We were running heavily on Amazon AWS, and I changed jobs shortly before the migration, but I think they moved to a variety of Amazon solutions (Kinesis, RedShi

We were running heavily on Amazon AWS, and I changed jobs shortly before the migration, but I think they moved to a variety of Amazon solutions (Kinesis, RedShift, etc). Not sure what they are running on now. To be honest, Hadoop on AWS through Elastic MapReduce (EMR) actually served us really well. The cluster downtime we had in our early days were all "human error" (configuration and Map/Reduce programming mistakes). Once we fixed those, EMR served us like a champ. The bigger issues we had were with HBase actually, particularly during its region splitting. It was great for storing massive amounts of data that you could store and query fast (with the right key design), but we weren't able find people skilled enough in HBase ops to make us comfortable with continuing to use it. I have seen Teradata, Netezza and Vertica being used heavily at that time (circa 2010-2013) but never worked on them directly. Like you said, they are extremely expensive, and I can imagine why some of the larger installations were trying to move to alternatives. End of the day, I don't have any perfect solutions to recommend to Hadoop or HBase. I think there are a variety of cloud-based solutions from Amazon, Microsoft and Google; as well as hybrid open-source solutions that do the job, but to each his own!