I am on a project dealing with a lot of data in the form of images and videos (Data related to wind engineering). My requirement is to build a predictive algorithm based on the data I have. I have found many tools with which I can analyse the data where each tool has its own advantages and disadvantages. Big data being really new to me, I find it very difficult to choose a platform to start with. There should be other people here who should have dealt with similar situations.
- What criteria should I mainly take into account before selecting a tool for analysing big data?
Some of the Criteria that I have taken into account : Visualization, Interaction, Security, Data Access and integration, Speed of response, Integrated Data Mining, Pattern Matching, Ease of use etc. As you can see the list that I have made for the criteria comes from the extensive reading of different articles on the topic. But I can't narrow down the list nor find the individual contribution of these criteria in the various tools available for analysis.
Let me also list some of the tools that I found after googling : Knime, Statistica 2, Rapidminer, Orange, WEKA, KEEL, R and RATTLE.
On what basis could I choose a tool from a list of tools that perform similar tasks?
UPDATE based on Comment
Aim : To develop a software that analyses the data coming from the wind mills and generate reports. The software should be able to predict when a wind mill can fail based on the analysis.
The project is still in the phase of gathering User Requirements. Maybe i am so early to come into conclusions about what tool should be used.
Someone else suggested that I should be finalise the requirements and then think about a tool that can help me get things done. So is it possible that I find what and how things should be analysed before finding a tool? And is it also possible that I find an algorithm for predictive analysis without knowing what would be the results of the tool after analysis.