Real-Time Behavioural Analytics Platform Case Study

Real-Time User Behavioural Analytics Platform

Helped build a real-time user behavioural analytics platform using Kafka, Golang, Apache Flink, and QuestDB — so behavioural events could be ingested, processed, and queried with real-time characteristics rather than batch-only delays.

Client type

Product / analytics platform

Engagement

Platform build

Stack

Kafka · Go · Apache Flink · QuestDB

Themes

Real-time analytics · Behavioural events · Streaming · Time-series

Challenge

The challenge

User behavioural analytics only creates value when signals are fresh and queryable.

High-volume event streams from products and clients
Need for stream processing — not only nightly ETL
Low-latency storage and query for behavioural and time-oriented workloads
Services written for throughput and operability (Go) in the hot path

Approach

What BlackPotato did

Designed and built core pieces of a real-time behavioural analytics platform
Kafka as the durable event backbone
Golang services for high-performance ingest and platform components
Apache Flink for stream processing and behavioural aggregations / features
QuestDB for time-series–oriented analytics storage and fast queries
End-to-end path: events → process → store → real-time analytical access

Flow

Reference architecture

User / product events
Kafka
Go services ↔ Flink stream processing
QuestDB
Real-time behavioural analytics / consumers

Architecture

Stack roles

Technology

Role

Kafka

Event transport and buffering at scale

Golang

Performance-sensitive services in the platform

Flink

Real-time stream processing

QuestDB

Time-series analytics store for behavioural queries

Outcomes

Real-time path for user behavioural data
Clear separation of streaming transport, processing, and analytical store
Stack suited to high ingest and timely query behavioural use cases