Real-Time Analytics Platforms Built for Production

The Challenge

Traditional data warehouses and batch ETL pipelines were designed for a world where daily or hourly data freshness was acceptable. Today, that's not good enough. Fraud happens in milliseconds. Customer behavior shifts in real time. Operational systems need to react to events instantly. Batch analytics platforms cannot keep up.

Building real-time analytics platforms from scratch is hard. The open-source ecosystem is rich but complex: getting Kafka, Apache Flink, and ClickHouse to work together reliably at scale requires deep expertise in distributed systems, stream processing semantics, and analytical database internals that most engineering teams don't have in-house.

Real-Time Analytics
Use Cases We Power

Real-time analytics platforms unlock a wide range of use cases across industries. Here are the most common ones we implement.

Operational Dashboards

Live business dashboards updated in real time from event streams. Engineering, ops, and business teams see the same fresh numbers the moment they change—no more waiting for overnight batch jobs to surface yesterday's metrics.

Fraud & Anomaly Detection

Stream processing pipelines with Apache Flink that evaluate complex event patterns in real time and trigger alerts or automated interventions within milliseconds of a suspicious event—before fraudulent transactions complete.

Clickstream Analysis

Ingest and analyze user behavior events at millions of events per second with Kafka. Aggregate sessions, compute funnels, and serve real-time personalization signals from ClickHouse with sub-second latency across your entire user base.

IoT & Sensor Data

High-throughput ingestion pipelines for IoT sensor data, device telemetry, and time-series metrics. Kafka handles millions of events per second; Flink processes and enriches streams; ClickHouse answers analytical queries in milliseconds over years of history.

Log Analytics

Real-time log ingestion, parsing, and analysis pipelines that give engineering teams instant visibility into application health, error rates, and performance degradations—without the cost of Splunk or the limitations of cloud-native logging services.

AI Feature Serving

Real-time feature pipelines that compute and serve ML model features with sub-second freshness. Streaming aggregations in Flink feed low-latency feature stores backed by ClickHouse, enabling real-time personalization, recommendations, and AI-driven decision making at scale.

Our Real-Time Analytics Stack

We build real-time analytics platforms on proven open-source components that scale from startup to enterprise without vendor lock-in or unpredictable licensing costs.

Apache Kafka — Ingest & Transport

Kafka serves as the durable, high-throughput backbone of the real-time analytics platform. It decouples producers from consumers, provides replay capability, and handles millions of events per second with horizontal scalability. We design and optimize Kafka clusters for throughput, latency, and operational simplicity.

Apache Flink — Stream Processing

Apache Flink processes and enriches event streams with exactly-once guarantees, stateful computations, and windowed aggregations. We implement Flink jobs for real-time ETL, complex event processing, anomaly detection, and ML feature computation—with the operational rigor required for production stream processing.

ClickHouse — Real-Time Analytics Serving

ClickHouse is the analytical database of choice for real-time analytics at scale. Its columnar storage, vectorized execution, and real-time insert capabilities make it ideal for dashboards, ad-hoc queries, and aggregations over billions of rows with sub-second response times. We design ClickHouse schemas, optimize table engines, and tune materialized views for your specific workload.

Why Choose BigData Boutique

Full-Stack Streaming Expertise

We hold deep expertise across the entire streaming analytics stack: Kafka internals, Flink stream processing semantics, and ClickHouse analytical database engineering. Most firms know one layer; we know all three and how they interact under production load.

Production-First Engineering

We don't deliver proof-of-concepts and leave. Every real-time analytics platform we build includes monitoring, alerting, runbooks, and operational handoff. We measure success by uptime and query latency in production, not demo day.

Vendor-Neutral Architecture

We build on open-source technology—Kafka, Flink, ClickHouse, Apache Iceberg—so you own your data and your platform without vendor lock-in or unpredictable per-query pricing. Cloud-native deployment on AWS, GCP, or Azure is fully supported.