Hi,

Thanks for the reply. Let me explain our scenario little bit more.
Currently we have multiple data feeds through files from different systems.
We run batch jobs to extract the data from files, normalize that data,
match that data against Oracle database and finally consolidate the cleaned
data in Oracle.

I am evaluating rather than running batch jobs, can I run spark streaming
from the data files to finally write the cleansed data into Oracle
database. Once the data is consolidated in Oracle, it serves as the source
of truth for external users.

Regards,
Sri Eswari.

On Mon, Sep 21, 2015 at 10:55 PM, Jörn Franke <jornfra...@gmail.com> wrote:

> You do not need Hadoop. However, you should think about using it. If you
> use Spark to load data directly from Oracle then your database might have
> unexpected loads of data once a Spark node may fail. Additionally, the
> Oracle Database, if it is not based on local disk, may have a storage
> bottleneck. Furthermore, Spark standalone has no resource management
> mechanism for supporting different slas, you may need yarn (hadoop) for
> that. Finally, using the Oracle Database for storing all the data may be an
> expensive exercise. What I have seen often is that hadoop is used for
> storing all the data and managing the resources. Spark can be used for
> machine learning over this data and the Oracle Database (or any relational
> datastore, Nosql database, in-memory db) is used to serve the data to a lot
> of users. This is also the basic idea behind the lambda architecture.
>
> Le mar. 22 sept. 2015 à 7:13, Sri <sriesh.subb...@gmail.com> a écrit :
>
>> Hi,
>>
>> We have a usecase  where we get the dated from different systems and
>> finally
>> data will be consolidated into Oracle Database. Does spark is a valid
>> useless for this scenario. Currently we also don't have any big data
>> component. In case if we go with Spark to ingest data, does it require
>> hadoop.
>>
>>
>>
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