Google BigQuery: The Best Marketing Data Warehouse

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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.

Understanding the Concept: What is a Data Warehouse for Marketing

A data warehouse is a centralized cloud-based repository for storing and analyzing diverse cross-channel marketing data. 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.

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

  • 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 (eg. 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.
  • 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 (eg. 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.

Understanding the Differences between Data Lakes and Data Warehouses

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 begining stages (or if you are just starting our establishing 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.

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 Warehouses

Data Lakes

Designed for

Structured data from relational databases.

Can store structured, unstructured, and semi-structured data.


Is required before storing data.

Allows for storage of raw data


Analytics for business decisions

Cost-effective big data storage    

Suitable for

Ideal for scenarios where queries and reports are well-defined and consistent.

Ideal for exploratory data analysis, machine learning, and big data applications.


Only stores data relevant to analysis

Stores all data that might be used—can take up petabytes!


Less flexible in handling changes in data types and structures.

More flexible and adaptable to changes in data types, formats, and structures.

Type of data

Primarily used for analyzing historical data for business intelligence

Designed to handle large volumes of data from diverse sources.


Data analysts, Digital analysts, Business analysts

Data scientists and Data engineers

Importance of Data Warehouses and Data Lakes in Marketing

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

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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:

  • Starts free;
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  • It doesn’t require you to upload data as CSV files or use paid third-party services

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Why is Google BigQuery the perfect data lake for marketing?

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.

What is Google BigQuery

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.

Google BigQuery's architecture, designed for handling extensive data and complex queries in real-time, is built on Google's advanced technologies like Colossus for reliable storage, Dremel for swift data processing, Jupiter for scalable management, and Borg for isolated query execution.  Its table-based data model, supporting various file formats, enhances efficient data querying through partitioned tables.

Google BigQuery Advantages

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 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 a vast number of specialists can work with it. It also comes with ready-made sets of SQL queries so you can 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 case of system failures. This is accomplished through the automatic replication and distribution of 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 with no 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 more than 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 the takeaways of this article or go 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.

Features of Google BigQuery

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 any attachments from you. 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 requests per month for free. 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.

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  1. 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.
  2. 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 and 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.

Use cases of Google Bigquery For Marketing Tasks

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

Onmichannel Paid Ads Campaign Performance Analysis

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, TikTok and 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.

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Combining Online Web Analytics with Sales CRM

Marketing agencies can use BigQuery to segment their audiences and personalise 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 prior to 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.

Comprehensive Attribution Analysis

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.

E-commerce Data Analysis with BigQuery

Using BigQuery enables marketers to scrutinize e-commerce behaviour like sales data, identifying standout products, forecasting demand, and refining 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:


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Key Takeaways

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.

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  • What makes BigQuery suitable for AI and Machine Learning projects?

    BigQuery integrates seamlessly with machine learning, allowing SQL-based model creation and execution within its environment, simplifying AI and ML implementations.
  • Can BigQuery handle real-time data processing?

    Yes, BigQuery excels in real-time data processing with capabilities for streaming data ingestion and near real-time analytics, ideal for time-sensitive data analysis.
  • How does BigQuery maintain data security within a data warehouse framework?

    In a data warehouse environment, BigQuery upholds stringent data security by implementing encryption, adhering to international standards, and employing advanced access control measures, such as row-level security.
  • How does Google BigQuery (GBQ) enhance a data warehouse solution?

    GBQ is a cloud-based data warehouse that complements and enhances data warehouse solutions by enabling the storage and querying of extensive datasets. It integrates smoothly with other Google Cloud Platform services, offering a robust platform for processing and analyzing data.
  • What are the advantages of utilizing BigQuery for data warehouse analytics?

    Key benefits of using BigQuery in a data warehouse context include rapid query performance, scalability, user-friendliness, cost-efficiency, versatility, and sophisticated machine learning capabilities.
  • How does BigQuery as a data warehouse compare to conventional data warehouses?

    BigQuery, as a modern data warehouse solution, holds distinct advantages over traditional data warehouses. These include a pay-per-use pricing model, rapid querying capabilities, the elimination of initial hardware or software investments, and seamless integration with other services on the Google Cloud Platform.

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Top 30 handpicked Google Data Studio dashboards for marketers

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Top 30 handpicked Google Data Studio dashboards for marketers