentiality.
6. Cost. All users receive 10GB for storage and up to 1 TB of free requests per month. In addition, new users receive $300 for 90 days to pay for services on the Google platform. For more information, see Google's guide to BigQuery pricing and cost controls.

7. BigQuery ML is also highly effective in a data warehouse setting. This service empowers specialists to develop predictive models on structured and semi-structured data housed within the data warehouse. It facilitates the processing of unstructured data as well, minimizing the need for extensive manual preprocessing.
8. Enhanced Search Index Features: BigQuery's search index capabilities now encompass unstructured data. Whether through BigQueryML for image inferences or document extraction via remote UDFs, these functionalities enhance the platform's search and analysis capabilities.

To summarize, Google BigQuery is part of a large ecosystem that's continuously growing and developing.

You can use it to apply machine learning, discover emerging data patterns, and test new hypotheses.

You can restore a deleted table if it hasn't been more than seven days since it was deleted and if you know its name and the name of the dataset from which it was deleted.

This will lead to timely insights into how your business is performing, which will enable you to modify your processes for better results.

BigQuery offers various capabilities for marketers, with three key scenarios to kickstart your strategy:

Consolidate data from various PPC campaigns across different channels to discern the most effective platforms for each campaign. With OWOX BI for BigQuery, data from popular platforms like Facebook, Instagram, LinkedIn, and TikTok, Bing Ads can be centralized in BigQuery, allowing comprehensive visual reporting of integrated data. This centralization leads to smarter budget allocation and enhanced ROI in performance marketing.

Marketing agencies can use BigQuery to segment their audiences and personalize their marketing campaigns. Understanding visitor behavior both before and after conversion is crucial for digital marketers.

Typically, user interactions with multiple pages and content occur over several visits before conversion. Initially, this data is linked to an anonymous clientID, which can be integrated into Google BigQuery.

Upon conversion, a CRM userID record is created and linked to the clientID, enabling the tracking of both pre- and post-conversion activities.

Using OWOX BI with BigQuery, you can effectively monitor on-site activities both before and after conversion from Google Analytics 4 and merge this with detailed data from your CRM.

Attributing conversions to a single channel or touchpoint is complex in modern consumer behavior. Collecting all interactions across various channels and sources, both before and after conversion, enables a more profound understanding of both the marketing funnel and the customer journey. This holistic view is essential for effectively analyzing and optimizing marketing strategies.

Using BigQuery enables marketers to scrutinize e-commerce behavior like sales data, identify standout products, forecast demand, and refine pricing tactics.

Additionally, it facilitates the analysis of customer behaviors, like cart abandonment and product preferences, and gathers customer feedback. These insights are instrumental in bolstering customer retention and loyalty. To leverage BigQuery for e-commerce analytics effectively, it's essential to amalgamate various data types, including sales, product, and customer information, into the platform.

Useful links:

• Get started with Google Cloud Platform — An interactive tutorial to learn the basics of the Google Cloud Platform
• Overview of the main Google BigQuery features — Practice writing requests for marketing analysis; look at the main functions of BigQuery and see their possibilities using specific examples; learn how to write basic queries and test them on demo data
• Connecting BigQuery and Google Sheets — Find out how to build any report or graph in Google Sheets based on data from GBQ without needing to upload data as CSV files or use paid third-party services

• Modernize your data warehouse with BigQuery — Discover how a brick-and-mortar and online retailer uses advanced analytics in BigQuery to better forecast demand and optimize their operations in real-time
• Building an e-commerce recommendation system using BigQuery ML

Data Management Challenges: Integrating multiple data sources into BigQuery can be complex and may require significant data transformation. Also, being deeply integrated with Google Cloud can make it challenging for those not already within that ecosystem.

Security and Compliance: Users are responsible for managing data access and ensuring compliance with regulations like GDPR. There is also limited control over where data is physically stored, which can be a compliance issue for some organizations.

Skillset Requirement: Effective use of BigQuery requires expertise in SQL and data schema optimization. Organizations might need to invest in training or hire specialists, adding to overall costs.

OWOX BI simplifies the maintenance of BigQuery by automating data integration and transformation, which reduces the complexity and technical expertise required. It enhances BigQuery's usability for all users, ensuring that even non-technical staff can manage and analyze data effectively.

With OWOX BI, you can combine data from advertising services, website tracking systems, offline stores, call tracking systems, and CRMs into Google BigQuery.

Marketing analytics, with its data-based conclusions and forecasting, is a necessity for any modern business. It's no longer a toy for the rich but a necessary and useful tool for business development and progress. However, to fully use and benefit from advanced analytics, it's essential to create a basis for it.

To implement new tools, machine learning, and various methods for optimizing advertising campaigns, a business needs to make decisions based on the data it's collected. For marketing departments, the best solution for storing data is a data warehouse— specifically, the popular and convenient Google BigQuery.