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COMPARISON OF IBM, GEPHI, TABLEAU, SIMBA, AZURE, LAMBDA, HADOOP, SPARK AND R IBM WATSON ANALYTICS: Watson Analytics is a smart data analysis and visualization service we can use to quickly discover patterns and meaning in our data – all on our own. With guided data discovery, automated predictive analytics and cognitive capabilities such as natural language dialogue, we can interact with data conversationally to get answers we understand. Whether we need to quickly spot a trend or we have a team that needs to visualize report data in a dashboard, Watson Analytics has we covered. Anything with textual data would be a perfect scenario for this product as the ultimate aim for it was to create an instance which would be able to process textual data just like the way humans might do it. It can definitely parse textual data really well. It has some great features associated with it for analyzing the textual data. We don't have to know real world coding ...

Challenges in using Hadoop vs spark •       Hadoop and Spark are Apache projects, they are Open source and free software products. And both especially designed to run on commodity hardware white box server system. Generally, cost wise both are cheap and equal. •       They are highly compatible with each other. By using IDBC and ODBC spark shares all MapReduce’s data sources and file formats. •       Spark 10 times more faster in batch processing and 100 times faster in memory analytics than MapReduce because MapReduce operates in steps i.e. read data from the cluster, perform an operation, write results to the cluster, read updated data from the cluster, perform next operation, write next results to the cluster, etc. but Spark does all data analytics operations in-memory and in near real-time i.e. Read data from the cluster, perform all of the requisite analytic operations, write results to the clust...