EMC and RainStor Optimize Interactive SQL on Hadoop

Pivotal HAWQ was one of the most groundbreaking technologies entering the Hadoop ecosystem last year through its ability to execute complete ANSI SQL on large-scale datasets managed in Pivotal HD. This was great news for SQL users – organizations heavily reliant on SQL applications and common BI tools such as Tableau and MicroStrategy can leverage these investments to access and analyze new data sets managed in Hadoop.

Similarly, RainStor, a leading enterprise database known for its efficient data compression and built-in security, also enables organizations to run ANSI SQL queries against data in Hadoop – highly compressed data.  Due to the reduced footprint from extreme data compression (typically 90%+ less), RainStor enables users to run analytics on Hadoop much more efficiently.  In fact, there are many instances where queries run significantly faster with a reduced footprint plus some filtering capabilities that figure out what not to read.  This allows customers to minimize infrastructure costs and maximize insight for data analysis on larger data sets.

Serving some of the largest telecommunications and financial services organizations, RainStor enables customers to readily query and analyze petabytes of data instead of archiving data sets to tape and then having to reload it whenever it is needed for analysis. RainStor chose to partner with EMC Isilon scale-out NAS for its storage layer to manage these petabyte-scale data environments even more efficiently. Using Isilon, the compute and storage for Hadoop workload is decoupled, enabling organizations to balance CPU and storage capacity optimally as data volumes and number of queries grow.

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Furthermore, not only are organizations able to run any Hadoop distribution of choice with RainStor-Isilon, but you can also run multiple distributions of Hadoop against the same compressed data. For example, a single copy of the data managed in Rainstor-Isilon can service Marketing’s Pivotal HD environment, Finance’s Cloudera environment, and HR’s Apache Hadoop environment.

To summarize, running RainStor and Hadoop on EMC Isilon, you achieve:

  • Flexible Architecture Running Hadoop on NAS and DAS together: Companies leverage DAS local storage for hot data where performance is critical and use Isilon for mass data storage. With RainStor’s compression, you efficiently move more data across the network, essentially creating an I/O multiplier.
  • Built-in Security and Reliability: Data is securely stored with built-in encryption, and data masking in addition to user authentication and authorization. Carrying very little overhead, you benefit from EMC Isilon FlexProtect, which provides a reliable, highly available Big Data environment.
  • Improved Query Speed: Data is queried using a variety of tools including standard SQL, BI tools Hive, Pig and MapReduce. With built-in filtering, queries speed-up by a factor of 2-10X compared to Hive on HDFS/DAS.
  • Compliant WORM Solution: For absolute retention and protection of business critical data, including stringent SEC 17a-4 requirements, you leverage EMC Isilon’s SmartLock in addition to RainStor’s built-in immutable data retention capabilities.

I spoke to Jyothi Swaroop, Director of Product Marketing at Rainstor, to explain the value of deploying EMC Isilon with RainStor and Hadoop.

1.  RainStor is known in the industry as an enterprise database architected for Big Data. Can you please explain how this technology evolved and what needs it addresses in the market?

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VCE Vblock: Converging Big Data Investments To Drive More Value

As Big Data continues to demonstrate real business value, organizations are looking to leverage this high value data across different applications and use cases. The uptake is also driving organizations to transition from siloed Big Data sandboxes, to enterprise architectures where they are mandated to address mission-critical availability and performance, security and privacy, provisioning of new services, and interoperability with the rest of the enterprise infrastructure.

Sandbox or experimental Hadoop on commodity hardware with direct attached storage (DAS) makes it difficult to address such challenges for several reasons – difficult to replicate data across applications and data centers, lack of IT oversight and visibility into the data, lack of multi-tenancy and virtualization, difficult to streamline upgrades and migrate technology components, and more. As a result, VCE, leader in converged or integrated infrastructures, is receiving an increased number of requests on how to evolve Hadoop implementations reliant on DAS to being deployed on VCE Vblock Systems -  an enterprise-class infrastructure that combines server, shared storage, network devices, virtualization, and management in a pre-integrated stack.

Formed by Cisco and EMC, with investments from VMware and Intel, VCE enables organizations to rapidly deploy business services on demand and at scale – all without triggering an explosion in capital and operating expenses. According to IDC’s recent report, organizations around the world spent over $3.3 billion on converged systems in 2012, and forecasted this spending to increase by 20% in 2013 and again in 2014. In fact, IDC calculated that Vblock Systems infrastructure resulted in a return on investment of 294% over a three-year period and 435% over a five-year period compared to data on traditional infrastructure due to fast deployments, simplified operations, improved business-support agility, cost savings, and freed staff to launch new applications, extend services, and improve user/customer satisfaction.

I spoke with Julianna DeLua from VCE Product Management to discuss how VCE’s Big Data solution enables organizations to extract more value from Big Data investments.

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1.  Why are organizations interested deploying Hadoop and Big Data applications on converged or integrated infrastructures such as Vblock?

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Revolution Analytics Boosts the Adoption of R in the Enterprise

The path to competitive advantage is being able to make predictions from Big Data. Therefore, the more you can build predictive analytics into your business processes, the more successful your organization will become. There is no doubt that open-source R is the programming language of choice for predictive analytics, and thanks to Revolution Analytics, R has the enterprise capabilities needed to drive adoption across the organization and for every employee to make data-driven decisions.

Revolution Analytics is to R what the vendor RedHat is to the Linux operating system—a company devoted to enhancing and supporting open-source software for enterprise deployments. For example, Revolution Analytics recently released R Enterprise 7 to meet the performance demands of Big Data whereby R now runs natively within Hadoop and data warehouses. I spoke with David Smith, VP of Marketing at Revolution Analytics to explain how Revolution Analytics has accelerated the adoption of R in the enterprise.

1.  What benefits do Revolution Analytics provide to organizations over just using open-source R?

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Alpine Data Labs – Making Predictive Analytics Pervasive and Persuasive

Big Data has exposed the need for deeper data insights through predictive analytic techniques such as data mining, machine learning, and modeling. The interesting thing to note is that predictive analytics has been around for a long time, used by a select few, in select organizations. Its value has always been recognized and applauded, but its true potential never fully realized due to lack of widespread adoption, as well as issues around data accessibility, performance, statistical expertise, business sponsorship, cost, and more. In fact, nearly 90 percent of organizations that do employ predictive analytic software agree that it has given them a competitive advantage, according to a new survey.

The advent of Big Data has driven the uptake of predictive analytics due to the curiosity of very capable Data Scientists, along with new tools and technologies from companies such as Alpine Data Labs.  Alpine Data Labs provides next generation predictive analytics to address legacy issues and meet the new demands of Big Data. But more importantly, Alpine Data Labs is mainstream-oriented whereby business users, not just statisticians and Data Scientists, are compelled to mine data.

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Backed by $16M in Series B funding, Alpine Data Labs is getting some serious momentum in the Big Data analytics startup space, offering zero coding for creating and deploying complex predictive models on Hadoop. I spoke with Alpine Data Labs CEO Joe Otto to talk about their game changing approach to predictive analytics for Big Data.

1.  Lets first talk about leading predictive analytics incumbents such as SAS, IBM SPSS, and other analytics vendors who got their start years ago with desktop and server software designed for data mining and advanced analytics. How has Alpine Data Labs overcome the issues around these incumbent technologies and address the new needs of Big Data?

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