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.