Hortonworks Data Platform (HDP) is an open source framework for distributed storage and processing of large, multi-source data sets. HDP modernizes IT infrastructure and keeps data secure—in the cloud or on-premises—while helping to drive new revenue streams, improve customer experience, and control costs.
Hortonworks merged with Cloudera in eary 2019.
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.
I find HDP easy to use and solves most of the problems for people looking to manage their big data. Evaluating the Hortonworks Data Platform is easy as it is free to download and install in your cluster. Single node cluster available as Sandbox is also easy for POCs.
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.
It does a good job of packaging a lot of big data components into bundles and lets you use the ones you are interested in or need. It supports an extensive list of components which lets us solve many problems.
It provides the ability to manage installations and maintenance using Apache Ambari. It helps us in using management packs to install/upgrade components easily. It also helps us add, remove components, add, remove hosts, perform upgrades in a convenient manner. It also provides alerts and notifications and monitors the environment.
What they excel in is packaging open source components that are relevant and are useful to solve and complement each other as well as contribute to enhancing those components. They do a great job in the community to keep on top of what would be useful to users, fixing bugs and working with other companies and individuals to make the platform better.
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.
Since it doesn't come with propriety tools for big data management, additional integration is need (for query handling, search, etc).
It was very straightforward to store clinical data without relations, such as data from sensors of a medical device. But it has limitations when needed to combine the data with other clinical data in structured format (e.g. lab results, diagnosis).
Overall look and feel of front-end management tools (e.g. monitoring) are not good. It is not bad but it doesn't look professional.
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.
We chose [Hortonworks Data Platform] because it's free and because [it] was an IBM partner, suggested as big data platform after biginsights platform.
You can install in more physical computer without high specs, then you can use it in order to learn how to deploy, configure a complete big data cluster.
We installed also in a cloud infrastructure of 5 virtual machine
