Snowplow and Confluent
Seamlessly integrate high-fidelity behavioral data with enterprise-grade streaming to drive real-time operations and AI applications.
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Powering Real-Time Customer Behavioral Data
Streaming for Your Operational Estate
Real-time operational applications often lack the context-awareness needed to make informed decisions. Behavioral data is frequently siloed in the analytical estate—stored in warehouses and lakehouses—making it difficult to leverage in real-time operational systems where it is needed most. By integrating Snowplow's customer data infrastructure with Confluent’s enterprise-grade streaming, businesses can power their operational and analytical estates with the most accurate and timely behavioral data. This ensures AI applications receive continuously updated, high-quality, governed data, enabling real-time decision-making, automation, and adaptability across business functions.
Comprehensive SDKs for Real-Time Operations
Seamless Data Generation at Scale: Snowplow provides over 35 first-party trackers and SDKs, enabling businesses to collect real-time behavioral data from web, mobile, IoT, and server-side applications. This ensures a continuous flow of event-level data into the operational estate.
Integrated with Confluent for Enterprise-Grade Streaming: Event data collected via Snowplow seamlessly flows into Confluent Cloud and Apache Kafka, ensuring high-throughput, low-latency streaming for downstream operations and AI-driven applications.
Real-Time Enrichment and Stream Processing
Enriching Data for Smarter Decisions: Snowplow’s 15+ built-in enrichments enhance raw behavioral data with PII masking, geo lookups, and sessionization, before streaming into Confluent’s real-time processing engine.
Flink-Powered Stream Processing: With Confluent’s support for Apache Flink, businesses can execute complex transformations and aggregations on behavioral data streams, unlocking immediate insights for real-time personalization, fraud detection, and operational intelligence.
Flexible Deployment Models and Managed Streaming
Deploy Where You Need It: Snowplow offers full BYOC deployment, allowing businesses to run their behavioral data pipeline within their own VPC, maintaining strict compliance and security while integrating seamlessly with Confluent Cloud or self-managed Kafka.
Scalable, Managed Streaming with Confluent Cloud: For businesses seeking a fully managed streaming infrastructure, Confluent Cloud ensures enterprise-grade reliability, auto-scaling, and low-latency delivery of behavioral data into modern AI, analytics, and operational workflows.
Unified Data Pipeline
End-to-End Data Flow
Establish a seamless data pipeline from data collection with Snowplow to real-time streaming and processing with Confluent, ensuring data integrity and consistency.
Simplified Data Management
Reduce complexity in data architecture by integrating two powerful platforms, streamlining data operations and management of intelligent applications to address use cases such as fraud detection.
Advanced Data Enrichment & Governance
High-Fidelity Behavioral Data for AI
Enrich AI models with structured, contextual, and first-party behavioral data, ensuring more accurate decision-making and operations.
Schema Enforcement & Quality Assurance
Maintain data consistency, accuracy, and governance across streaming and analytical workflows, preventing AI failures caused by poor-quality data.
AI-Optimized Streaming & Processing Infrastructure
High-Fidelity Behavioral Data for AI
Confluent's event-driven architecture, powered by Kafka and Flink, ensures low-latency, high-throughput streaming, critical for AI applications requiring real-time responsiveness.
Schema Enforcement & Quality Assurance
Snowplow’s event-level behavioral data is optimized for both high-speed transactional needs and deeper AI model training, ensuring businesses can seamlessly integrate real-time automation alongside analytical insights.
“Tracking “in-view” elements greatly improves outcomes. What we found was that a lot of the cars that were included in the sort recommendations, may not actually be in the viewport of the end-user at the time they’re looking at car rental options. Incorporating this information in training data substantially improves the model’s predictions.”
Kyle Fitzpatrick, Insights Architecture Manager
CARTRAWLER
