My take was humorous but it didn’t hide anything. Kafka was built so that LinkedIn could shove all its real-time click data through a single funnel—terabytes upon terabytes. It has since been evangelized and created a cottage industry of Confluent salespeople who will give your manager a course in how to lobby their engineers into using Kafka. Have scaling problems? Kafka. Have business events that need to be ordered? Kafka! Have “changing schemas”? KAFKA!! I’m always suspicious when a company gives a product away for free tbut then charges $$$ for “support”.
I worked for a high profile recently-failed project from a company that rhymes with Brillo, and our data was just beginning to be too big for google sheets (!). However, we were also having organizational problems because the higher ups were seeing the failing project losing money so they of course decided to hire 100 extra engineers. Our communications (both human and programmatic) were failing and the confluent salespeople began circling like buzzards. Of course by the time it was suggested we we use it the project was already 6 months past the point of no return.
My advice is that if your data fits in a database, use a database. Anyone who says that isn’t scalable should have to tell you the actual reason it doesn’t scale and the number of requests/users/GBs/uptime/ etc that is the bottleneck.
To be very clear, Confluent doesn't give _their_ product away for free. Confluent Platform has many differing features to Apache Kafka that never make it into upstream.
E.g., Confluent Replicator vs. Mirror Maker 2, Confluent Platform's tiered storage has been available for quite some time (right now a bunch of people from AirBnB are doing a stellar job bringing tiered storage to FOSS Kafka, I'm hoping 3.1 or 3.2).
Actually, easiest thing to do to see the differences is grep all the subpages of this link for properties that start with "confluent":
I worked for a high profile recently-failed project from a company that rhymes with Brillo, and our data was just beginning to be too big for google sheets (!). However, we were also having organizational problems because the higher ups were seeing the failing project losing money so they of course decided to hire 100 extra engineers. Our communications (both human and programmatic) were failing and the confluent salespeople began circling like buzzards. Of course by the time it was suggested we we use it the project was already 6 months past the point of no return.
My advice is that if your data fits in a database, use a database. Anyone who says that isn’t scalable should have to tell you the actual reason it doesn’t scale and the number of requests/users/GBs/uptime/ etc that is the bottleneck.