Yeah, so this is really crucial to understand and there's a lot of misconceptions and misunderstandings here, right?
So, therefore one of the first things I always explain is that Kafka is not a message broker. But before that even, it's really, as you mentioned, it's important to point out the difference between data at rest, which in the end means that you store data on a disc or in a database and you keep it there, and then you do a query to that with a SQL query with a web service or whatever.
So that's good for reporting or for training analytic model. But with data in motion, you can continuously act on data. While it's ingesting and I typically recommend to really start not from a technical discussion, but always start from the business problem, if that helps. And if you ask the business, whatever the business is, it really doesn't matter.
The business will always tell you that realtime data beats slow data. So if you ask the business if it's better to use data now And act on it, whatever the action is an alert, a payment, even just a report. But now it's better than later, no matter if later seconds later, minutes later or days later, right?
And with that's the game changer of data in motion with the streaming platform. That's the high level difference between where you still use a data warehouse for reporting is perfect for that. You still use a big data platform for training models, right? But for many use cases, Acting now is more business value, which can be reducing risk, increasing revenue, making customers happy.
That's depending on the use case, but this is the difference of using data in motion while it's happening. And with that, now spoken about the technology, this is the big difference to a message queue or message broker. A message broker is only here to send data from A to B that's great, right? But that does not add the business value.
The business value is when you also use the data in real time, and that's not what you do with a message broker. And therefore I explain Apache Kafka is four different components. Number one is the messaging component. That's what everybody understands and what people are using. However, number two, and that's really already the thing where people most underestimate is the storage of Kafka, because with Kafka, you also decouple the systems very well. You put it into the Kafka log, and then every single consumer can consume at its own pace because reality is that yes, realtime data beat slow data, but most systems so they are not realtime and some will never be real time.
And so you get data into Kafka once from a realtime messaging system, from a web service request response or from a batch workload. And then you have it in Kafka once, and then everybody can consume it one real time, one near real time, one request response, and one batch. They all decoupled because of the storage of Kafka.
And this is the biggest game changer compared to a message queue so that you really cannot just do messaging in real time, but also provide Data consistency across different systems because most systems are not realtime, and this is the biggest value of Kafka, right? Often even more important than the realtime capability.
And in addition to the messaging and storage combination, which is the core of Kafka, you even have Kafka Connect for data integration, and you have Kafka streams or KSQL for stream processing for correlating the data. And while you are absolutely right that most of our customers or end users with open source start with the billing ingestion pipeline, even there, even if you don't do stream processing, which is more advanced, but even in the beginning, you should always use Kafka Connect for that, right?
Because it's part of the Kafka ecosystem for doing integration with databases, with other message queues, with data warehouses that's built into the platform and you don't need yet another ETL tool or cloud service for that because even the integration and processing capabilities are built on Kafka, scalable, reliable, real time decoupling, guaranteed ordering.
All that's built into one platform. And this is really what makes Kafka so unique in the market. With a message broker, you need to add another ETL tool with another code base and infrastructure, and you need to add another storage system and you need another correlation engine with a Kafka ecosystem, you get all of that in one platform end to end that makes operations, scalability, and support much-much easier. And this is really why Kafka is so successful in the market.