Elasticsearch is a really scalable solution that can fit a lot of needs, but the bigger and/or those needs become, the more understanding & infrastructure you will need for your instance to be running correctly. Elasticsearch is not problem-free - you can get yourself in a lot of trouble if you are not following good practices and/or if are not managing the cluster correctly. Licensing is a big decision point here as Elasticsearch is a middleware component - be sure to read the licensing agreement of the version you want to try before you commit to it. Same goes for long-term support - be sure to keep yourself in the know for this aspect you may end up stuck with an unpatched version for years.
I would only recommend IBM Security QRadar SIEM in a few situations. For one, it's very easy to setup and use if all your log sources are generic from known vendors. It's also significantly cheaper than Splunk, which is nice if you're trying to save money or be more efficient. I would not recommend IBM Security QRadar SIEM for environments with a lot of custom logs and complicated detection requirements.
As I mentioned before, Elasticsearch's flexible data model is unparalleled. You can nest fields as deeply as you want, have as many fields as you want, but whatever you want in those fields (as long as it stays the same type), and all of it will be searchable and you don't need to even declare a schema beforehand!
Elastic, the company behind Elasticsearch, is super strong financially and they have a great team of devs and product managers working on Elasticsearch. When I first started using ES 3 years ago, I was 90% impressed and knew it would be a good fit. 3 years later, I am 200% impressed and blown away by how far it has come and gotten even better. If there are features that are missing or you don't think it's fast enough right now, I bet it'll be suitable next year because the team behind it is so dang fast!
Elasticsearch is really, really stable. It takes a lot to bring down a cluster. It's self-balancing algorithms, leader-election system, self-healing properties are state of the art. We've never seen network failures or hard-drive corruption or CPU bugs bring down an ES cluster.
Need to spend more time configuring the system to properly interpret and normalize different type of data collected from multiple resources.
While Rule creation QRadar uses that rules to detect security threats and generate alerts, but to creating and managing rules is bit complex & tedious work to complete.
IBM Security QRadar SIEM is excellent in handling large & complex systems that requires in-depth knowledge and extensive training to configure and maintain the system which includes upgrading, optimization of performance & issue troubleshooting.
QRadar is an established and stable product, we have been using it for many years and want to continue to focus on it. Anyone who has used the product and knows it knows how reliable it is and how it facilitates continuous monitoring of threats from outside and inside. it is an exceptional product that is very useful for us.
To get started with Elasticsearch, you don't have to get very involved in configuring what really is an incredibly complex system under the hood. You simply install the package, run the service, and you're immediately able to begin using it. You don't need to learn any sort of query language to add data to Elasticsearch or perform some basic searching. If you're used to any sort of RESTful API, getting started with Elasticsearch is a breeze. If you've never interacted with a RESTful API directly, the journey may be a little more bumpy. Overall, though, it's incredibly simple to use for what it's doing under the covers.
As a grade I give 8 as QRadar is not easy to learn. It requires some time to master it. It also needs a team of people actively working on the product. Once you learn to use it the software works very well and it is easy to correlate and understand detected threats. It only takes time to learn how to use it well and configure it properly.
We've only used it as an opensource tooling. We did not purchase any additional support to roll out the elasticsearch software. When rolling out the application on our platform we've used the documentation which was available online. During our test phases we did not experience any bugs or issues so we did not rely on support at all.
Customer support is Good of IBM, While Using IBM QRadar its deployment is to slow and suddenly stop working and crashed we have contacted IBM Support and Rised a Ticket within a few minute we get call back from customer support and Query Resolved by them Fast And Rapid Support of Ibm
The training was very useful and the people who taught us were very knowledgeable. Although the software may initially seem difficult to learn they made things much easier for us.
The training was very useful and the people who taught us were very knowledgeable. Although the software may initially seem difficult to learn they made things much easier for us.
Initial patience is required to learn how to use the product, and it takes a dedicated team to use it. One person is not enough, and it's not enough to just set it up and check it once in a while. It has to be used daily and kept under control to be used effectively
As far as we are concerned, Elasticsearch is the gold standard and we have barely evaluated any alternatives. You could consider it an alternative to a relational or NoSQL database, so in cases where those suffice, you don't need Elasticsearch. But if you want powerful text-based search capabilities across large data sets, Elasticsearch is the way to go.
IBM Qradar takes the best from its competitors. Reliable and stable but sometimes very expensive, the SIEM from IBM offers a wide range of scenarios in which the customers can suite and size their own infrastructures. IBM Qradar doesn't really needs to stack up againt its competitors because it already sets an example in the SIEM world.
We have had great luck with implementing Elasticsearch for our search and analytics use cases.
While the operational burden is not minimal, operating a cluster of servers, using a custom query language, writing Elasticsearch-specific bulk insert code, the performance and the relative operational ease of Elasticsearch are unparalleled.
We've easily saved hundreds of thousands of dollars implementing Elasticsearch vs. RDBMS vs. other no-SQL solutions for our specific set of problems.
