The Mirantis Kubernetes Engine (formerly Docker Enterprise, acquired by Mirantis in November 2019)aims to let users ship code faster. Mirantis Kubernetes Engine gives users one set of APIs and tools to deploy, manage, and observe secure-by-default, certified, batteries-included Kubernetes clusters on any infrastructure: public cloud, private cloud, or bare metal.
$0
per year
TIBCO ActiveSpaces
Score 7.0 out of 10
N/A
ActiveSpaces from TIBCO supports an application infrastructure.
N/A
Pricing
Mirantis Kubernetes Engine
TIBCO ActiveSpaces
Editions & Modules
Free
$0.00
per year
Basic
$500.00
per year
No answers on this topic
Offerings
Pricing Offerings
Mirantis Kubernetes Engine
TIBCO ActiveSpaces
Free Trial
Yes
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
These pricing options are compatible with Linux or Windows Server and are per year, per node. The basic version requires maximum online purchase not to exceed 50 nodes. Support/professional services are not included.
Docker is great for when you would want to use a VM for any given application, but don't need the overhead of the whole OS. Docker containers use very little computing resources, boot up very quickly, and are very easy to set up. An instance where Docker may not be appropriate would be for an application that requires good security. If in this situation, a true VM would probably be your best bet.
TIBCO ActiveSpaces is only suited in the company where Tibco suites have been already used, the license cost is not a concern, it is only required for data caching purposes and only works in the client/server mode for middle-sizes of data. If the expectation is data cache + distributed computation or embedded IMDG is also one requirement or the data cluster needs span multiple data centers, other light-weight OpenSource IMDG solutions should be considered
Docker has a bit of a learning curve, and it takes some time to become familiar with the tooling and syntax. Transitioning an existing architecture to docker can represent a significant investment.
Docker attempts to provide some level of cross-host container orchestration via swarm, but it falls short of third-party solutions like kubernetes.
We occasionally run into stability issues when the docker daemon is subjected to high load (many applications starting/stopping frequently). In these cases, docker hangs and we have to restart or replace the node.
TIBCO ActiveSpaces Tuple-based data structure is not flexible enough to support customized native data objects.
There is no secondary index option.
Doesn't provide "predicate based In-Grid distributed search and result aggregation."
only can be used as data cache, the node's only contribute memory but can not use the data partition owner node's computation power to apply the distributed computation customized by the user.
Cross data center replication (geographically) is a pain.
Encounter the performance issue when the data volume is huge, even according to the architecture design, linear scaling up should not have that issue.
Docker's CLI has a lot of options, and they aren't all intuitive. And there are so many tools in the space (Docker Compose, Docker Swarm, etc) that have their own configuration as well. So while there is a lot to learn, most concepts transfer easily and can be learned once and applied across everything.
TIBCO ActiveSpaces is easy to install and integrate with other product suites. It is easy to understand and implement as well. TIBCO ActiveSpaces supports multiple databases for storing the data(we are using Oracle Database). All the master data related to the users is being stored using TIBCO ActiveSpaces which keeps the data in memory and help to retrieve it quickly. It has helped to prevent concurrent login sessions by the same user as session details are stored in TIBCO ActiveSpaces and we override the existing user session with the new session details.
The community support for Docker is fantastic. There is almost always an answer for any issue I might encounter day-to-day, either on Stack Overflow, a helpful blog post, or the community Slack workspace. I've never come across a problem that I was unable to solve via some searching around in the community.
I have not used any other software as a container management solution. Its containerized apps allow the usage of less memory, thus they start and shut down very fast. This tool is helping the enterprise software to work quickly against the changing conditions thus offers great scaling by simultaneously allowing me to meet the demands, which also leads to easy implementation of the strategies.
Before using TIBCO ActiveSpaces, we were storing all the data in Oracle Database and due to large volumes of data response time was more and overall performance had reduced. With the introduction of TIBCO ActiveSpaces, we moved the master data to TIBCO ActiveSpaces for storing data which needed frequent access in memory for faster retrieval. This improved the performance significantly and also made managing data easier.
We are able to try things very quickly compared to before. If you need to debug it, changes on X/Y/Z will have an impact on the way your app works, and changing libraries or configurations of the environment easily can improve your development cycles.
In case someone new arrives, the onboarding is pretty easy thanks to Docker. We have tried many configs and images until we reached a point were we have what we want. We don't have to painfully do that again for every new user. We just send him the image.
Developers with basic knowledge of TIBCO and general data knowledge can easily design and develop an ActiveSpaces based cached solution. As the ActiveSpaces concepts are very simple and easy to understand.
Some business areas can predict the high influx of a service usage during a certain period. Business will be highly rewarded if they can identify these business areas and provide a cached solution using TIBCO AS.
Again, this is not a TIBCO ActiveSpaces only advantage and this is true for any/all caching products.
Some examples for the previous points are
a. telecom company pre-loading (eager load) customer's usage for the last month, right before releasing/issuing the bills to the customers.
b. Airline industry loading the customer's itinerary a week before his travel start date. Hence the last minute scrambling to fetch the customer's itinerary travel plans can be avoided.
