Interview with Jack Norris, SVP, Data & Applications, MapR Technologies
The term data fabric is increasingly entering the discussion when it comes to processing, storing and analyzing data. However, the definition and focus of a data fabric differs. How does MapR define data fabric?
JN: MapR takes it from a whole cloth perspective for a data fabric that represents a converged view of how to have a scalable, flexible solution that converges capabilities across data types, processing, and locations including cloud and on-premise. MapR provides the mechanism for customers to build a unified data fabric that stretches from edge devices, into the data center, and across public clouds.
This is in contrast to ETL vendors who define data fabric in terms of the connected movement and transformation of data between sources and destinations, or storage vendors are recasting some of their solutions in terms of data fabric that stretches their current capabilities. Each of these solutions is targeted at a different function and level in an organization from data administrators to storage administrators to enterprise architects.
What value does a data fabric offer?
JN: The main reason for a data fabric is to enable organizations to pursue digital transformation. Transformation is not limited to a single application or a single data source. It is the culmination of many, many different applications running in on top of a multi-tenant, secure, data fabric. For many organizations transforming the customer experience or driving business excellence requires the injection of intelligence into operational applications. MapR introduced MapR-XD to allow users to create global data fabrics which are inherently ready for analytical and operational applications. The data fabrics created with the MapR-XD layer encompass multiple standard interfaces including HDFS, NFS, REST, POSIX, and will support analytical and operational workloads including batch, interactive, or streaming.
What was the impetus for MapR to develop a data fabric solution?
JN: How organizations storage, manage, and process data are growing through the biggest paradigm shift, we’ve seen in thirty years. With the exponential growth of data volumes and the limitations of rigid infrastructures, moving data and integrating analytics with operational processes is becoming increasingly difficult. Data is contained in separate silos that make it even more difficult to derive meaning and intelligence from valuable data. This leads to high costs of processing and storing of data, and these costs only continue to grow with the data volumes. We were hearing from our customers that they needed a scalable data platform that offers support for multiple data centers, cloud solutions and edge locations in one global namespace.
They recognized that today’s storage and data management technologies were not designed to take advantage of distributed computing environments, cloud infrastructures, containers and virtualization, and IoT. Moreover, the ability to analyze the data contained across separate silos consists of processing difficulties and delays. This was driving the need for a new data platform, a data fabric if you will that would also support intelligent applications that automate real-time operational decisions on the basis of deep analytical insights.
Can you provide some examples on how customers are benefitting from a data fabric solution?
JN: MapR-XD enables companies to unify, manage, and act on data rapidly, ultimately resulting in critical advantages. For financial services, speed in identifying potential fraudulent activity is critical for keeping data safe and customers protected. For retailers, the ability to provide rich context to customer increases their satisfaction and drives sales. For healthcare, this translates into better diagnostics and quality of care while reducing costs. 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. In addition, MapR-XD is also extensively used by enterprise organizations deploying across cloud platforms, because of its scale, reliability, and ability to host different applications across different user groups.
How does the MapR data fabric solution fit into your overall product platform?
JN: As part of the MapR Converged Data Platform, MapR-XD supports any data type from the edge to the data center and multiple cloud environments with automatic policy-driven tiering from hot, warm or cold data to enable customers to create global data fabrics which are ready to integrate analytical and operational applications. By providing a robust solution to manage data movement across multiple locations with security, high performance and multi-tenancy, MapR is providing a strategic solution for enterprises embarking on crafting and implementing a next gen-data strategy. The MapR Converged Data Platform enables a data fabric with a global view of data and metadata, supporting a wide diversity of data types for both analytics and operations.
What do CIO’s need to consider as the devise their data fabric strategy?
JN: CIOs should consider a data fabric as an important strategic platform to drive innovation and disrupt their 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. CIOs should also 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 easy integration of analytics with operations, adding new applications, and deploying new models. Because it is protecting of your most important corporate assets, a data fabric also needs to provide enterprise-grade data protection, reliability and availability while dramatically simplifying data lineage, governance and security.