Kafka and Real-Time Data Engineering

Real-time data engineering means processing events continuously instead of in scheduled batches. Kafka is the most common backbone; this guide connects the concepts from transport to processing to operations.

1. Kafka’s model

A topic is a partitioned, replicated, append-only log. Partitions provide ordering and parallelism; keys route related events to the same partition; consumer groups divide partitions among consumers.

Read: Topics, partitions and consumer groups · Practise: Partitions and consumer groups

2. Log or queue?

Logs retain events for replay by many consumers; queues distribute units of work. Most data pipelines want the log model.

Read: Kafka vs message queues

3. Delivery semantics

At-least-once is the practical default; make processing and sinks idempotent. Kafka’s exactly-once covers read-process-write within Kafka.

Practise: At-least-once delivery

4. Schemas and contracts

Use a schema registry with compatibility rules so producers cannot break consumers.

Read: Schema evolution patterns

5. Stream processing

Aggregate by event time, use watermarks for late data, deduplicate by event id, checkpoint state and offsets.

Read: Batch vs streaming · Case study: Real-time analytics pipeline

6. Change data capture

Stream database changes from the transaction log through Kafka and apply them with ordered, idempotent merges.

Read: CDC patterns and failure modes · Case study: CDC platform

7. Operations

Monitor consumer lag, under-replicated partitions and end-to-end latency; size partitions for peak throughput and consumer parallelism; set retention longer than your longest outage.

Read: Kafka ingestion system design

Build it

Start with the Kafka → Spark → Delta streaming project, then the real-time analytics project. Revise with the Kafka cheat sheet.