Challenges with high cardinality data
Since you have a dynamic data like you said, you can use neural net to identify and merge updating variables and data.
Also you should use classifiers like

CVParameterSelection : For cross validation parameter selection.
PART : For making a decision tree, great utility as it works on divide and conquer rule.
REP Tree (Pruned) : For reduced error in output by splitting error values

And finally when you have the systems in place, you can run the prediction model!
Background: I am working on classifying data from a ticketing system data into a failed or successful requests. A request goes into various stages before getting completed. Each request is assigned to different teams and individuals before being marked as complete. 
Making use of historical data I want to create predictions for these tickets at a final state x before they are marked as complete(success or fail).
Amongst the various features, individual's name who work on the records & team names are very important factors in analysing this data. Being a huge organization I expect 5-10 new names being added every day.
Historical data 
60k records (used for training, validation and testing)
Has 10k unique individual names 
Current data
Overal 1k records
- Has 200 individual names
I'm facing a challenge due to high cardinality data like individual names whose number is not fixed and keeps on growing.
1. Challenge while making actual predictions - The no. of columns for the current data will be different every time and would never match the feature length of training data. 
- So I have to train my model every single time, I want to make predictions.
2. Challenge while data prep -  The above also presents a challenge for data prep as now I always have to encode the complete data and the query encoded data to split into current and future data.
Sorry for the long story. 
What am I looking for?
Is there a better way to approach? 
These high & constantly changing dimensions is a pain. Any suggestions on how can I handle them, to avoid training every time?
Note: I tried using PCA and Autoencoders for dim red. (The results were not great for my highly unbalanced dataset so I'm working on the data with high dimensions only)