{'fraction_non_alphanumeric': 0.029411764705882353, 'fraction_numerical': 0.0, 'mean_word_length': 4.897637795275591, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Big data analytics on high Velocity streams: A case study

Big data management is often characterized by three Vs: Volume, Velocity and Variety. While traditional batch-oriented systems such as MapReduce are able to scale-out and process very large volumes of data in parallel, they also introduce some significant latency. In this paper, we focus on the second V (Velocity) of the Big Data triad; We present a case-study where we use a popular open-source stream processing engine (Storm) to perform real-time integration and trend detection on Twitter and Bitly streams. We describe our trend detection solution below and experimentally demonstrate that our architecture can effectively process data in real-time - even for high-velocity streams.