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Redpoint + Google BigQuery

Architecture summary

In this reference solution architecture document, we present a strategic framework for integrating Redpoint Global's Customer Data Platform (CDP) with Google's cloud-based data warehousing and analytics platform. This integration is designed to leverage the strengths of both platforms, enabling the seamless flow of unified customer data from Redpoint CDP into BigQuery for advanced data storage, processing, and analysis.

By outlining the technical and strategic considerations, this document serves as a comprehensive guide for our internal users, partners, and clients, empowering them to effectively unify customer data, derive actionable insights, and make data-driven decisions with unparalleled speed and accuracy. The architecture aims to optimize data utilization across platforms, enhance data governance and security, and provide a single source of truth for customer data.

With Google's scalable and high-performance data warehousing capabilities, organizations can efficiently store, manage, and analyze massive volumes of customer data, enabling real-time insights and personalized customer experiences. By integrating Redpoint CDP with BigQuery, businesses can unlock the full potential of their customer data, drive innovation, and gain a competitive edge in today's data-driven landscape.

The Redpoint CDP supports both a full SaaS solution and a data in place architecture when utilizing Google Cloud and BigQuery.

Example use cases

  • Unified customer view for personalized campaigns across multiple channels

  • Advanced customer segmentation for targeted outreach

  • Real-time customer insights for immediate action

  • Predictive analytics to forecast customer behavior

  • Simplified data governance and compliance management

  • Enhanced data security and privacy controls

  • Scalable data storage for growing customer data volumes

Logical diagram

Key components and roles

  • Redpoint CDP

    • Unified profile of individuals

    • Identity resolution with key persistence

    • API-accessible single customer views

    • Audience definition / segmentation

  • Google BigQuery

Data flows

Source system

Target system

Representative data

Transfer mechanism





Core Identity
Offer History
Modeling data



Redpoint sits directly on BigQuery and is able to query the databases and datasets without any data movement.



Web Events

Realtime Services





Real-time decisions
Visitor Profile




Transactional Systems






BigQuery ML


Model Scores
Next Best…




Why does this architecture/solution make sense?

In this architecture, Redpoint CDP acts as the central hub for customer data collection, unification, and enrichment, serving as the single source of truth for customer data, both consuming data from and persisting data within BigQuery. BigQuery functions as the robust and scalable data storage and processing powerhouse, enabling efficient data warehousing, advanced analytics, and secure data sharing. By integrating these two platforms, organizations can create a seamless data pipeline that leverages the strengths of each system.

Redpoint CDP's ability to collect and refine customer data from various sources, coupled with its advanced identity resolution and data quality management capabilities, ensures that high-quality, unified customer profiles are readily available. Google's cloud-based architecture and near-infinite scalability allow for the storage and processing of massive volumes of customer data, enabling real-time analytics and insights.

This integration empowers organizations to break down data silos, achieve a holistic view of their customers, and make data-driven decisions with unparalleled speed and accuracy. By leveraging Google's BigQuery capabilities, organizations can securely share customer insights across departments and with external partners, fostering collaboration and innovation. Moreover, the combined solution helps organizations maintain data governance and compliance with global data privacy regulations, as both platforms prioritize data security and privacy.

The Redpoint CDP + BigQuery architecture creates a powerful, end-to-end customer data management and analytics solution that drives personalized customer experiences, optimizes marketing strategies, and fuels business growth in today's data-driven landscape.

Business benefits

  1. 360-Degree Customer View: By integrating Redpoint CDP and BigQuery, organizations can achieve a comprehensive, unified view of their customers, enabling them to deliver highly personalized experiences, improve customer satisfaction, and increase customer lifetime value.

  2. Enhanced Data-Driven Decision Making: The combination of Redpoint CDP's data unification and enrichment capabilities with Google's advanced analytics and real-time data processing empowers businesses to make informed, data-driven decisions that drive growth, optimize marketing strategies, and improve operational efficiency.

  3. Scalability and Performance: Google's cloud-based architecture and near-infinite scalability, coupled with Redpoint CDP's robust data management capabilities, allow organizations to efficiently store, process, and analyze massive volumes of customer data, ensuring optimal performance and the ability to grow with the business.

  4. Secure Data Sharing and Collaboration: Google's secure data sharing features, combined with Redpoint CDP's data governance and compliance capabilities, enable organizations to safely share customer insights across departments and with external partners, fostering collaboration, innovation, and data-driven decision-making.

  5. Reduced Data Silos and Improved Efficiency: By integrating Redpoint CDP and BigQuery, businesses can break down data silos, streamline data management processes, and reduce the time and effort required to derive valuable customer insights, ultimately improving operational efficiency and reducing costs.

Technical benefits

  1. Unified and Enriched Customer Data: Redpoint CDP's advanced data unification and enrichment capabilities consolidate customer data from various sources, creating a single, comprehensive view of each customer and enhancing data quality for accurate analysis and targeting.

  2. Scalable and High-Performance Data Warehousing: Google's cloud-based architecture and near-infinite scalability enable organizations to store, manage, and process massive volumes of customer data efficiently, ensuring optimal performance and the ability to handle growing data needs.

  3. Real-Time Data Processing and Analytics: Google's real-time data processing capabilities, combined with Redpoint CDP's data streaming features, allow organizations to analyze customer data and derive actionable insights in real-time, enabling faster decision-making and more responsive customer engagement.

  4. Seamless Data Integration and Connectivity: The integration between Redpoint CDP and BigQuery is streamlined, leveraging Google's Cloud Storage as well as ODBC and JDBC drivers for efficient querying and data transfer. This simplifies the process of connecting the two platforms and reduces the complexity of managing data pipelines, saving time and resources.

  5. Robust Data Security and Compliance: Both Redpoint CDP and BigQuery prioritize data security and privacy, offering advanced security features such as encryption, access controls, and data governance tools to protect sensitive customer data and ensure compliance with global data privacy regulations.

  6. Collaborative Data Sharing and Accessibility: Google's secure data sharing capabilities allow organizations to easily share customer data and insights across departments and with external partners.

  7. Simplified Data Governance and Lineage: The combined solution simplifies data governance by providing tools for data lineage tracking, data cataloging, and metadata management. This ensures that data is consistently and accurately defined, making it easier to maintain data quality, comply with regulations, and track data usage across the organization.

Overall architectural considerations

  1. Google BigQuery pricing is based on several factors, including storage, queries, data ingestion, and additional features like streaming inserts and machine learning.

  2. Google BigQuery scales efficiently and seamlessly to handle large-scale data processing and analytics. The scalability of BigQuery is one of its core strengths, enabling it to process petabytes of data with high performance and reliability.

  3. BigQuery automatically handles scaling based on the workload. Given this, you need to consider optimization strategies to get the most out of BigQuery with the least amount of cost.

  4. For customers with predictable and high query volumes, BigQuery offers slot reservations. This allows users to purchase dedicated slots (compute resources) to guarantee performance for their workloads. Slots can be flexibly managed and reassigned to different projects or workloads as needed.

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