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 Initially built by LinkedIn, Apache Kafka is a "distributed event store" and "stream processing" platform.
 But what does that mean exactly?
 
+## Basics of Kafka
+
+The core principles of Kafka are very simple. There are three key components: producers, consumers, and the kafka cluster.
+
+### Producers
+
+Producers are responsible for creating new messages for your Kafka system. Every time an event occurs that the system
+should know about, a producer will generate a message describing this event.
+
+For example, a bookstore may have an application that _produces_ a message every time someone buys a new book:
+
+```json
+{
+  "action": "purchase",
+  "title": "The Metamorphosis",
+  "price": 9.99,
+  "buyerId": "84694fc7",
+  "storeNum": "FL-231",
+}
+```
+
+The rest of the system decides what exactly to _do_ with this new information. Those parts of the system are called
+**Consumers**.
+
+### Consumers
+
+Whenever a message is produced in the Kafka system, a consumer can act in response to that new information.
+
+Returning to the previous example: this particular bookstore may have a service that informs authors and publishers
+every time one of their books has been purchased. This service may work as a consumer. Then, every time a book is
+purchased, the service _consumes_ the new message, and alerts the creators however it sees fit.
+
+One of the main benefits of this structure is an inherent loose coupling. Producers do not care how consumers work. If a
+consumer wants to send a text, rent a billboard, or order an ice cream, the producer simply is not interested. This
+allows each part of the system to focus on its own piece of the domain, each staying smaller and more self-contained.
+
+### Kafka Cluster
+
+The Kafka cluster, in short is what facilitates the connection between producers and consumers. An indefinite number of
+producers and consumers can connect to the cluster, and the cluster itself can be powered by many servers.
+
+The cluster decides exactly which messages need to go where, and a key part of this process is its use of **Topics**.
+
+#### Topics
+
+"Topics" are Kafka's way of dividing up messages into different categories. A simple topic for our bookstore might be
+`book_purchases`, or `coffee_orders` (for the requisite café inside). So, when a book is sold, our producer would push a
+new message into the `book_purchases` topic. And of course, when someone orders a fancy latte or something hot and
+bitter, a message is instead put into the `coffee_orders` topic.
+
+Importantly, consumers do not blindly receive every message produced in the system. Instead, they subscribe to
+individual topics. Our author-notification service might subscribe just to the `book_purchases` topic, not being
+very interested in messages from `coffee_orders`.
+
+Thus, every time the Kafka cluster receives a newly-produced message with a given topic, it only forwards it along to
+consumers that have intentionally subscribed.
+
+## TODO: 
 ## Distributed Event Store
 
-## Stream Processing
+Essentially
+
+## Stream Processing