EMC’s Making Waves In The Data Lake
Feb19

EMC’s Making Waves In The Data Lake

EMC has been floating its Data Lake Foundation concept for the better part of a year, but today the company is launching a flotilla of new products and solutions intended to: eliminate storage silos; simplify management; improve utilization; massively scale; support existing and emerging standards; be secure; and deliver in-place analytics with faster time to results. Among the goodies are: a 2.5X increase in capacity with the Isilon HD400 platform (up to 50PB within a single cluster), and the extremely dense (3.2PB/ rack) HD400 will help reduce operational expenses including power, cooling and datacenter floor space expense by 50%; OneFS 7.2 operating system will support newer and more current versions of Hadoop protocols including HDFS 2.3 and 2.4; and, OpenStack Swift support for both file and object storage. Depending upon the organization and the industry, data is growing at least 50% per year, and this represents “both a problem and an opportunity,” said Suresh Sathyamurthy, Sr. Director, Product Marketing for EMC’s Emerging Technologies Division. The data lake concept offers a way to address both, he said. There are two major takeaways from today’s announcements, said Sathyamurthy: scalability, and the new software ensures EMC is up to date with the ecosystem. And while they provide additional capabilties for the thousands of customers performing analytics on the Isilon platform, the announcements also open the door to a lot more prospects, especially service providers. “Even with the volume of customers we have today… we believe we are the market share leader… we’re just scratching the surface”. EMC is looking at 3-4x growth next year, he said. Nick Kirsch, EMC’s VP & Chief Technology Officer, ETD, recently noted that  data lakes are here to stay. He quoted IDC, which said “data lakes should be a part of every big data workflow in the enterprise.” He divided the data lake market into two segments: the first involves utilizing intelligent software-defined storage resource management to efficiently store petabytes of data—and making that data available with multiprotocol access.; the second means a hyper-converged data lake that’s complete with apps, compute resources, and networks—delivered as an integrated appliance. In both cases, the decision is based on the unique challenges businesses face in delivering performance, managing growth, and gaining insights from their data. According to a recent blog from ETP’s David Noy, VP Product Management, EMC received recognition for its Isilon Scale-Out NAS family of products in the Gartner report Critical Capabilities for Scale-Out File System Storage. Created to identify the contenders in the Scale-Out File Storage industry, Isilon rated highest in three of five Use cases: Overall Use Cases (4.17 out of 5), Commercial HPC Use Cases...

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Look at the data management architecture and technology portfolio of any large enterprise and you will more than likely find a heterogeneous collection of databases, data warehouses, data integration tools and business intelligence applications from multiple vendors. Over the years, most large enterprises have spent multiple-millions of dollars procuring, deploying and operating these data management technologies, which today support many mission-critical business processes and revenue-generating applications. While many of these technologies are growing long in the tooth and cost enterprise customers millions of dollars a year in maintenance and support, they none-the-less form the backbone of many enterprise operations and are not going away any time soon. It is against this backdrop that Hadoop makes its appearance. The open source Big Data platform began life as a set of related data storage and processing approaches developed by web giants like Google and Yahoo to solve specific tasks (first among them, indexing the world wide web.) But Hadoop quickly evolved into a general-purpose framework supporting multiple analytic use-cases. A number of forward-thinking enterprises took notice, as, simultaneously, the ever-increasing volume, variety and velocity of data (a.k.a. Big Data) raining down on the enterprise began to overwhelm the traditional data management stack. According to feedback from the Wikibon community, many data management practitioners today are eager to leverage Hadoop to at once relieve some of this pressure on existing data management infrastructure and to develop new, differentiating analytic capabilities. To read the complete article, CLICK...

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IT solutions seldom follow an entirely linear path, either technologically or commercially. Instead, they proceed in fits and spurts – overcoming points of resistance, adding key new features and innovations, adapting to marketplace dynamics and pursuing new opportunities when and where they emerge. These points were on clear display at SAP’s recent TechEd 2014 conference, the company’s annual get together for technically-inclined customers, partners and IT professionals, where improvements to and presentations concerning SAP’s HANA technologies abounded. But what was particularly interesting about the gathering was a significant shift in how SAP is talking about HANA and explaining its capabilities and value to businesses. Let’s take a closer look at that. To read the complete article, CLICK HERE NOTE: This column was originally published in the Pund-IT...

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In December 2014, SpaceCurve will announce a spatial data platform that is designed for big data systems and spatial data, enabling real-time ingest, index, query and correlation of spatial, sensor, “Internet of things”, mobile device, social media and other streaming and historical data sources for real-time analytics and business insights. SpaceCurve has completed beta testing, the solution is generally available and they have five customers. Traditional data architectures weren’t designed to handle spatial data at the speed and scale required to gain insight that can answer complex spatial questions in real-time. SpaceCurve has been architected for large volume (imagery data, for example) fast-moving data (from sensors) and the complex fusion of several types of data (weather, chemical, terrain, for example.) To read the complete article, CLICK HERE NOTE: This column was originally published in the Pund-IT...

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