How to use Snowplow’s source-available collector in a real-time data stack?

Implementing Snowplow's collector in a real-time data stack enables comprehensive behavioral data collection with immediate processing capabilities.

Installation and configuration:

  • Set up the Snowplow collector to receive events from web, mobile, and server-side sources
  • Configure the collector for real-time data processing with minimal latency
  • Implement proper authentication, security, and data validation at the collection layer

Stream processing integration:

  • Use Kafka to stream collected data into downstream processing tools like Apache Flink or Spark
  • Implement real-time enrichment and validation as data flows through the pipeline
  • Configure parallel processing for high-throughput event handling

Storage and analytics:

  • Process and enrich data using tools like dbt before storing in your data warehouse
  • Support multiple storage destinations including Snowflake, BigQuery, and ClickHouse
  • Use tools like Flink or Kafka Streams for real-time analytics and event-driven use cases

Learn How Builders Are Shaping the Future with Snowplow

From success stories and architecture deep dives to live events and AI trends — explore resources to help you design smarter data products and stay ahead of what’s next.

Browse our Latest Blog Posts
Webinar

The CDP Market Is Evolving—Are You Asking the Right Questions?

Video

CDO Magazine Interview with Kalyani Sekar

Webinar

Reimagining Data Governance for Faster Business Outcomes

Video

Snowplow & AWS Case Study: Secret Escapes

Speaking Session

Entity-First Driven Design for Event Data Modeling

Get Started

Whether you’re modernizing your customer data infrastructure or building AI-powered applications, Snowplow helps eliminate engineering complexity so you can focus on delivering smarter customer experiences.

Book a DemoWatch our 10-min Demo