Apache Kafka Explained: A Clear Mental Model for Event-Driven Systems

TL;DR

• Kafka feels complicated until you stop thinking in APIs and start thinking in data flow.
• Kafka is a distributed event log that sits at the center of your system.
  • Applications publish events using Producers.
  • Existing databases stream data in using Kafka Connect Source.
  • Events are processed in real time using Kafka Streams or ksqlDB.
  • Multiple services consume the same data independently using Consumer Groups.
  • Processed data flows out to databases, search engines, or analytics systems via Kafka Connect Sink.
• You don't need every Kafka API on day one. You only need the ones your problem demands.
• Once you understand why each API exists and how data flows through Kafka, the rest security, monitoring, tuning becomes easier to reason about.
• Kafka isn't about moving messages. It's about designing systems that can evolve without breaking.

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Lately, I've been diving deeper into backend engineering and system design, trying to understand not just how systems work, but why they are designed the way they are.

As part of that journey, Apache Kafka kept appearing as a core building block in modern, real-time architectures. But what stood out to me wasn't Kafka itself it was the set of APIs Kafka provides, each solving a very specific kind of data movement and processing problem.

This blog focuses on Kafka APIs and when to use each one. Instead of trying to cover everything Kafka offers, we'll look at a practical question engineers often ask:

"Which Kafka API should I use for my use case?"

We'll explore scenarios like:

• Moving data from an existing database into Kafka using Kafka Connect
• Publishing real-time events from applications using the Kafka Producer API
• Consuming and reacting to events with the Kafka Consumer API
• Performing transformations, aggregations, and stream processing using Kafka Streams and ksqlDB

The goal here is not to explain Kafka feature-by-feature, but to build a clear mental model of how these APIs fit together and how to choose the right one based on the problem you're solving.

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Why Apache Kafka Exists

As systems grow, one problem shows up again and again: data needs to move fast, reliably, and to many places at once.

Traditional architectures struggle here. Databases are great at storing state, but they aren't designed to continuously broadcast changes. APIs work well for request–response interactions, but they break down when multiple systems need the same data in real time. Polling becomes expensive, tightly coupled integrations become fragile, and scaling turns into a coordination problem. What starts as a simple data flow quickly becomes a web of point-to-point connections.

This is the class of problems Kafka was built to solve.

Kafka introduces a different way of thinking about data not as requests or rows, but as events. Instead of asking systems to call each other directly, Kafka lets systems publish facts about what happened, while other systems consume those facts independently, at their own pace.

At its core, Kafka acts as a durable, distributed event log:

• Producers write events once
• Kafka stores them reliably and in order
• Multiple consumers read the same events without interfering with each other

This decoupling is what enables scale. Systems no longer need to know who is consuming their data, how fast they consume it, or even if they are online at the same time. Kafka sits in the middle, absorbing spikes, preserving history, and allowing real-time systems to evolve independently.

In short, Kafka doesn't replace databases or APIs it complements them by solving event distribution at scale.

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Kafka's Core Abstraction: Events, Logs, and Ordering

To understand Kafka, it helps to forget queues, APIs, and frameworks for a moment and think in terms of logs.

At the heart of Kafka is a simple idea: everything is an event, and events are never changed only appended.

An event is just a fact about something that happened:

• An order was created
• A payment was processed
• A user logged in
• A database row was updated

Kafka stores these events inside topics. A topic is not a table and not a queue. It's best thought of as a named, append-only log of events.

Partitions: How Kafka Scales

Each topic is split into partitions. Partitions are where Kafka's scalability comes from. Instead of one long log, Kafka maintains multiple logs in parallel. Each partition:

• Is ordered
• Is written sequentially
• Can be read independently

This allows Kafka to scale horizontally multiple producers can write to different partitions, and multiple consumers can read in parallel.

The key rule to remember: Ordering in Kafka is guaranteed per partition, not globally.

This design trade-off is intentional. It gives Kafka high throughput while still preserving meaningful order where it matters.

Offsets: Kafka's Memory

Within a partition, every event gets an offset. An offset is simply a monotonically increasing number that represents an event's position in the log. Kafka does not track "which messages are consumed" consumers do.

This is a crucial shift in thinking:

• Kafka stores events
• Consumers store their own position (offset)

Because of this:

• Consumers can replay events
• Multiple consumers can read the same data
• Systems can recover by reprocessing history

Kafka doesn't push messages. Consumers pull events and decide how fast to move forward.

Why This Model Matters

This log-based design is what enables all Kafka APIs to exist:

• Producers append events
• Consumers read events
• Streams process events in motion
• Connect moves events between systems

Once you see Kafka as a distributed, ordered event log, the rest of the ecosystem stops feeling complex it starts feeling composable.

Now that the core model is clear, the next logical question is: Who writes to this log, who reads from it, and how does Kafka coordinate this at scale?

That's where Producers, Consumers, and Consumer Groups come in.

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Kafka Producers and Consumers: Writing and Reading Events at Scale

Once you understand Kafka as a distributed event log, the roles of producers and consumers become straightforward.

Kafka Producers: Writing Events

A producer is any application that publishes events to Kafka. Producers don't send messages to consumers. They write events to a topic, and Kafka takes responsibility from there.

What makes producers powerful is how little they need to know:

• They don't know who will consume the data
• They don't know how many consumers exist
• They don't care whether consumers are online right now

They simply emit events facts about what happened.

Kafka handles:

• Partition assignment
• Ordering within partitions
• Durability through replication

This makes producers lightweight and easy to scale. You can add more producers without redesigning downstream systems.

Kafka Consumers: Reading Events

A consumer reads events from Kafka topics. But unlike traditional messaging systems, Kafka does not track which events are "consumed". Each consumer keeps track of its own offset its position in the log.

This design enables powerful behaviors:

• Consumers can replay past events
• Multiple consumers can read the same data independently
• Failures don't cause data loss processing can resume

Consumers pull data at their own pace. Kafka never pushes events onto them.

Consumer Groups: Horizontal Scaling Done Right

Kafka scales consumers using consumer groups. A consumer group is a logical group of consumers that work together to process a topic.

Key idea:

• Each partition is read by only one consumer within a group
• Different groups can read the same topic independently

This gives you two forms of scalability:

1. Parallelism within a service (multiple consumers in one group)
2. Fan-out across services (multiple groups consuming the same data)

For example:

• One consumer group processes orders for billing
• Another processes the same orders for analytics
• A third handles notifications

All from the same Kafka topic.

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Where APIs Start to Diverge

At this point, Kafka gives you two fundamental capabilities:

1. Producers write events
2. Consumers read events

But real systems need more than just reading and writing. Sometimes:

• Your data already lives in a database
• You need to transform or aggregate streams
• You want SQL instead of code
• You want to move data into search or analytics systems

This is where Kafka's APIs begin to specialize.

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Kafka APIs: Choosing the Right Tool for the Job

Once you understand Kafka's event log and the producer–consumer model, the next challenge is practical: How do I get data into Kafka, process it, and move it out without building everything from scratch?

This is where Kafka's APIs come in. Each API exists to solve a specific class of problems. Choosing the right one simplifies your architecture; choosing the wrong one adds unnecessary complexity.

Let's walk through them one by one.

1. Kafka Producer & Consumer APIs

For custom event-driven applications

This is the lowest-level and most flexible way to interact with Kafka.

When to use:

• Your application generates events (user actions, system events, logs)
• You want full control over publishing and consuming logic
• You are building custom services

How it fits:

• Producers publish events to Kafka topics
• Consumers read events and react to them
• Consumer groups allow horizontal scaling

This API is ideal when Kafka is part of your core application logic.

2. Kafka Connect (Source & Sink)

For moving data between Kafka and external systems

Kafka Connect exists to solve a very common problem: "My data already exists somewhere else." Instead of writing and maintaining custom ingestion code, Kafka Connect provides a framework and ecosystem of connectors.

• Kafka Connect Source moves data into Kafka:

  • Databases (CDC)
  • Filesystems
  • SaaS platforms
  • Message systems

• Kafka Connect Sink moves data out of Kafka:

  • Databases
  • Search engines
  • Data warehouses
  • Cloud storage

When to use:

• Data already lives outside Kafka
• You want reliability, retries, and scalability
• You want minimal custom code

Kafka Connect turns Kafka into a data integration backbone.

3. Kafka Streams

For real-time processing and transformations

Kafka Streams is a library for building stream processing applications directly on top of Kafka. It allows you to:

• Filter, map, and transform streams
• Join multiple streams
• Perform aggregations and windowed computations
• Maintain local state

When to use:

• You need real-time transformations
• You want processing logic close to the data
• You prefer application-level control

Kafka Streams applications:

• Consume from topics
• Process data
• Write results back to Kafka

All while leveraging Kafka's fault tolerance and scalability.

4. ksqlDB (KSQL)

For stream processing using SQL

ksqlDB builds on top of Kafka Streams but exposes it through SQL-like queries. Instead of writing code, you define:

• Streams
• Tables
• Continuous queries

When to use:

• You want fast development
• You prefer SQL over Java/Scala
• You need real-time analytics or transformations

ksqlDB is especially useful for:

• Exploratory data processing
• Lightweight transformations
• Streaming dashboards

It lowers the barrier to entry for stream processing.

5. Schema Registry

For managing data contracts

As Kafka systems grow, data compatibility becomes critical. Schema Registry provides:

• Centralized schema management
• Versioning and evolution rules
• Backward and forward compatibility

When to use:

• Multiple producers and consumers
• Strong data contracts
• Long-lived event streams

It prevents breaking changes and makes event-driven systems safer to evolve.

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How These APIs Work Together (Putting It All Together)

So, how does all of this actually work together?

Instead of explaining everything again in words, let’s look at the diagram above.

At a glance, you can already see the flow.

Kafka sits at the center, and every API around it plays a specific role in moving, processing, or consuming data.

Now let’s walk through this step by step in a simple way.

Step 1: Bringing Data into Kafka
In many real-world systems, data already exists somewhere else most commonly in databases like PostgreSQL, MySQL, or Cassandra. Instead of writing custom ingestion code, Kafka Connect Source is used here.

• It continuously reads data from the source database
• Converts changes into events
• Pushes them into Kafka topics

At this point, Kafka becomes the single source of truth for events.

Step 2: Publishing Application Events
Not all data comes from databases. Applications like mobile apps, backend services, and microservices produce events directly. This is where the Kafka Producer API is used.

• Applications publish events to Kafka topics
• Kafka handles durability, ordering (per partition), and scalability

The producer doesn't care who consumes the data. It only publishes facts.

Step 3: Processing and Transforming Data
Once data is inside Kafka, we often want to filter events, aggregate data, enrich streams, or join multiple event sources. This is handled by Kafka Streams or ksqlDB.

• Kafka Streams is used when you want full control using code
• ksqlDB is used when you prefer SQL-based stream processing

Both read from Kafka topics, process data in real time, and write results back to Kafka.

Step 4: Consuming Processed Events
Now that data is processed, different systems may need it for different purposes. This is where the Kafka Consumer API comes in.

• Consumers read events from topics
• Consumer groups allow horizontal scaling
• Multiple services can consume the same data independently

Each consumer decides how fast to read and how to react.

Step 5: Moving Data Out of Kafka
Finally, processed data often needs to be stored or indexed elsewhere for example, writing results back to a database, sending data to a search engine, or pushing data to analytics systems. This is handled by Kafka Connect Sink.

• It reads from Kafka topics
• Writes data to target systems reliably

Again, no custom glue code required.

Key Takeaway

Kafka's real strength doesn't come from any single API. It comes from how composable these APIs are.

Each API:

• Solves one specific problem
• Integrates cleanly with the others
• Keeps systems decoupled and scalable

Once you understand why each API exists, choosing the right one becomes a design decision, not a guessing game.

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Final Thoughts: Kafka Is Boring And That's the Point

At first glance, Kafka can feel overwhelming. Too many APIs. Too many diagrams. Too many opinions on the right way to use it.

But once the mental model clicks, something interesting happens. Kafka stops feeling like a complex system and starts feeling like a quiet, reliable middle layer that just does its job.

• Producers publish events.
• Consumers react.
• Streams transform.
• Connect moves data in and out.
• No drama.

And that's exactly why Kafka works so well.

Kafka doesn't try to be clever. It doesn't care who consumes the data. It doesn't ask you to redesign your system every time something new shows up. It just records what happened and lets the rest of the system figure it out.

If there's one mistake people make with Kafka, it's trying to use everything at once. You don't need Streams, ksqlDB, Connect, and five consumer groups on day one. Most systems start simple and evolve naturally as requirements grow.

And yes, there's still a lot more to Kafka than what we covered here. Security. Monitoring. Configurations. Performance tuning. Operational trade-offs.

All of that matters. But without a clear mental model of how data flows through Kafka, those topics feel scattered and overwhelming. With this flow in mind, everything else starts to fall into place.

So if you're new to Kafka, don't aim for perfection. Aim for clarity. Understand why each API exists. Use only what your problem demands. Let the architecture grow over time.

Because in the end, Kafka isn't about moving messages. It's about designing systems that can change without breaking and that's a skill that matters far beyond Kafka itself.

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