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State...<br />
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1) Amount of data you'll be managing.<br />
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2) Type of workload (primarily read, primarily write, mixed).<br />
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3) Type of clients accessing data - like browsers or API service.<br />
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4) Typical amount of data returned each client access.
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State...

1) Amount of data you'll be managing.

2) Type of workload (primarily read, primarily write, mixed).

3) Type of clients accessing data - like browsers or API service.

4) Typical amount of data returned each client access.

Vice President Of Engineering and Product Development

Tej Luthra

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Hello.. what type of storage architecture would be most useful for kafka, zookeeper running in a kubernetes and containerized environment. Would glusterfs that manages storage persistence, replication, resiliency, and HA be an overkill when the distributed bigdata services do this on their own anyway? We're debating internally why glusterfs would be needed for distributed bigdata systems like Cassandra, Mongo, Kafka, Zookeeper, etc. Thus trying to keep the architecture simpler. Just wondering if anyone has thoughts or experience to share that would help us a bit also.
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Hello.. what type of storage architecture would be most useful for kafka, zookeeper running in a kubernetes and containerized environment. Would glusterfs that manages storage persistence, replication, resiliency, and HA be an overkill when the distributed bigdata services do this on their own anyway? We're debating internally why glusterfs would be needed for distributed bigdata systems like Cassandra, Mongo, Kafka, Zookeeper, etc. Thus trying to keep the architecture simpler. Just wondering if anyone has thoughts or experience to share that would help us a bit also.

Need for GlusterFS for distributed bigdata services running in a kubernetes managed environment

Big data describes data sets that are so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying and information privacy. The term often refers simply to the use of predictive analytics or certain other advanced methods to extract value from data, and seldom to a particular size of data set.