All of the above solutions involve more expertise and time, which is not
suitable for small teams. Ofcourse, they achieve good results for large
data and big companies.  Small companies will loose the benefit of using
the appengine paas with such wide solutions.
It is beneficial to provide some aggregation layer on top of datastore with
built in fast google technologies.
Interested developers will choose to use it.

Regards,
Rajesh
*www.VeersoftSolutions.com <http://www.veersoftsolutions.com/>*
*www.GainERP.com <https://www.gainerp.com/>*
*Accounting/Inventory/Orders/Sales/Purchase on Google Cloud Platform and
Mobile*

On Sat, Jan 28, 2017 at 12:48 AM, Jim <jeb62...@gmail.com> wrote:

> Another option is to use map-reduce against your datastore tables for
> aggregation of truly 'big' data sets.  It's nowhere near as flexible as
> some of the other options mentioned here, but if your requirements are
> fairly static it works great and will allow you to keep your data in one
> place.
>
>
>
> On Thursday, January 26, 2017 at 2:52:06 PM UTC-6, George (Cloud Platform
> Support) wrote:
>>
>> How large can your sales invoice data get in the end? The solutions
>> recommended above may work well for relatively small volumes. If you need
>> to process terabytes of data in the end, Cloud Bigtable
>> <https://cloud.google.com/bigtable/docs/overview> might prove speedier
>> and cost less overall.
>>
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