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.


*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?

When we introduced Business Data Lake last year, the industry confirmed that we had the right vision – include real-time, interactive, and batch data ingest and processing capabilities supported by data management technologies such as in-memory, MPP, and HDFS technologies. The challenge for customers was how to get started with the Data Lake journey and how much budget should be allocated across the breadth of data management technologies that comprise a Data Lake. Also, as data processing requirements change over time, customers want to protect IT investments and not be locked down into any specific technology.

Although Pivotal has always provided enterprise-class technologies to support Busniess Data Lakes, customers were still challenged with how much to invest in Pivotal Greenplum Database for MPP analytical processing versus Pivotal HAWQ for interactive SQL access to HDFS versus Pivotal Gemfire for real time, in-memory database processing, etc. To take these pain points off the table, Big Data Suite offers customers a flexible, multi-year subscription to Pivotal Greenplum Database, GemFire, SQLFire, GemFire XD, HAWQ, and Pivotal HD. It includes unlimited use Pivotal HD through a paid subscription of value added services- Pivotal Greenplum Database, GemFire, SQLFire, GemFire XD, HAWQ.

The significance of this new consumption model is that customers can now store as much Big Data as they want in HDFS, but only be charged for the value they extract from the data.  As an example, a customer could buy 1,000 cores worth of Big Data Suite, and for the first year use 80% of cores dedicated to Pivotal Greenplum Database and 20% of cores dedicated to HAWQ. Over the years, as data and insight start to expand in HDFS, the customer can spin down the use of Pivotal Greenplum Database, and spin up the use of HAWQ without having to pay anything extra as long as the cores don’t exceed 1,000.

2.  What was the impetus in providing unlimited use of Pivotal HD in the Big Data Suite?

Data grows 60% per year, yet IT budgets grow 3-5% per year. Hadoop pricing does not meet limited IT budgets, as vendors charge by terabyte or node. Each time you want to add more data to your Data Lake to increase capacity, you are charged for it. We are telling customers that if they invest in Pivotal, they can grow their Data Lake or expand the HDFS footprint without being taxed for it.  This allows customers to focus on more important aspects such as data analysis and operationalization through analytical database, SQL query, and in-memory technologies.

3.  It sounds like Pivotal Big Data Suite brings all data management technologies in line with Hadoop economics?

Yes, with Big Data Suite, we are aggressively cutting the price of Greenplum (Analytics Data Warehouse) and GemFire (In-memory data grid system) to be in line with the cost economics of Hadoop.

4.  How does Big Data Suite address Data Lake strategies?

Big Data suite fulfills the data management needs of a Data Lake. And because each organization will have different data processing needs over time, we have designed a flexible pricing model for Big Data Suite whereby you can mix and match technologies at any point in time.

For example, a Data Lake for a Telecommunications organization will look different from a Data Lake for a Healthcare organization. The Telco may have immediate real time requirements, whereas the Healthcare Payor may have immediate interactive SQL access to HDFS requirements, but prioritize real time capabilities for next year. If customers standardize with other Hadoop vendors, they may end up purchasing multi-vendor technologies for real time, interactive, and batch processing over time simply because of pricing, creating more data silos. With Pivotal, we remove these silos with the Big Data Suite flexible consumption model approach.

5.  Who are the ideal candidates for the Big Data Suite?

Big Data Suite is ideal for any organization since we believe a flexible subscription model is the smart way to grow a Data Lake. I confirmed this approach with our Data Science team – when they experiment with new sets of data to solve a problem, the data processing requirements are unknown until you operationalize it. One use case may require an analytical database technology versus another may require interactive SQL access to HDFS technology. Therefore, the Data Lake must offer data processing options or a toolkit to address diverse use cases without creating additional data silos.

Calculate your savings with Pivotal Big Data Suite compared to data management in an Enterprise Data Warehouse.

RSA and Pivotal: Laying the Foundation for a Wider Big Data Strategy

Building from years of security expertise, RSA was able to exploit Big Data to better detect, investigate, and understand threats with its RSA Security Analytics platform launched last year. Similarly, Pivotal leveraged its world-class Data Science team in conjunction with its Big Data platform to deliver Pivotal Network Intelligence for enhanced threat detection using statistical and machine learning techniques on Big Data. Utilizing both RSA Security Analytics and Pivotal Network Intelligence together, customers were able to identify and isolate potential threats faster than competing solutions for better risk mitigation.

As a natural next step, RSA and Pivotal last week announced the availability of the Big Data for Security Analytics reference architecture, solidifying a partnership that brings together the leaders in Security Analytics and Big Data/Data science. RSA and Pivotal will not only enhance the overall Security Analytics strategy, but also provide a foundation for a broader ‘IT Data Lake’ strategy to help organizations gain better ROI from these IT investments.

RSA’s reference architecture utilizes Pivotal HD, enabling security teams to gain access to a scalable platform with rich analytic capabilities from Pivotal tools and the Hadoop ecosystem to experiment and gain further visibility around enterprise security and threat detection. Moreover, the combined Pivotal and RSA platform allows organizations to leverage the collected data for non-security use cases such as capacity planning, mean-time-to-repair analysis, downtime impact analysis, shadow IT detection, and more.



Distributed architecture allows for enterprise scalability and deployment

I spoke with Jonathan Kingsepp, Director of Federation EVP Solutions from Pivotal to discuss how the RSA-Pivotal partnership allows customers to gain much wider benefits across their organization.

1.  What are the technology components of this is this new RSA-Pivotal Reference architecture?

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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.


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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Pivotal Data Dispatch Shrinks The Big Data Productivity Gap

The data warehousing and business intelligence space has undergone a huge transformation in the past several years whereby business users are moving away from these traditional, ‘IT bottleneck’ environments to more agile ones driven by Big Data.  For example, when business users lobbied for self-service access, they got Tableau.  When they pressed for data discovery, they got Endeca.  What’s next?  An agile, yet controlled environment to satisfy both the business and IT community.   Pivotal Data Dispatch (Pivotal DD) fulfills the needs of all enterprise data stakeholders by empowering business users with on-demand access and analysis to Big Data – all under an established system of metadata and security defined by IT.


I spoke with Todd Paoletti, Vice President of Product Marketing at Pivotal to explain why Pivotal DD is the next Big Thing to hit the Big Data market.

1. Walk me through how Pivotal DD is used from the inception of a Big Data project and what issues it overcomes during a project lifecycle?

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