Storage and Availability Management Blog

The Storage and Availability Management Group at Symantec is dedicated to providing solutions that enable efficient storage management and highly available infrastructure. Find news, information and tips that help you to resolve your storage management, high availability and disaster recovery issues across the heterogeneous data center.

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    Updated: Fahima 13 Aug 2012

    All the Hype around Big Data Analytics Weighing you Down?

    You've heard the buzz word. So what's behind the hype? Big data can translate into big business when you can mine for the relevant insights. But where do you start? Running Hadoop Analytics is not a plug and play game. But now, Cluster File System users can plug our Hadoop Connector into the Hadoop stack, and run the analytics on our trusted file system, with all the benefits that Cluster File System delivers.  We just made available a Connector for Hadoop based on Cluster File System technology that enables you to run Big Data analytics on your existing infrastructure, running on tools that you are already familiar with. Working closely with Hortonworks, we launched Symantec Enterprise Solution for Hadoop to make Hadoop enterprise-ready, and fully supported.  What does this mean? Run analytics on...
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    Updated: Rags Srinivasan 06 Jun 2012

    Big Data Storage - Conversation with CTO of Storage and Availability Management Group

    What does Big Data mean to enterprises? How does this change their storage architecture? Does the need for taking advantage of Big Data analytics mean a cluster of 1000s of compute nodes? The answers create lot more confusion than clarity and it depends on who you ask. In an attempt to add clarity to the conversation I sat down with  V.R.Satish, CTO of Storage and Availability Management Group at Symantec to get his thoughts on how enterprises should approach the Big Data storage problem. Here are some excerpts from my conversation. Rags: Satish, we keep hearing about the data deluge, growth and variety. How do you think enterprises must approach Big Data? Satish: As always with any business decision, start with the problem we are trying to solve, focus on the value realization and how it will help you better serve your internal and external customers. How you approach the problem and...
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    Updated: Rags Srinivasan 05 Jun 2012

    Hadoop High Availabilty or Highly Available Hadoop

    For enterprises, what comes first?  Adopting a solution that is not highly available and then try to make it enterprise ready or start with high availability as core feature then add the power of analytics? In my past few articles I wrote about the challenges to adopting Hadoop in the enterprise and what it would take to make it enterprise ready. One of the points I highlighted is the NameNode high availability or the lack of it. In Hadoop Distributed File System (HDFS), NameNode is the metadata server that has the location information for data blocks distributed across DataNodes. If NameNode fails, the cluster would be unavailable to analytics applications. The Hadoop community has been working on a solution to add High Availability to HDFS. The solution entails adding another NameNode with shared storage and changing DataNodes to send...
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    Created: Rags Srinivasan 30 May 2012

    Options for Hadoop Storage - Looking Beyond Direct Attached Storage

    In my previous article I wrote about the five factors that limit Hadoop's role in the enterprise datacenter. To recap, the limitations are, Increased storage sprawl Three times as much storage Costly data moves Single point of failure No support for backup The first three issues stem directly from the architecture choice recommended for Hadoop clusters - use of many different compute nodes, each with its own embedded Direct Attached Storage (DAS).   Enterprises choosing Hadoop are forced to make the trade-off of accepting these limitations in favor of getting the power of Hadoop analytics.  Is DAS the only choice despite its limitations? In a very well researched article, John Webster of Evaluator group poses and answers the question on...
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    Updated: Rags Srinivasan 28 May 2012

    Enterprise Hadoop - Five issues with Hadoop that need addressing

    The biggest driver for adoption of Hadoop is its promise of unlocking value from an enterprise’s vast data store. Use cases that show incremental revenue from data analysis are very well publicized.  Every organization strives to achieve that and wants to leverage the power of data analytics to drive its revenues. Promises aside, Hadoop storage has severe issues that calls  into question its place in the enterprise datacenter. Increased storage and server sprawl – Hadoop cluster is built  with numerous commodity hosts, each with its own direct attached storage. Just when datacenter architects have spent considerable time and resources consolidating their datacenters and reducing footprint through server consolidation, virtualization and private cloud,  Hadoop requires them to build out a massively parallel system with hundreds or even thousands of compute nodes. Managing these numerous nodes and keeping them up to date...
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    Updated: Rags Srinivasan 30 May 2012

    Pitfalls of Observational Big Data Analysis

    The siren song of Big Data analysis is, "Don't filter data before you collect, don't try to decide whether or not certain data is relevant, collect everything. Analysis of such large volumes of data is bound to find something interesting". Let us look at an nice simple study reported recently about cyclists wearing helmets. This comes to us from an article in The Wall Street Journal. The main finding is, "Bike helmets make men ride faster". The question we need to ask about such a causation claim is how was the study conducted. The study falls in the category of Big Data analysis we see conducted with large volumes of unrelated data, just because it is available. Data was collected daily at seven locations, each equipped with two cameras programmed to detect moving objects...
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    Updated: Rags Srinivasan 30 May 2012

    Big Data and Spurious Correlations

    Suppose you read the following headline in a major newspaper article, what would you think? Student Test Scores Tied to Number of Bathrooms in their Homes Let us say, this article is also associated with a chart showing this relation   Look at those near perfect correlations. Should we start adding more bathrooms to help our children? Except there is no such study but very close.  The x-axis is actually income level of the family. While we see a nice positive correlation between income and test scores, Harvard Economics Professor Greg Mankiw warns us about the spurious correlation using...
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    Updated: Rags Srinivasan 30 May 2012

    Symantec Solution for Big Data Featured in IDC Link

    IDC analysts Jean S. Bozman and Laura DuBois published their latest analysis from Symantec Vision conference. In the May 15th IDC LINK (subscription required) they had this to say about Symantec solution (bold text mine) Big Data. Symantec is readying a product that leverages its clustering file system (CFS) to manage Hadoop-style workloads for the enterprise, through compatible APIs. The solution, which is designed to enable datacenters to leverage open-source Hadoop for enterprise workloads with high availability, will use customers' existing infrastructure. Although the broad outlines for this offering were discussed at VISION during technical sessions, this product would be...
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    Updated: Rags Srinivasan 30 May 2012

    Big Data or Big Analysis

    Does Big Data ( big on volume and variety) mean better insights? Taking this to the extreme, does Big Data eliminate application of thought? A recent New York Times article writes, Big Data, which should probably be called Big Analysis, is about looking at that information in novel ways to find new patterns for prediction. I agree with their call but let us not try to change an accepted terminology. What Times article states is the fact that we are able to look at data in new ways with newer tools. The value add comes more from analytics applications that help in answering the question at hand. At the extreme I refer to above, we see those who favor relying on volume of data, on the Bigness of Big Data, to tell us what to do. The next logical step for them is to include every possible data source and every bit of data in the analysis...
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    Updated: Rags Srinivasan 30 May 2012

    Big Data Driven Marketing

    Columbia Business School’s Center on Global Brand Leadership and the New York American Marketing Association (NYAMA) recently published their research on role of data and analytics in Marketing. Their report titled, "Marketing ROI in the Era of Big Data",provides key insights into how enterprises are applying Big Data analytics in their marketing decisions. To me the most important finding of this study is the gap between desire and reality. While 91% want to be data driven in their decisions, it has not yet reflected in practice Enterprises have been collecting data for long time. What has changed now is the volume, different types of  data (variety) and how fast it is changing (velocity).  In my own conversations I find many enterprises believe more data is not...