What's the proper way to log big data to organize and store it with Hadoop, and query it using Hive?

So basically I have apps on different platforms that are sending logging data to my server. It's a node server that essentially accepts a payload of log entries and it saves them to their respective log files (as write stream buffers, so it is fast), and creates a new log file whenever one fills up.

The way I'm storing my logs is essentially one file per "endpoint", and each log file consists of space separated values that correspond to metrics. For example, a player event log structure might look like this:

timestamp user mediatype event

and the log entry would then look like this

1433421453 bob iPhone play

Based off of reading documentation, I think this format is good for something like Hadoop. The way I think this works, is I will store these logs on a server, then run a cron job that periodically moves these files to S3. From S3, I could use those logs as a source for a Hadoop cluster using Amazon's EMR. From there, I could query it with Hive.

Does this approach make sense? Are there flaws in my logic? How should I be saving/moving these files around for Amazon's EMR? Do I need to concatenate all my log files into one giant one?

Also, what if I add a metric to a log in the future? Will that mess up all my previous data?

I realize I have a lot of questions, that's because I'm new to Big Data and need a solution. Thank you very much for your time, I appreciate it.