In today’s digital businesses, real-time decisions require real-time data. At Essid Solutions, we design and implement low-latency analytics platforms using Apache Kafka, Apache Flink, and ClickHouse to deliver streaming insights at scale.

🔧 Why Real-Time Analytics?

• Detect anomalies or fraud in real-time
• Optimize marketing campaigns instantly
• Power recommendation engines and personalization
• Monitor IoT or sensor data with live dashboards

Batch ETL isn’t fast enough when milliseconds matter.

⚖️ Core Components of a Real-Time Stack

1. Data Ingestion – Apache Kafka, MQTT, WebSockets
2. Stream Processing – Apache Flink or Kafka Streams for windowing, joins, enrichment
3. Analytics Database – ClickHouse for ultra-fast OLAP queries
4. Visualization – Grafana, Redash, Superset
5. Monitoring & Alerting – Prometheus, Alertmanager

🌐 Architecture Overview

[ Data Sources: Apps, Sensors, APIs ]
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[ Kafka Topic Streams ]
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[ Flink Jobs: Enrichment, Filtering, Joins ]
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[ ClickHouse OLAP DB ] ---> [ Dashboards & Alerts ]

Optional: Use Kafka Connect to bridge sources like PostgreSQL or S3.

💼 Use Case: Fintech Fraud Detection System

A fintech company wanted to detect suspicious transactions in real-time. We:

• Ingested data via Kafka from APIs and mobile apps
• Built Flink jobs for rule-based fraud scoring
• Stored results in ClickHouse with sub-second queries
• Visualized alerts in Grafana

Result: Reduced fraud resolution time from hours to seconds.

📅 Build Your Real-Time Analytics Stack

We’ll help you launch scalable, event-driven pipelines for live business insights.

👉 Request a real-time architecture call
Or email: hi@essidsolutions.com