I recently caught up with Jack Norris, Senior Vice President of Data and Applications at Mapr, to discuss how storage and data management are in the midst of a generational re-platforming to leap forward into the digital age and how “data fabric” technology is taking a leading role. Jack drives understanding and adoption of new applications enabled by data convergence. With over 20 years of enterprise software marketing experience, he has demonstrated success from defining new markets for small companies to increasing sales of new products for large public companies. Jack’s broad experience includes launching and establishing analytic, virtualization, and storage companies and leading marketing and business development for an early-stage cloud storage software provider. He has also held senior executive roles with EMC, Rainfinity (now EMC), Brio Technology, SQRIBE, and Bain and Company. Jack earned an MBA from UCLA Anderson and a BA in Economics with honors and distinction from Stanford University.
Daniel D. Gutierrez – Managing Editor, insideAI News
insideAI News: What trends are driving the move towards a converged data fabric?
Jack Norris: Storage and data management are in the midst of a generational re-platforming to leap forward into the digital age. How we store, process and analyze data is going through a tremendous change to support this digital transformation. There are many trends that can be viewed in isolation that are part of this seismic shift including big data, cloud, containers, microservices and deep learning. Each of these trends puts pressure on existing storage silos that pose an obstacle to the shared data requirements required to address these trends. A converged data fabric, therefore, is both a consequence and an enabler of these trends.
insideAI News: The term “data fabric” is now increasingly entering the discussion when it comes to the processing, storing and analyzing of data. However, the definition and focus of a data fabric differs based on your perspective. What is yours?
Jack Norris: The term “data fabric” is being used to describe a broad range of approaches from traditional storage, to ETL tools, to big data architectures. Our definition is a “whole cloth” perspective that represents a scalable, flexible platform that converges capabilities across data types and across locations including cloud and on-premise.
insideAI News: What should CIOs consider before deciding on a data fabric strategy?
Jack Norris: A data fabric strategy is key to simultaneously reducing costs and developing innovations. So the first consideration is to make sure that a data fabric solution can effectively support existing applications. Standard file, database and POSIX support are required to easily take advantage of the high-scale, lower-cost data fabric. Secondly, digital transformation requires the integration of analytics with operations. A data fabric has to support real-time, event streams, and database transactions to effectively drive digital transformation.
insideAI News: MapR recently introduced MapR-XD Cloud-Scale Data Store with a converged data platform. What benefits does this type of data store deliver to customers?
Jack Norris: The MapR Platform enables customers to create a data fabric with a global view of data and metadata, supporting a wide diversity of data types for both analytics and operations. MapR-XD is a high-scale, reliable, globally distributed data store, delivering an organization’s data fabric for managing files, objects, and containers supporting the most stringent speed, scale, and reliability requirements within and across multiple edge, on-premises, and cloud environments. Some use cases include the following:
- For financial services, speed in identifying potential fraudulent activity is critical for keeping clients safe from cyber criminals. MapR-XD enables companies to unify, manage, and act on data rapidly, ultimately resulting in critical advantages.
- For data warehouse use cases, MapR is being used to drive consistent speed at scale, hosting multi-tenant applications, while maintaining the different tiers of data.
- MapR-XD is also extensively used by enterprise organizations building a cloud platform, because of its scale, reliability, and ability to host different applications across different user groups.
insideAI News: What specifically does MapR-XD include?
Jack Norris: MapR-XD is software for building intelligent applications with the MapR Converged Data Platform. MapR-XD includes the MapR multi-temperature Global Namespace and data management in the form of security, compression, snapshots, multi-tenancy, and self-healing.
insideAI News: How should enterprise architects, storage admins and CIOs get started deploying a data fabric?
Jack Norris: First, CIOs should consider a data fabric as an important strategic platform to drive innovation and disrupt industries. However, a data fabric can also be deployed tactically. Most organizations face budget pressures and CIOs are tasked with simultaneously decreasing costs while driving innovation. A data fabric is an invaluable technology to reduce costs by offloading data from expensive systems to drive down costs. Secondly, CIOs should look at data sources as events flows. Driving innovation is simplified through an integrated publish and subscribe environment supported by a converged data fabric. This enables the ability to easily integrate analytics with operations, add new applications, deploy new models. Thirdly, a data fabric that provides enterprise-grade data protection, reliability and availability dramatically simplifies data lineage, governance and security of your most important corporate assets.
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