From Blueprint to Commercial Outcomes: Designing a Real-Time Personalization Solution with Snowplow, Flink, and Evoura
Building on Real-Time Blueprints for Personalization
At Snowplow, we understand that every organization’s data stack is unique. Whether it’s driven by team preferences, existing investments, or long-term architecture goals, the design of a modern data platform is rarely one-size-fits-all. That’s why Snowplow is designed to be composable by default, enabling teams to tailor their implementation based on how much they want to build, integrate, or buy.
To support this flexibility, we’ve developed a growing set of Blueprints and Solution Accelerators. Blueprints serve as high-level design patterns for key use cases, like real-time personalization, composable analytics, customer-facing AI agents or a composable CDP. Accelerators take things a step further by providing ready-to-run code and vendor-specific guides that help developers get hands-on with specific solutions.
These resources exist along a composability spectrum. Some teams want full control and prefer to assemble a bespoke stack using best-in-class open-source tools like Flink, Redis, and Kafka, with Snowplow providing clean, enriched behavioral data. Others prefer to move faster with more off-the-shelf components, such as Snowplow Signals, which packages many of these capabilities into a production-ready customer intelligence system.
We are delighted to launch our latest solution accelerator, Real-Time Shopper Features using Apache Flink. This new accelerator, built in collaboration with Evoura, brings our Real-Time Product Personalization Blueprint to life. It provides a hands-on example of how to stitch together streaming components to compute real-time shopper features and serve them with low latency.
Whether you’re building from the ground up or accelerating with packaged tools, Snowplow enables your team to meet your business where it is today and scale with confidence tomorrow.
From Behavior to Feature in Real Time
Today’s leading ecommerce experiences are shaped by real-time context. Whether it’s surfacing a smart product recommendation, adapting an interface based on scroll behavior, or scoring a risk event as it unfolds, behavioral intelligence needs to operate in the moment.
Talking to Data Science and Personalization teams at our Retail & Ecommerce customers, it is clear that they have successfully delivered on sophisticated machine learning models that make use of behavioral data ‘at rest’ in the data warehouse or lake. In this mode, features are calculated or ‘engineered’ off the historical event data and used to train offline models.
The next frontier is to be able to deliver on real-time use cases that depend on ‘fresh features’: essentially, the ability to calculate and update feature scores at very low latency for use in real-time inference. For many Data Science and Personalization teams, the software development lifecycle for machine learning with real-time use cases is unclear. We want to fix this with our Real-Time Product Personalization Blueprint.
In support of this, Snowplow has partnered with real-time data experts at Evoura to create a deployable solution accelerator for real-time shopper features. This accelerator reflects our increasing investment in Apache Flink, enabling the next-generation of complex low-latency stream processing and data modeling.
This accelerator also showcases our new Snowplow Local offering. With Snowplow Local, developers can launch and test the end-to-end solution from their local machine, giving them full visibility and control. This means developers can get hands-on with Snowplow’s technology from day one — without needing a commercial agreement in place — making it easy to explore, learn, and prove value before scaling up.
Introducing the Flink Real-Time Shopper Features Accelerator
This accelerator gives developers a ready-to-use reference implementation for real-time feature generation and personalization use cases.
Architecture Highlights
- Snowplow Local
A fast, portable developer environment for event collection, enrichment, and streaming built on Docker Compose. - Apache Kafka
Enables high-throughput ingestion of enriched Snowplow events into Flink. - Apache Flink
Processes behavioral events in real time and computes user or session-level aggregates. - Redis
Stores the resulting feature vectors for fast retrieval by downstream APIs and personalization engines.
Supporting Production-Ready Streaming with Ververica
We’re proud to acknowledge the important work of Ververica, the original creators of Apache Flink. Their commercial Ververica Platform makes it easier to deploy and manage Flink jobs in production, with built-in support for reliability, scalability, and observability.
Teams that start with this accelerator and want to take it to enterprise scale can confidently run Snowplow-powered Flink pipelines in Ververica-managed environments.
Develop with Speed and Confidence Using Snowplow Local
Traditional streaming architectures can be time-intensive to test and iterate. Snowplow Local simplifies this process, giving developers a local sandbox to simulate the entire stack without needing cloud infrastructure.
Why Developers Love Deploying Accelerators with Snowplow Local
- Rapid Setup
Launch Kafka, Flink, Redis, and Snowplow services with a single Docker Compose file. - Safe Iteration
Test and debug trackers, enrichments, Flink jobs, and feature stores without risk to production systems. - Full Observability
Watch enriched events move through the pipeline, inspect real-time Redis state, and validate behavior in context.
Snowplow Local helps developers move quickly from prototype to production-grade streaming architecture.
What’s covered in the accelerator?
Here’s what you’ll cover in this step-by-step sequence and architecture:
- Snowplow trackers collect user behavior from web or mobile
- Events are enriched and streamed into Kafka via Event Forwarding (aka “Snowbridge”)
- Flink jobs transform those events into computed features
- Features are written into Redis for downstream consumption
Meet Evoura: A Strategic Partner in Streaming Use Cases
Evoura is a boutique data consultancy focused on helping teams unlock value from real-time architecture. With deep experience in Apache Flink, Redis, and event-driven design, Evoura played a key role in shaping this accelerator for both performance and extensibility.
"We’re thrilled to partner with Snowplow on this accelerator program. The combination of high-quality behavioral data and stream-native design unlocks incredible possibilities for data science and personalization teams. Together with Snowplow and Ververica, we’re helping digital brands move faster from idea to intelligent, real-time experiences."
Paul Kirby, CEO at Evoura
Engineering teams looking to expand this implementation or scale to production can work directly with Evoura for advisory, infrastructure, or deployment support.
A Composable Approach to Real-Time Personalization
No two personalization stacks are the same. That’s why Snowplow supports a composable model that adapts to your use case, infrastructure, and appetite for control.
You can use this accelerator to build a fully customizable real-time system using Kafka, Flink, and Redis. Or you can opt for Snowplow Signals, our turnkey solution for personalization and real-time decisioning built on top of the Snowplow pipeline.
Want to see more examples of stack configurations?
Explore the Real-Time Product Personalization Blueprints
Driving Commercial Outcomes for Your Business
Real-time personalization is not just a technical evolution but a commercial unlock. For ecommerce, product, and data teams, adopting this blueprint can help drive meaningful outcomes across user conversion, experimentation velocity, and operational efficiency.
Increased Conversion Through Intelligent Targeting
Respond to user intent at the right moment. By leveraging live behavioral signals, your team can deliver personalized recommendations, content, and offers precisely when they matter most, helping to increase clickthrough rates and cart conversions.
Reduced Time-to-Value for Personalization Use Cases
With a ready-to-deploy architecture and local development environment via Snowplow Local, teams move faster from concept to implementation. Cut experimentation cycles from months to days and accelerate time-to-impact.
Future-Proofed ML and AI Integration
This accelerator is built with modern machine learning best practices in mind. Generated features are ready for online inference and next-best-action systems, helping teams reduce data preparation overhead while powering more dynamic personalization strategies.
Lower Total Cost of Ownership Compared to SaaS Suites
Rather than relying on closed-box personalization tools, this blueprint enables teams to build on open standards and scale cost-effectively. Host in your own cloud, retain governance and observability, and avoid vendor lock-in.
Strategic Optionality with Expert Support
Whether your team is scaling a fully customized stack or migrating to a managed solution, Snowplow’s ecosystem gives you options. Partner with Snowplow, Ververica, and Evoura to design a path that fits your goals, infrastructure, and resources.
Get Started Today
GitHub Repository
Includes setup instructions, Flink jobs, Redis schema, and tracking examples
Full Documentation
Step-by-step walkthrough of architecture, implementation, and real-time use cases
Partner with Evoura Leverage hands-on expertise to scale your event streaming infrastructure from proof of concept to production