Re: impala out of fashion?
You have a point, but you're wrong. I'd love to trade specific benchmarks with you, but the wonderful world of DeWitt obviously prevents that. So, I'll leave this with a couple of general points:
1) "Disk based vs memory based"
You're right that Impala is, in principal, an in-memory system. However, the point must be reinforced that in general, any given Hadoop system is ten times (or better) cheaper than the equivalent RDBMS appliance. When you can buy your boxes by the thousand from China at ridiculously low costs, it becomes trivial to establish memory pools on the order of Petabytes. This is a common use case for Impala in the enterprise, and it's one you will spend multiple tens of millions (dominated by license and consultancy costs) trying to match with Oracle or Teradata. Yes, you're limited* to the size of your memory pool, but an Impala memory pool is almost always going to be cheaper than Oracle RAC or Teradata.
*If you're doing truly insane workloads, there's nothing stopping you sacrificing your latency and going from milliseconds response to seconds/minutes by regressing to Hive (on MapReduce/Tez/Whatever) - same data, same hardware, same software packages, same code, all under the same license. In those cases it's not like you'd be getting sub-second responses under Oracle anyway. Frankly I've never encountered a customer with big enough BI workloads to run out of memory but not enough spare cash to just buy another terabyte or ten of RAM.
2) Join performance
Impala performs as well (usually better) as any other system at joins. Admittedly this wasn't always the case, but this was usually down to people doing Silly Joins, trying to replicate their old data-warehouse style workloads in Hadoop without re-engineering their data, and doing it on pre-Parquet data formats (RCFile was particularly poor at joins). Back in reality, where people actually do the sensible thing and thoroughly denormalise their analytical datasets, Impala always outperforms all its major open source competitors[1] and almost always outperforms An Unnammed Commercial Appliance Competitor[2] on the TPC-DS workload[3], which is join-heavy and not at all optimised for Impala.
[1] http://blog.cloudera.com/blog/2014/05/new-sql-choices-in-the-apache-hadoop-ecosystem-why-impala-continues-to-lead/
[2] http://blog.cloudera.com/blog/2014/01/impala-performance-dbms-class-speed/
[3] http://www.tpc.org/tpcds/