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From Ali Akhtar <ali.rac...@gmail.com>
Subject Re: Architecture recommendations for a tricky use case
Date Thu, 29 Sep 2016 14:01:38 GMT
It needs to be able to scale to a very large amount of data, yes.

On Thu, Sep 29, 2016 at 7:00 PM, Deepak Sharma <deepakmca05@gmail.com>
wrote:

> What is the message inflow ?
> If it's really high , definitely spark will be of great use .
>
> Thanks
> Deepak
>
> On Sep 29, 2016 19:24, "Ali Akhtar" <ali.rac200@gmail.com> wrote:
>
>> I have a somewhat tricky use case, and I'm looking for ideas.
>>
>> I have 5-6 Kafka producers, reading various APIs, and writing their raw
>> data into Kafka.
>>
>> I need to:
>>
>> - Do ETL on the data, and standardize it.
>>
>> - Store the standardized data somewhere (HBase / Cassandra / Raw HDFS /
>> ElasticSearch / Postgres)
>>
>> - Query this data to generate reports / analytics (There will be a web UI
>> which will be the front-end to the data, and will show the reports)
>>
>> Java is being used as the backend language for everything (backend of the
>> web UI, as well as the ETL layer)
>>
>> I'm considering:
>>
>> - Using raw Kafka consumers, or Spark Streaming, as the ETL layer
>> (receive raw data from Kafka, standardize & store it)
>>
>> - Using Cassandra, HBase, or raw HDFS, for storing the standardized data,
>> and to allow queries
>>
>> - In the backend of the web UI, I could either use Spark to run queries
>> across the data (mostly filters), or directly run queries against Cassandra
>> / HBase
>>
>> I'd appreciate some thoughts / suggestions on which of these alternatives
>> I should go with (e.g, using raw Kafka consumers vs Spark for ETL, which
>> persistent data store to use, and how to query that data store in the
>> backend of the web UI, for displaying the reports).
>>
>>
>> Thanks.
>>
>

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