Hadoop-as-a-Service: An On-Premise Promise?

Hadoop-as-a-Service (HaaS) is generally referred to Hadoop in the cloud, a handy alternative to on-premise Hadoop deployments for organizations with overwhelmed data center administrators that need to incorporate Hadoop but don’t have the resources to do so. What if there was also a promising option to successfully build and maintain Hadoop clusters on-premise also referred to HaaS? The EMC Hybrid Cloud (EHC) enables just this – Hadoop in the hybrid cloud.

EHC, announced at EMC World 2014, is a new end-to-end reference architecture that is based on a Software-Defined Data Center architecture comprising technologies from across the EMC federation of companies: EMC II storage and data protection, Pivotal CF Platform-as-a-service (PaaS) and the Pivotal Big Data Suite, VMware cloud management and virtualization solutions, and VMware vCloud Hybrid Service. EHC’s Hadoop-as-a- Service was demonstrated at last week’s VMworld 2014 San Francisco – the underpinnings of a Virtual Data Lake:

EHC leverages these tight integrations across the Federation so that customers can extend their existing investments for automated provisioning & self-service, automated monitoring, secure multi-tenancy, chargeback, and elasticity to addresses requirements of IT, developers, and lines of business. I spoke with Ian Breitner, Global Solutions Marketing Director for Big Data, to explain why EMC’s approach to HaaS should be considered over other Hadoop cloud offerings.

1.  In your opinion, what are the key characteristics of HaaS?

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EMC Hadoop Starter Kit: Creating a Smarter Data Lake

Pivotal HD offers a wide variety of data processing technologies for Hadoop – real-time, interactive, and batch. Add integrated data storage EMC Isilon scale-out NAS to Pivotal HD and you have a shared data repository with multi-protocol support, including HDFS, to service a wide variety of data processing requests. This smells like a Data Lake to me – a general-purpose data storage and processing resource center where Big Data applications can develop and evolve. Add EMC ViPR software defined storage to the mix and you have the smartest Data Lake in town, one that supports additional protocols/hardware and automatically adapts to changing workload demands to optimize application performance.

EMC Hadoop Starter Kit, ViPR Edition, now makes it easier to deploy this ‘smart’ Data Lake with Pivotal HD and other Hadoop distributions such as Cloudera and Hortonworks. Simply download this step-by-step guide and you can quickly deploy a Hadoop or a Big Data analytics environment, configuring Hadoop to utilize ViPR for HDFS, with Isilon hosting the Object/HDFS data service.  Although in this guide Isilon is the storage array that ViPR deploys objects to, other storage platforms are also supported – EMC VNX, NetApp, OpenStack Swift and Amazon S3.

I spoke with the creator of this starter kit James F. Ruddy, Principal Architect for the EMC Office of the CTO to explain why every organization should use this starter kit optimize their IT infrastructure for Hadoop deployments.

1.  The original EMC Hadoop Starter Kit released last year was a huge success.  Why did you create ViPR Edition?

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Pivotal Big Data Suite: Eliminating the Tax On A Growing Hadoop Cluster

The promise of Big Data is about analyzing more data to gain unprecedented insight, but Hadoop pricing can place serious constraints on the amount of data that can actually be stored for analysis.  Each time a node is added to a Hadoop cluster to increase storage capacity, you are charged for it.  Because this pricing model is counterintuitive to the philosophy of Big Data, Pivotal has removed the tax to store data in Hadoop with its announcement of Pivotal Big Data Suite.

Through a Pivotal Big Data Suite subscription, customers store as much data as they want in fully supported Pivotal HD, paying for only value added services per core – Pivotal Greenplum Database, GemFire, SQLFire, GemFire XD, and HAWQ.   The significance of this new consumption model is that customers can now store as much Big Data as they want, but only be charged for the value they extract from Big Data.

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*Calculate your savings with Pivotal Big Data Suite compared to traditional Enterprise Data Warehouse technologies.

Additionally, Pivotal Big Data Suite removes the mind games associated with diverse data processing needs of Big Data.  With a flexible subscription of your choice of real-time, interactive, and batch processing technologies, organizations are no longer locked into a specific technology because of a contract.  At any point of time, as Big Data applications grow and Data Warehouse applications shrink, you can spin up or down licenses across the value added services without incurring additional costs.  This pooled approach eliminates the need to procure new technologies, which results in delayed projects, additional costs, and more data silos.

I spoke with Michael Cucchi, Senior Director of Product Maketing at Pivotal, to explain how Pivotal Big Data Suite radically redefines the economics of Big Data so organizations can achieve the Data Lake dream.

1. What Big Data challenges does Big Data Suite address and why?

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Pivotal HD 2.0: Hadoop Gets Real-Time

Everything we do generates events – click on a mobile ad, pay with a credit card, tweet, measure heart rate, accelerate on the gas pedal, etc. What if an organization can feed these events into predictive models as soon as the event happens to quickly and more accurately make decisions that generate more revenue, lower costs, minimize risk, and improve the quality of care? You would need deep and fast analytics provided by Big Data platforms such as Pivotal HD 2.0 announced yesterday.

Pivotal HD 2.0 brings an in-memory, SQL database to Hadoop through seamless integration with Pivotal GemFire XD, enabling you to combine real-time data with historical data managed in HDFS. Closed loop analytics, operational BI, and high-speed data ingest are now possible in a single OLTP/OLAP platform without any ETL processing required. Use cases are ones that are time sensitive in nature. For example, telecom companies are at the forefront of applying real-time Big Data analytics to network traffic. The “store first, analyze second” method does not make sense for rapidly shifting traffic that requires immediate action when issues arise.

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I spoke with Senior Director of Engineering at Pivotal Makarand Gokhale to explain the value in bringing OLTP to a traditional batch processing Hadoop.

1. Real-time solutions for Hadoop can mean many things- performing interactive queries, real-time event processing, and fast data ingest. How would you describe Pivotal HD’s real-time data services for Hadoop?

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