QlikView Scalability Issues

Felipe Carrera
Solutions Manager Business Intelligence at a marketing services firm with 51-200 employees
Data model, data size, RAM size, hardware CPUs and UI expressions / charts, are strongly related. It is possible to scale QV without problems, but the issue is more complex than not having enough RAM. Complex expressions at the UI level, single threaded object calculations, complex data models, huge data size, etc., can alter the performance of the QV application and thus we might think we need to scale. There are many tools to measure QV performance and to try keep them at an optimal level. Otherwise, if everything else is fine, we need to increase RAM. Actually, we have 1 TB RAM on our production server. View full review »
Vyacheslav Koval
Board of Management with 501-1,000 employees
Enterprise Architecture & Business Analyst - Analytics at a paper AND forest products with 1,001-5,000 employees
Right now, we are facing a challenge concerning the capacity, or balancing the use of the servers. In the end, we expect to have a good resolution. View full review »
Bernard Barnard
Head of Qlikview IT at a financial services firm with 1,001-5,000 employees
Scalability depends on the environment and requirements. If you have large data sets, due to the in-memory nature of QlikView, the hardware needs to support your requirements. View full review »
Qlik Technical Consultant at a tech services company with 11-50 employees
QlikView is a memory intensive application and does sometimes start to weigh on your PC resources. Also you would need to learn some development tricks to manage unnecessary time wasting when loading large database sources, like the use of QVD – QlikView proprietary data format - for faster and efficient loading times. View full review »
Business Intelligence Director at a tech services company with 51-200 employees
I have not encountered any scalability issues. View full review »
Consultant at a tech services company
Scalability is directly proportional to RAM size & the number of concurrent users accessing the solutions. We didn't see major stability-related issues post-deployment. View full review »
BI Project Leader with 1,001-5,000 employees
I have encountered issues with scalability. We have generated models with large amounts of data and have found that the processor and memory of our server has been declining. We do not have something to help us to size the equipment requirements when a big data model is developed in QlikView. View full review »
Jop Moekotte
Business Analyst at a manufacturing company with 1,001-5,000 employees
Due to in memory technology, when datasets get really huge, rendering of graphs can take some time or even return out of memory. View full review »
Alexis Hadjisoteriou
Partner with 51-200 employees
Owner at a tech services company with 51-200 employees
In general, the solution is a little problematic in terms of scalability, as everything is stored in RAM. For more complicated analysis for many users, the memory consumption is very high and requires a lot of investment in hardware. View full review »
Patricio Honore, M.S.
Analyst - Business Applications at a pharma/biotech company with 10,001+ employees
We have not encountered any scalability issues. View full review »
Assistant Manager at a tech vendor with 10,001+ employees
Senior IT Business Partner at a manufacturing company with 501-1,000 employees
Only due to the costs. View full review »
Master Data at a hospitality company with 10,001+ employees
I don’t think scalability is applicable to QlikView. View full review »

Sign Up with Email