I'd very much recommend you first think about the nature of your problem, data, environment, transformation and storage needs and then start evaluating platform

I'd very much recommend you first think about the nature of your problem, data, environment, transformation and storage needs and then start evaluating platforms. Don't just take the latest buzz words and start hacking away. Do you need batch analysis or stream processing? How much data do you have? How will you need to combine stored data with streaming data? What data needs to be in memory vs disk? How far back in time does you analysis need to go? Can you pull that off in a streaming fashion or do you need a Lambda architecture? Are all your queries known ahead of time or ad-hoc? How will you access historical events? Do you really have data that requires 'big data' tools or can you get away with R on a powerful workstation? You don't give many details here so I can only through out questions which first need answers. I.e. in our environment we have the whole Cloudera stack, we use map reduce for some things, spark for others and storm for others. We discovered we don't need Kafka but can get away with just a message broker. We have HDFS, Hive, Couchbase, Oracle and a few other stores all with their own sweet spot.