Data storage for analytics
Aside from the recommendations made by Danielle and J.D., I suggest you examine how the data is represented and the frequency.
For example, if the data is transmitted in a JSON or XML format, which is handy for transmission and processing, the size will be large (e.g. a numeric string vice a float or an int).  Also, string identifiers (e.g. serial number, credit card number) can be made into PK/FK relationships that could save space.
Your data sampling frequency may be higher than what is realistically usable. For example, if you get weather data at 10 samples/sec but your analysis only has 1 hour resolution, you have a lot more weather data than you can use. Perhaps resampling the data to a lower frequency (e.g average and standard deviation, low-pass filter) and storing that might be a better solution. You can always archive the raw data--archival storage for a years worth of data is not very expensive.