Pure Storage, Inc.

Stefan Dorsett

Ivan Jibaja

Prashant Jaikumar

Curtis Pullen

Srinivas Chellappa

Gaurav Jain

Neil A Vachharajani

Roy Kim

Neil Buda Vachharajani

Creating a containerized data analytics pipeline, including: creating a data analytics pipeline having a component that is deployed within a container that executes in a container cluster that is external to shared storage accessible by components of the data analytics pipeline; and responsive to detecting that a component of the data analytics pipeline has failed, deploying another instance of the component in a failover container that is configured to support one or more components of the data analytics pipeline.

Creating a containerized data analytics pipeline

Executing a big data analytics pipeline in a storage system that includes compute resources and shared storage resources, including: receiving, from a data producer, a dataset; storing, within the storage system, the dataset; allocating processing resources to an analytics application; and executing the analytics application on the processing resources, including ingesting the dataset from the storage system.

Executing a big data analytics pipeline using shared storage resources

Processing data through a storage system in a data pipeline including receiving, by the storage system, a dataset from a collector on a data producer, wherein the dataset is disaggregated from metadata for the dataset by the collector; storing the dataset on the storage system; receiving, by the storage system from a data indexer, a request for data from the dataset, wherein the request for the data comprises the metadata gathered by the collector on the data producer; servicing, by the storage system, the request for the data by locating the data using the metadata gathered by the collector on the data producer and received in the request for the data; and receiving, from the data indexer, indexed data indexed using the metadata gathered by the collector on the data producer.

Processing data through a storage system in a data pipeline

Providing for high availability in a data analytics pipeline without replicas, including: creating a data analytics pipeline, wherein each component of the data analytics pipeline is deployed within a container; creating a failover container; detecting that a component within the data analytics pipeline has failed; and responsive to detecting that the component within the data analytics pipeline has failed, deploying the component within the data analytics pipeline that has failed in the failover container.

Creating a highly available data analytics pipeline without replicas

Managing a data analytics pipeline

Providing for high availability in a data analytics pipeline without replicas

Storage system with compute resources and shared storage resources

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San Jose, CA, United States of America

Stefan Dorsett