Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
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.
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.
If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used
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.
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
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
Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.
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.
