As marketing organizations become more data-savvy and customer expectations continue to evolve, brands are under pressure to activate personalized experiences across channels — faster and at scale. Together, Adobe Real-Time Customer Data Platform (Real-Time CDP) and Google Cloud’s BigQuery, a unified data to AI platform, offer a powerful way to bridge customer data and analytics environments, creating a unified marketing intelligence engine.

In this blog, we’ll explore what makes Adobe Real-Time CDP and Google BigQuery individually powerful, and how integrating these applications can unlock next-generation audience targeting and personalization use cases.

Overview of Adobe Real-Time CDP.

Adobe Real-Time CDP harmonizes data, technology, and teams. Purpose-built for marketers, it helps deliver relevant, real-time experiences by collecting and unifying customer data into real-time profiles that power personalization.

Key capabilities:

• Real-time customer profiles: Merge streaming and batch data into a single, real-time view of each customer.
• Flexible connectivity to enterprise systems: Prebuilt source and destination connectors to external systems, options to federate or ingest data from enterprise data warehouses, and native connectivity to Adobe applications provide a composable architecture that accelerates time to value.
• Purpose-built AI: Dedicated B2C and B2B models designed to support customer experience workflows and use cases, including generative and agentic AI tools that help teams understand, optimize, and act on their data.
• Data collaboration: Embedded, privacy-centric collaboration with second- and third-party data partners.
• Full-funnel marketing at scale: Use both known customer and unknown prospect data to build audiences and personalize experiences across the customer lifecycle.
• Privacy and governance: Built-in data governance and consent frameworks simplify compliance across regions.

The comprehensive capabilities of Adobe Real-Time CDP to power customer experience orchestration set it apart from other customer data platforms — offering marketers multiple opportunities to drive business value.

Overview of Google BigQuery.

Google BigQuery platform is a fully-managed, serverless data warehouse that enables ultra-fast SQL queries on large datasets. It's a favorite among data engineers and analysts for its scalability, low maintenance, and deep integration with Google Cloud’s ecosystem.

Key capabilities:

• Blazing-fast query performance: Analyze petabytes of data in seconds with distributed architecture in BigQuery.
• Serverless infrastructure: No need to manage clusters or worry about scaling — it auto-scales on demand.
• Built-in ML and BI integrations: Easily apply machine learning models or integrate with tools like Looker, Looker Studio, and Vertex AI.
• Security and compliance: Features like data encryption, IAM policies, and audit logging help meet enterprise-grade security and compliance requirements.

BigQuery excels at cost-efficient data processing, making it ideal for complex data modeling and large-scale customer analytics.

The value of integration.

Integrating Adobe Real-Time CDP and BigQuery allows customers to build on the work they’ve done to aggregate and organize data in BigQuery, powering low latency marketing workflows in Adobe Real-Time CDP. This accelerates time to market for marketing campaigns by making data readily accessible to the teams that need it.

This integration unlocks several high impact use cases for marketing teams:

Predictive audiences based on machine learning models.

Data scientists may already be scoring customers in BigQuery using ML models. Those scores can be further enriched and activated through Adobe Real-Time CDP to power personalization across channels.

Unified customer segmentation across systems.

Marketers can enrich behavioral data in Adobe Real-Time CDP (e.g., from websites or apps) with transactional or offline data from BigQuery (e.g., CRM or POS systems) for precise segmentation.

Real-time campaign suppression.

Leverage transaction history or churn prediction data in BigQuery to suppress specific users from email or ad campaigns activated through Adobe Real-Time CDP.

Localized or region-specific campaigns.

For global brands, regional teams often maintain separate BigQuery datasets. Adobe Real-Time CDP allows localized teams to build audience segments relevant to their markets while centralizing activation in a single tool.

Overview of integration patterns.

Unlocking these benefits is straightforward. Customers can set up a connection from Adobe Real-Time CDP to Google Big Query by choosing one or both of the following paths:

Data federation via Adobe Experience Platform Federated Audience Composition.

Federated Audience Composition allows customers to create and update Adobe Real-Time CDP audiences directly from Big Query, without any ingestion process. Rather than importing massive datasets into Adobe Experience Platform (which can be costly and time-consuming), Federated Audience Composition allows marketers and data teams to query BigQuery directly from Experience Platform and retrieve only the relevant audience segment for activation. This approach offers several benefits:

• Reduces data movement: Avoids redundant data ingestion by querying only what’s needed.
• Leverages existing investments: Utilizes existing data modeling and pipelines built in BigQuery.
• Accelerates time to activation: Shortens the time from audience planning to live campaign.

Bridges marketing and data teams: Enables marketers to use audiences created by data teams, without requiring them to rebuild segments from scratch.

Customers can accelerate time to value by quickly getting started with Federated Audience Composition:

• Set up a federated source connector in Adobe Real-Time CDP to connect with BigQuery.
• Use SQL-based, composable segmentation to query data stored in BigQuery.
• Persist only the resulting segment definitions (not the raw data) for the duration required by the use cases.

Ingest only the resulting audience IDs into Adobe Real-Time CDP for activation.

Direct dataset ingestion via prebuilt source connector.

Direct dataset ingestion offers an alternative path for integrating Adobe Real-Time CDP with BigQuery. This approach allows brands to copy raw data from BigQuery into Adobe Real-Time CDP for audience creation. It is particularly effective under the following conditions:

• Work with raw data: Ingesting raw data makes attributes and events available to Adobe Real-Time CDP for building granular audiences.
• Leverage purpose-built AI in Adobe Real-Time CDP: Running Real-Time CDP models (e.g., propensity scoring with Customer AI) on ingested data generates actionable insights to optimize audiences.
• Copy essential datasets: Critical datasets can be copied comprehensively into Adobe Real-Time CDP to power low-latency use cases.

The process to set up the BigQuery source connector in Adobe Real-Time CDP is straightforward:

• Add IP addresses to an allowlist, then connect BigQuery using Real-Time CDP APIs or the UI.
• Create a dataflow for BigQuery source data and schedule ingestion runs at a cadence aligned with use case requirements.
• Use the ingested data to create new audiences or update existing audiences that leverage that dataflow.

Conclusion

The Adobe Real-Time CDP and BigQuery integration is an important capability for organizations seeking to unify marketing agility with data scale. It brings together the best of both worlds: the marketer-friendly activation power of Adobe Real-Time CDP and the analytical depth of Google’s unified BigQuery platform.

In an age where data gravity is real, the ability to access critical datasets, query data where it lives, and activate it seamlessly is a strategic advantage. Whether you’re a marketer, data engineer, or architect, this integration is worth exploring to streamline your path to personalized customer experiences.

Contact your Adobe account team to learn more about the Adobe Real-Time CDP and BigQuery integration.

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