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From Jörn Franke <jornfra...@gmail.com>
Subject Re: Near Real time analytics with Spark and tokenization
Date Sun, 15 Oct 2017 09:12:17 GMT
Can’t you cache the token vault in a caching solution , such as Ignite? The lookup of single
tokens would be really fast.
About what volumes one talks about? 

I assume you refer to PCI DSS, so security might be an important aspect which might be not
that easy to achieve with vault-less tokenization. Then, with vault-less tokenization you
need to recalculate all tokens  in case the secret is compromised.
There might be other compliance requirements , which may need to be weighted by the users.

> On 15. Oct 2017, at 09:15, Mich Talebzadeh <mich.talebzadeh@gmail.com> wrote:
> 
> Hi,
> 
> When doing micro-batch streaming of trade data we need to tokenization certain columns
before data lands in Hbase with Lambda architecture.
> 
> There are two ways of tokenizing data, vault based and vault less using something like
Protegrity tokenization.
> 
> The vault-based tokenization requires clear text and token values to be stored in a vault
say Hbase and crucially the vault cannot be on the same Hadoop cluster that we are processing
real time. It could be in another Hadoop cluster for tokenization.
> 
> This causes latency for real time analytics when token values have to be calculated and
then stored in remote Hbase vault.
> 
> What is the general approach to this type of issue. It seems to be based to use vault-less
tokenization?
> 
> Thanks
> 
> Dr Mich Talebzadeh
>  
> LinkedIn  https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw
>  
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> 
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