[okfn-labs] Quick and dirty analytics on large CSVs - what's the best approach?

Thu Apr 11 17:57:05 UTC 2013

Is that file available compressed?  Their web server doesn't seem to know
how to serve it compressed (which must be killing their bandwith).

Tom

On Thu, Apr 11, 2013 at 1:04 PM, Rufus Pollock <rufus.pollock at okfn.org>wrote:

> Hi folks,
>
> I'm playing around with some largish CSV files as part of a data
> investigation for OpenSpending to look at which companies got paid the
> most by (central) government in the UK last year. (More details can be
> found in this issue:
> <https://github.com/openspending/thingstodo/issues/5>)
>
> The dataset i'm working with is the UK departmental spending which,
> thanks to Friedrich's efforts, is already nicely ETL'd into one big
> 3.7 Gb file [1].
>
> My question is do folks have any thoughts on how best to do quick and
> dirty analytics on this? In particular, I was considering options
> like:
>
> * Postgresql - load, build indexes and then sum, avg etc (already
> started on this)
> * Elastic MapReduce (AWS Hadoop)
> * Google BigQuery
>
> Let me know your thoughts!
>
> Regards,
>
> Rufus
>
> [1]: Details of the file
> https://github.com/openspending/thingstodo/issues/5#issuecomment-16222168
>
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