Apache Spark vs. IBM Security QRadar SIEM

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.6 out of 10
N/A
N/AN/A
IBM Security QRadar SIEM
Score 8.7 out of 10
N/A
IBM Security QRadar is security information and event management (SIEM) Software.N/A
Pricing
Apache SparkIBM Security QRadar SIEM
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkIBM Security QRadar SIEM
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache SparkIBM Security QRadar SIEM
Top Pros
Top Cons
Features
Apache SparkIBM Security QRadar SIEM
Security Information and Event Management (SIEM)
Comparison of Security Information and Event Management (SIEM) features of Product A and Product B
Apache Spark
-
Ratings
IBM Security QRadar SIEM
8.7
60 Ratings
11% above category average
Centralized event and log data collection00 Ratings9.927 Ratings
Correlation00 Ratings8.960 Ratings
Event and log normalization/management00 Ratings9.527 Ratings
Deployment flexibility00 Ratings7.927 Ratings
Integration with Identity and Access Management Tools00 Ratings8.456 Ratings
Custom dashboards and workspaces00 Ratings7.660 Ratings
Host and network-based intrusion detection00 Ratings9.625 Ratings
Data integration/API management00 Ratings9.07 Ratings
Behavioral analytics and baselining00 Ratings8.339 Ratings
Rules-based and algorithmic detection thresholds00 Ratings9.240 Ratings
Response orchestration and automation00 Ratings7.75 Ratings
Reporting and compliance management00 Ratings7.838 Ratings
Incident indexing/searching00 Ratings8.97 Ratings
Best Alternatives
Apache SparkIBM Security QRadar SIEM
Small Businesses

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Score 8.0 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Splunk Enterprise
Splunk Enterprise
Score 8.4 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 8.8 out of 10
Microsoft Sentinel
Microsoft Sentinel
Score 8.4 out of 10
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User Ratings
Apache SparkIBM Security QRadar SIEM
Likelihood to Recommend
9.9
(24 ratings)
8.7
(81 ratings)
Likelihood to Renew
10.0
(1 ratings)
9.1
(3 ratings)
Usability
10.0
(3 ratings)
9.1
(1 ratings)
Support Rating
8.7
(4 ratings)
8.6
(55 ratings)
Ease of integration
-
(0 ratings)
8.3
(51 ratings)
User Testimonials
Apache SparkIBM Security QRadar SIEM
Likelihood to Recommend
Apache
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.
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IBM
QRadar is very well suited on environments where there are not multiple tenants or domains, we do have success on this kind of scenario. IBM Security QRadar SIEM is less appropriate for environments with multiple tenants, specially when each tenant represent a different End Costumer (such as for MSSP companies), those environments require a high amount of rules and building blocks replications, since each tenant will have its own "BB definitions", servers, rules exception, etc. Also, some information, such as EPS count or EPS dropped are generated by QRadar's own log sources, which takes place on default domain, therefore users associated with different domain can not have access to those logs, even when the information is related to other domain's environment. For example, even if Event Collector 1 is associated to Domain A, the log informing its dropped EPS is generated by System notification, log source that must be associated to Default domain.
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Pros
Apache
  • Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.
  • Apache Spark does a fairly good job implementing machine learning models for larger data sets.
  • Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.
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IBM
  • Enables identification and prioritization of vulnerabilities in IT infrastructure for corrective action.
  • Facilitates security incident investigation and forensic analysis.
  • Provides a real-time view of security events, enabling immediate incident response.
  • Can integrate with external threat intelligence sources to enrich data and improve threat detection.
  • Enables the generation of detailed and customized reports.
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Cons
Apache
  • Memory management. Very weak on that.
  • PySpark not as robust as scala with spark.
  • spark master HA is needed. Not as HA as it should be.
  • Locality should not be a necessity, but does help improvement. But would prefer no locality
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IBM
  • 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.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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IBM
With the arrival of IBM Security QRadar SIEM at our company, we have a better vision of all the security needs that may arise, it is a very safe software to use that prevents threats from damaging our IT environment, it is impossible to change it for another software.
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Usability
Apache
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
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IBM
A very special system to use without problems, the process is very genuine and does not require complicated procedures.
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Support Rating
Apache
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.
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IBM
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
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Alternatives Considered
Apache
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python
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IBM
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.
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Return on Investment
Apache
  • Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
  • Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
  • Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
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IBM
  • Offense investigation was really helped in tackling the incidents. It was accurate and brief
  • The automation with IBM resilient (SOAR) was a milestone in elimination of user mistakes
  • The X-Force threat intelligence supported us in getting the work done without any 3rd party enterprise OSINT database
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