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Just curious, what did you migrate to? I think the comment "first wave of Big Data technologies" is interesting. I agree with it, but also, the term "big data
by sixdimensional 10y ago
Just curious, what did you migrate to?
I think the comment "first wave of Big Data technologies" is interesting. I agree with it, but also, the term "big data" is relevant to a point in time, I think. I mean, VLDB was the term before "big data" - I think when the term "big data" became popular was when technology such as Hadoop became more mainstream and available instead of just being used in special cases.
I was working with terabytes of data in SQL Server prior to 2008 (around 2004/2005 - which was doable but pushing some limits) and then got into Hadoop etc. afterwards. It was interesting to reach the limits of a tech we were using and be there for the transition to Hadoop etc.
There were expensive commercial alternatives - MPP databases like Teradata, Netezza or Vertica - or things like scientific computing platforms (which were very limited to scientific computing scenarios). But nothing was as.. mainstream and available as Hadoop became. And yet, Hadoop never really completely beat the traditional MPP vendors until you started getting engines like Impala, Presto, Spark etc. that made SQL on top of Hadoop easier. And that took quite a while.. in fact I'd say making SQL on top of Hadoop work well (among many other things) is probably still on-going in a way. We are much, much further along with that now than we were.
On the other hand, it was interesting to see the new opportunities develop for working with data other than just SQL - straight MapReduce, machine learning, graph processing, DAG, etc.
Will be interesting to see where we go from here. I still think there is a lot going on.
- samspenc 10y agoWe 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!