DataStax Enterprise (DSE) is the scale-out, cloud-native NoSQL database built on Apache Cassandra. DSE is Developer Ready providing developers the freedom of choice of REST, GraphQL, CQL and JSON/Document APIs.
There's really no reason to ever use Mesos. We switched over to Kubernetes and it's been a breath of fresh air - better CD support, easy CLI for browsing logs, no mysterious dangling redeploys. If you're looking for a tool to manage a fleet of Docker containers on VMs, Kubernetes beats Mesos by a wide margin.
Real-time transaction processing (both reads and writes) is where DataStax Enterprise shines. It's very fast with linear scalability should more resources be needed. Additional nodes are added very easily. DataStax Enterprise on its own (without Solr or Spark enabled) isn't well suited for long complicated reports. The data model doesn't support joining multiple tables together which is common in BI reporting.
Mesos may have many frameworks. If you have Mesos installed on your servers, you may use it for many kinds of tasks. Today we're running only web applications but the idea is to install a different framework for big data soon.
Datastax Cassandra provides high availability and good performance for a database. It is built on top of open source Apache Cassandra so you can always somewhat understand the internal functioning and why.
Datastax Cassandra is fairly simple to start using, you can install/setup your cluster and be productive in 1 day.
Datastax Cassandra provides a lot of good detailed documentation, and when starting, the detailed free videos on the Datastax site and documentation are very helpful.
Datastax Enterprise Edition of Cassandra provides more tools, good support, and quick response SLA for enterprise business support.
Unreliable deployments that would fail for no good reason. Sometimes our Docker container would be "restarting" forever because Mesos thought it didn't have enough resources to start the container.
Impossibly slow UI. Built in React under the hood with a lot of bloatware backed in, so loading the Mesos UI on a slow internet connection was painful.
No real logging solution - it would stream "console.log()" output to the UI, but searching for logs wasn't really possible without downloading a huge file.
No built-in support for redeploying containers from a CI. We had to create a service whose whole job was to expose an HTTP endpoint that restarted a container, and then made Circle CI ping the endpoint whenever we wanted to redeploy.
There is a bit of a learning curve and tasks that are simple in traditional RDBMS systems can be complicated with DataStax Enterprise but once you get the hang of denormalizing data and getting the data model correct DataStax Enterprise is very usable. Usability from the developer's standpoint is very simple - the complication is on the architecture side with the data model.
DataStax has the best community. They have instant customer support to solve problems and are knowledgeable of the problems faced by the customer. The documentation is pretty top-notch.
Kubernetes is really great and their community is growing really fast (Google influence). We evaluated it in the beginning and it would fit for our web applications workload. We decided to proceed with Mesos because it has more potential. You may use a different framework for different kinds of tasks on Mesos. There is a Kubernetes framework for Mesos, by the way.
DataStax Enterprise offered best-in-class write performance and scalability. The customer support team was very helpful in the adoption of new technology.
