Early on, we wanted to build a data pipeline that extracted insights from Twitter by doing sentiment analysis of tweets containing specific hashtags and to deploy the results to a real-time dashboard. This application was a perfect starting point for us, because the data science analytics were not too complex, and the application covered many aspects of a real-life scenario:
High volume, high throughput streaming data
Data enrichment with sentiment analysis NLP
Basic data aggregation
Data visualization
Deployment into a real-time dashboard
To try things out, the first implementation was a simple Python application that used the tweepy library (the official Twitter library for Python: https://pypi.python.org/pypi/tweepy) to connect to Twitter and get a stream of tweets and textblob (the simple Python library for basic NLP: https://pypi.python.org/pypi/textblob) for sentiment analysis enrichment.
The results were then saved into a JSON file for analysis. This prototype was a great way to getting things started and experiment quickly, but after a few iterations we quickly realized that we needed to get serious and build an architecture that satisfied our enterprise requirements.