Download the exact captured file
---
name: realtime-streaming-event-driven
description: Specializes in Real-Time Streaming Architectures, Batch-Stream Unification, and Streaming Databases based on Streaming Databases (Hubert Dulay) and Building Real-Time Analytics Systems (Mark Needham). Covers Apache Kafka, Apache Pinot, Apache Flink, ClickHouse, Change Data Capture (CDC via Debezium), and real-time OLAP queries with sub-second latency.
---
# Real-Time Streaming Architectures and Streaming Databases
This skill establishes patterns for designing and operating systems oriented toward continuous event streams, unified batch-stream processing, and real-time OLAP analytics with millisecond-scale latencies.
---
## ⚡ 1. Lambda vs Kappa vs Unification in a Streaming DB
```
┌─────────────────────────────────────────────────────────────┐
│ Operational Sources (OLTP: Postgres, MySQL, APIs, IoT) │
└──────────────────────────────┬──────────────────────────────┘
│ CDC (Debezium) / Event Producer
┌──────────────────────────────▼──────────────────────────────┐
│ Distributed Event Log (Apache Kafka / Apache Pulsar) │
└──────────────────────────────┬──────────────────────────────┘
│
┌──────────────────┴──────────────────┐
│ │
┌───────────▼───────────┐ ┌───────────▼───────────┐
│ Stateful Processor │ │ Streaming OLAP DB │
│ (Apache Flink) │ │ (Apache Pinot / │
│ (Windows, Aggregation)│ │ ClickHouse) │
└───────────────────────┘ └───────────┬───────────┘
│
┌───────────▼───────────┐
│ Dashboards & APIs │
│ (< 50ms Query Latency)│
└───────────────────────┘
```
---
## 📊 2. Change Data Capture (CDC) Patterns with Debezium
- **Log-Based CDC**: Directly reads the database's transaction logs (WAL in PostgreSQL, Binlog in MySQL) without the overhead of polling `SELECT` queries.
- **Outbox Pattern**: Atomic write to the `outbox` table within the same relational database transaction to guarantee that domain events are never lost during network failures.