Thanks for the clarifications, I wasn't not meaning to discredit your article in anyway. I am just trying to help people understand that these databases are very different from eachother, and were created to solve different use cases.
Hadoop works great for "big data" (petabytes) but it was overkill for us (working with terabytes).
This is a very common misunderstanding about Hadoop, terabytes of data is still very large when you want to apply complex transformations, or allow someone to run queries over it with low latency expectations (a few seconds).
Impala does not use MapReduce, it was designed with low latency goals in mind...speeds are comparable to RedShift (https://amplab.cs.berkeley.edu/benchmark/), although they are citing an older version.
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Thanks for the clarifications, I wasn't not meaning to discredit your article in anyway. I am just trying to help people understand that these databases are very different from eachother, and were created to solve different use cases.
This is a very common misunderstanding about Hadoop, terabytes of data is still very large when you want to apply complex transformations, or allow someone to run queries over it with low latency expectations (a few seconds).
Impala does not use MapReduce, it was designed with low latency goals in mind...speeds are comparable to RedShift (https://amplab.cs.berkeley.edu/benchmark/), although they are citing an older version.
Interesting. I will definitely check out Impala. Thanks for bringing it up.