“We work the way your mind works. It doesn’t matter if you get the thing perfect the first time. Let your mind go the way it wants and ask the questions that you want to ask. Your can customize [QlikView] based on the kinds of questions, the kinds of analysis, that [your users] want to do,” Deighton says.
While I have my head in the clouds, I should mention that Vertica has a cloud solution that they manage for you. Not new, but gives some perspective.
With competitive offerings in the $10-20k per terabyte, this is an attractive offer and a great way to try before you invest when you have that much data.
I hear Vertica is a screamer, but I can’t imagine getting sub-second results for 3 TB of data on 3 virtualized servers, for the same reasons I gave in my previous post.
QlikView depends entirely on processor speed, processor cache performance, memory latency and memory throughput. This makes QlikView an ideal reference for Intel, who uses QlikView to show off the latest product improvements. It also adds to the challenge of adapting QlikView to cloud platforms such as Amazon Web Services, Mosso, Joyent, etc.
The problem is virtualization. Virtualization is valuable to customers and service providers, but it’s also a thief! It adds overhead for the processor, cache and memory–everything that impacts QlikView performance!
The cloud, as in real life, is ever changing. You have no idea how many people are sharing your hardware and what their load will be from second to second. I would bet that nearly all deployed QlikView servers spend most of their time idle and the rest of their time at peak processing power. In the cloud, the goal is to spend as little time as possible idle for which we sacrifice peak processing power. QlikView depends on peak processing power and that type of application will suffer the most in the cloud.
But exactly how much will it suffer? Success in the cloud will need to be measured by the end-user experience. The cost of being in the cloud is vigilant monitoring and smart responses. What’s the right way to monitor the end-user experience in a company that uses OCX vs. AJAX, or is spread out geographically? Will bringing up more servers in the cloud improve response time? Should every QlikView server deliver the same set of apps, or should each app be served by a dynamic set of servers? Similarly, do some apps need sub-second response time while others can wait?
One thing stays the same. If you deploy large QlikView data sets you’re already sensitive to response times and what to consider when designing an app. In the cloud, smaller apps will need to think about costly chart expressions, messy data models and design choices that work fine on dedicated servers.