Introduction
In today’s business world, integrating HubSpot with the BigQuery data warehouse is essential for effective data management and real-time decision-making. This integration enables marketing and sales teams to analyze comprehensive customer data—from website visits to ad interactions and customer service feedback—providing a fuller picture for better strategies. By combining HubSpot’s CRM data with other sources like Google Analytics, businesses can unlock valuable insights and optimize campaigns throughout the customer journey.
Without integration, data remains siloed, limiting scalability and advanced analytics. BigQuery offers a scalable, serverless platform to process large datasets quickly, supporting predictive models and AI-powered analysis. Daily synchronization ensures up-to-date, reliable data for accurate reporting and decision-making. Additionally, BigQuery’s robust security and privacy features protect business data in the cloud.
Integrating HubSpot with BigQuery empowers organizations to streamline operations, enhance marketing precision, and gain a competitive advantage through unified, actionable insights.
If you want to learn more about how integrating HubSpot with BigQuery can help your organization—and be equally useful to other companies — keep reading!
Common Challenges of Using HubSpot Without Integration and Siloed Data
Lack of Scalability and Advanced Analytical Features
HubSpot handles basic reporting well, but when you want to predict future customer activities or their absence, or analyze cross-channel purchase paths, difficulties arise. Analyzing large data sets from various sources requires a modern data warehouse. BigQuery technology is an innovative solution that provides high performance, scalability, and flexibility in data analysis. Properly configuring analytical tools like BigQuery, including setting up integration parameters and authentication, is key to security, efficiency, and further development of the Business Intelligence system.
In BigQuery, analysts can easily explore data, prepare advanced reports, and even create predictive models quickly and efficiently. Additionally, SQL query optimization in BigQuery allows effective analysis of very large data sets and processing vast amounts of information. The BigQuery implementation process includes environment configuration, integration with other systems, and user training, which is a crucial stage for effective system use. Generally, standard solutions offer limited scalability and analytical capabilities compared to what BigQuery provides.
Near Real-Time Data Synchronization
Delays in data synchronization between systems can lead to incomplete reports and wrong decisions. In companies that base their activities on precise marketing campaigns or advanced customer segmentation, such delays can mean real losses due to suboptimal use of marketing funds.
Third-party low-code and no-code tools may initially seem attractive as a solution to data synchronization problems. Unfortunately, they quickly prove unable to handle data scale or lead to a sharp increase in costs. They often also do not offer full functionality necessary to maximize data potential. Cloud services like BigQuery provide greater flexibility, security, and access to advanced data management and processing features. In low-code/no-code tools, data transfer and cleaning often occur with minimal clicks, emphasizing ease of use.
In the case of large data volumes, the key challenges become storage costs and managing data storage in the BigQuery data warehouse. The price of storing and processing data in BigQuery is determined by the amount of data stored and processed, often calculated per GiB or per number of rows ingested, which can significantly impact overall costs as data grows.
Traditional solutions often require manual infrastructure updates, generating additional costs and risk of errors. Modern data warehouses like BigQuery eliminate the need for manual updates by automating maintenance and environment management processes. Additionally, the lack of need to manage infrastructure allows companies to focus on data analysis instead of server management. BigQuery’s serverless architecture ensures scalability, enabling flexible resource adjustment to the company’s current needs.
If you ask yourself the following questions, definitely continue reading this post.
- Is my company not fully utilizing potential because I don’t have HubSpot and other system data (Google Analytics or ERP) in one place?
- Do data delays prevent me from making accurate decisions?
Benefits of Integrating HubSpot with BigQuery
Introduction to Google Cloud BigQuery
Google BigQuery is an advanced cloud-based data warehouse that enables companies to store, process, and analyze vast volumes of data in real time. Thanks to this technology, organizations can easily combine data from various sources, including HubSpot, and gain a complete picture of their business processes. BigQuery allows fast report generation, building advanced analytical models, and making accurate decisions based on current data. Users can interact with their data using natural language queries, making it easier to retrieve insights without complex coding. Integrating HubSpot with Google BigQuery opens new possibilities for companies in data analysis, optimizing marketing and sales activities, and effective information management in a dynamic business environment. For new customers, BigQuery offers free credits and a free tier, allowing initial testing and usage without upfront costs.
Advanced Ad Targeting
BigQuery is an analytical powerhouse that fills a crucial gap caused by the lack of connection between HubSpot and other systems in the company. Imagine segments so precise that they hit customer needs with surgical accuracy. These are no longer just segments; they are laser-targeted audience groups, ready for your campaigns to finally deliver spectacular results.
Integration with other data sources
HubSpot in its basic version offers limited integration capabilities with other systems. Thanks to the connection with BigQuery, organizations can easily combine data from the CRM with data from Google Analytics, ERP systems, or financial tools. This allows for a more complete understanding of customers and optimization of actions along the entire purchase journey.
Access to complete real-time data
Thanks to the integration of HubSpot with BigQuery, data is available in real time, enabling immediate response to business needs. Moreover, organizations receive not only access to up-to-date data but also data they can trust without limits. Eliminating uncertainty ensures decisions are based on reliable information, allowing for rapid and accurate responses to market and customer needs.
Organizations can create precise customer segments and conduct marketing campaigns with even greater accuracy. Additionally, the use of business intelligence tools and AI-powered agents allows building advanced reports and dashboards based on data from BigQuery, supporting business decision-making in e-commerce and technology sectors.
Technical Aspects of Integration
Technological Components
Integrating HubSpot with BigQuery requires the use of advanced technologies. In the previously mentioned client case study, we applied:
- Serverless GCP components: For example, Cloud Run was used for containerizing the application, and Firestore enabled easy storage of metadata generated by the ELT process. This allowed for flexible and efficient processing of large data volumes while maintaining low operational costs. The integration was associated with a specific Google Cloud project, ensuring proper resource management and access control.
- Configuring and connecting HubSpot and BigQuery: We configured the connection between HubSpot and BigQuery by specifying authentication settings and connection parameters, ensuring secure and reliable data transfer. The systems were connected to enable seamless data flow.
- ETL process with full data scope: All important HubSpot endpoints for the client were included, enabling access to complete information, including customers, deals, tickets, and many other data. The process captured the details of each object type, ensuring granular and comprehensive data transfer.
- Real-time monitoring: An automatic monitoring system detected missing data and ensured its completeness before transferring it to BigQuery, where datasets are stored and analyzed for business intelligence and reporting purposes.
For configuration or licensing information, users can refer to the relevant page in the documentation or user interface.
Stability and Costs
The use of serverless architecture makes the solution scalable, and its maintenance does not generate high costs. In practice, the biggest limitation is the HubSpot API limits, not the system’s capabilities.
Available Data for Synchronization
As part of the HubSpot and BigQuery integration, many types of data can be synchronized and analyzed in one place. These include contacts, companies, deals, engagements, tickets, quotes, forms, products (product data such as availability, scoring, or analysis of user behavior related to the product), conversations, landing pages (a type of page), tasks, marketing emails, ad accounts, ad campaigns, ad groups, ads, campaigns, site pages (data about websites, landing pages, and other elements of digital infrastructure; each page can be synchronized as a specific data type), blog posts, object lists, associations, invoices, media bridge, sequences, deal splits, sales documents, feedback submissions, subscriptions, and commerce payments. Content such as marketing emails, blog posts, and sales documents can also be managed and synchronized, supporting marketing and customer engagement efforts. Each object type consists of individual records, allowing for granular access and analysis of specific data entries. It is also possible to synchronize user data, which allows for analyzing user behavior and segmenting system users and optimizing campaigns and personalizing communication.
Thanks to this, companies can easily analyze the full spectrum of data – from emails to ad effectiveness – and make better business decisions based on comprehensive information available in BigQuery. Campaign analytics and performance indicators are available for analysis within synchronized data, enabling assessment and optimization of marketing activities as well as monitoring results and trends.
Synchronization Frequency
Data synchronization between HubSpot and BigQuery is performed daily at 06:00 UTC+0. Each day, a new folder is created with subfolders corresponding to individual tables, and new or updated datasets are generated to organize the synchronized data. Within each table, data is stored as rows, which serve as the basic unit of information for analysis and processing. The last seven synchronizations are always available on the Google Cloud platform, allowing access to the details of previous synchronizations for quick comparisons of data from different periods, change analysis, and trend identification. This ensures that companies using Google Cloud and BigQuery work with current and complete data, which translates into the efficiency of analytical and operational processes.
Data Security in the Cloud
Data security in the cloud is a priority for every company deciding to implement a modern data warehouse. Google BigQuery, as an integral part of Google Cloud Platform, provides the highest level of business information protection. Data is stored in advanced, monitored Google data centers, and access is regulated by multi-level authorization and authentication mechanisms. Every operation – from transmission to storage – is encrypted, effectively protecting data from unauthorized access. Thanks to this, companies can confidently use BigQuery’s analytical capabilities, focusing on analysis and drawing conclusions, knowing their data is secure in the cloud. It is this trust in Google solutions that makes more and more organizations choose Google Cloud Platform as the foundation of their data-driven strategy.
Trend Prediction Using AI
Modern marketing and sales require not only historical data analysis but also predicting future trends. Google BigQuery, thanks to integration with AI tools in Google Cloud, allows companies to build advanced predictive models that support making accurate business decisions. These models rely on the value of specific data points, such as key attributes or metrics, to ensure precise forecasting. BigQuery enables fast processing of huge amounts of data and uses it to train artificial intelligence models, including AI-powered agents that extend human capabilities in data analysis and workflow automation. These agents can forecast customer behavior, campaign effectiveness, or market changes. This allows marketing and sales teams not only to react to current events but also to actively shape strategies based on predicted trends. Integration of BigQuery with Google Cloud AI Platform services enables even more advanced analysis and optimization of activities, translating into real business benefits and competitive advantage.
Case Study: Optimization of HubSpot and BigQuery Integration for a Pharmaceutical Industry Client
Initial Challenges
Our client – a large brand in the pharmaceutical and dietary supplements industry – already had HubSpot integrated with BigQuery, but it was not properly or efficiently implemented. The system used for data transfer could not handle the operation scale and generated a number of problems. Key challenges included:
- Lack of data completeness, with missing or incomplete records affecting the reliability of insights.
- Synchronization delays.
- Limited scope of supported endpoints, making it difficult to access the full details of various data objects.
As a result, managers could not make decisions in near real-time and relied on incomplete data.
Implemented Solution
The company decided to optimize the integration process, replacing the previous solution with a more scalable approach. An ETL on GCP was implemented, using tools such as Pub/Sub, BigQuery, Cloud Run Functions, and App Engine to create an interface for configuring and monitoring the system. The integration was associated with a specific Google Cloud project to ensure proper management and access control.
The new architecture was designed not only to transfer data but also to ensure its consistency and integrity at every stage of the process. Batch and incremental processes enabled fast processing of large numbers of rows, with datasets being managed and stored in BigQuery for efficient analysis and reporting. The system can be configured for optimal performance, allowing adjustments to connection parameters and resource allocation as needed, ensuring data availability in near real-time.
Results
- Immediate access to complete data from HubSpot.
- Improved operational efficiency, leading to reduced churn and increased customer satisfaction.
- Cost optimization by replacing the Hevo system with an in-house solution.
Summary
Integrating HubSpot with BigQuery opens new analytical and operational possibilities, allowing better use of data. Organizations can increase their efficiency, improve process stability, and reduce operational costs. Such a solution can be helpful not only for your company but also for other companies that want to better analyze data and optimize activities.
If you feel you are not fully utilizing HubSpot’s potential, it is worth talking to our experts. We will help you design and implement optimal integration so that data works for your business instead of being a barrier to its development. Contact us to learn how such implementation can help you achieve your business goals and get ahead of the competition. Learn more about integration possibilities and the full implementation process – we will gladly answer all your questions.
