Don’t Accept The Status Quo For Hadoop

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Hadoop is Everywhere – 99% companies will deploy/pilot Hadoop in 18-24 months according to IDC.  These environments will largely be based around standalone servers resulting in added management tasks due to data being spread out across many disk spindles across the data center.  With Hadoop clusters quickly expanding, organizations are starting to experience the typical growing pains one can compare to adolescence.  This begs the question- should DAS server configuration be the accepted status-quo for Hadoop deployments?

idcisilon

Whether you are getting started with Hadoop or growing your Hadoop deployment, EMC provides a long-term solution for Hadoop through shared storage and VM’s, delivering distinct value to the business in lower TCO and faster time-to-results.  I spoke with EMC Technical Advisory Architect Chris Harrold to explain why organizations are now turning to EMC to help transition Hadoop environments into adulthood.

1.  Almost every Hadoop deployment is based around the accepted configuration of standalone servers with DAS.   What have you seen as issues with this configuration with your customers?

 

These environments are growing rapidly.  As a result, the ability to support these environments starts to degrade pretty rapidly when you get to a larger scale.  Servers with DAS tend to be more difficult because they have components that can fail internally and require more babysitting over an enterprise class platform.

For example, it’s no trivial task to expand this environment, as you have to acquire the servers, stand them all up, configure, rack, and power them.  Just to add a sandbox or test environment is difficult in this standalone server model.

There is also a steep learning curve with Hadoop not only in terms of the analytics component but also just to simply get data in and out of Hadoop in a DAS environment.

2.  Hadoop was designed to run on a DAS architecture where the compute and storage is tightly coupled.  Why does EMC believe decoupling storage and compute through a shared storage and virtualizing compute resources is a better architecture?  How does this architecture address the issues you mentioned above?

When Hadoop was first introduced to the market in 2000, shared or enterprise storage was a high-end commodity; therefore it was very difficult to design something like Hadoop on shared storage.  Since then, shared storage has become more affordable.   At the same time, networking speeds have become faster so now it is more feasible to decouple compute and storage. By deploying Hadoop on a shared storage model, you eliminate all the issues around manageability with DAS and gain the benefits of enterprise class features such as virtualization, SANs, and scale out NAS.

Also, deploying large-scale standalone architectures is really a legacy approach, as many enterprises have moved away from this to a shared architecture.  As Hadoop is becoming a key component of data architectures, it will be challenging to maintain standalone servers since many enterprises have evolved to virtualized, shared environments.

EMC is enabling organizations to leverage enterprise storage and virtualization to quickly and easily deploy and manage growing Hadoop environments.  Utilizing enterprise technologies with Hadoop you also gain benefits such as ease of data import/export, data protection, and security.

3.  What are the components of the EMC recommended architecture for Hadoop?

We provide choice on how organizations want to architect Hadoop environments.  For shared storage, we provide EMC Isilon HDFS enabled storage, which is certified with all major Hadoop distributions.  We also provide Software Defined Storage through EMC ViPR HDFS/Object storage, which is certified with all major storage arrays and Hadoop distributions.

For pre-integrated compute and shared storage, the EMC Data Computing Appliance provides an optimized architecture utilizing Pivotal HD and EMC Isilon.

For an integrated infrastructure approach, we partner with VCE vBlock to provide choice of compute, storage, and networking technologies from VMware, Cisco, and EMC to optimize any major Hadoop distribution deployment.

4.  Who are the ideal candidates for the EMC architecture for Hadoop and why?

All organizations benefit from this architecture.  Whether you are just getting started with Hadoop or have a large-scale deployment, we make it easy to rapidly deploy and manage a Hadoop environment.  In fact, utilizing EMC storage, you can analytics enable data already living in storage arrays.  You don’t have to copy or move data to a separate standalone Hadoop DAS environment.   We have several step by step guides to walk you the process of easily configuring your Hadoop environment for HDFS enabled storage.

5. Although Hadoop is everywhere with IDC estimating that 99%o of companies will deploy/pilot Hadoop in the next 18-24 months, gaining ROI from the deployment is a challenge due to lack of skills – identifying the right opportunity and then executing.  How does EMC address this issue?

Yes, this a huge problem in the industry especially lack of Data Science skills.  EMC addresses the skills shortage through our services across the EMC Federation.  Pivotal Data Labs provides access to some of the best minds in Data Science to help organizations identify opportunities and execute utilizing the latest Big Data technologies and techniques.  The EMC Vision Workshop creates a strategic Big Data blueprint for organizations to continuously identify Big Data uses cases based on the organization’s business initiatives and implementation feasibility.  And this has become a huge success as the EMC Vision Workshop creates the needed organizational alignment – Lines of Business continuously working, communicating, and collaborating with IT in order to successfully identify the right Big Data use cases for success.

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One thought on “Don’t Accept The Status Quo For Hadoop

  1. Need help to clarify using emc storage does not need to copy to another stand alone Das for Hadoop. If I intend to run analytics for 2 different users, it will be using the same raw data and ingest into the analytic application layer database like Pivotal?
    Can I archive those raw data (data lake?) and move to another location and then run analytics from a different location?
    thanks

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