As the market moves toward cloud-based big data and analytics, three qualities emerge as vital for success. Services must be easy, unified, and enterprise grade.
Easy: Certainly no one goes out looking for a harder way to do their job. Cloud IaaS facilitates resource self-service provisioning, eliminating the hassles of procurement and deployment on-premises. Cloud PaaS takes this a step further and allows users to focus directly on building data pipelines, training machine learning models, developing analytics applications—placing all the effort on creation efforts rather than infrastructure operations. Self-service end-user-focused tools accelerate daily tasks like job submission, performance tuning, and workload analytics. Intelligent defaults and built-in logic eliminate much of the guess work. The net result is much improved productivity for data engineers, data scientists, and analysts.
Unified: Conceptually, the cloud sounds like a single place to host diverse, data-intensive functions. In practice, many services end up reproducing the silos that existed on-premises. A far superior approach is to truly consolidate data in one persistent object store and then bring different applications and workloads to bear against that set. Fragmented services lead to fragmented controls, when in actuality, what people really want is a common platform and control plane to manage everything, even across hybrid- and multicloud deployments. An advantageous side benefit of this unified approach is lower total cost of ownership, stemming from eliminating redundant data storage, leveraging transient compute, and simplifying management overhead.
Enterprise grade: Perhaps this goes without saying, but enterprises need cloud to be every bit as robust as their traditional approaches. To be acceptable, cloud analytics platforms must meet or exceed corporate requirements around security, governance, and management. Central control for role-based access, authentication, authorization, encryption, keys—all are required to pass audits and show compliance. The ability to discover and define metadata definitions for the business is a critical enabler for self-service functions, not least because businesses will want a platform that has been proven out in the market by their most demanding peers.
Having so many cloud-based analytics services available is a dream come true. However, it’s a nightmare to manage proper security and governance across all those different services. Nikki Rouda and Nick Curcuru share practical advice on how to minimize the risk and effort in protecting and managing data for multidisciplinary analytics and explain how to avoid the hassle and extra cost of siloed approaches.
Nikki Rouda is the cloud and core platform director at Cloudera. Nik has spent 20+ years helping enterprises in 40+ countries develop and implement solutions to their IT challenges. His career spans big data, analytics, machine learning, AI, storage, networking, security, and the IoT. Nik holds an MBA from Cambridge and an ScB in geophysics and math from Brown.
Nick Curcuru is vice president of enterprise information management at Mastercard, where he’s responsible for leading a team that works with organizations to generate revenue through smart data, architect next-generation technology platforms, and protect data assets from cyberattacks by leveraging Mastercard’s information technology and information security resources and creating peer-to-peer collaboration with their clients. Nick brings over 20 years of global experience successfully delivering large-scale advanced analytics initiatives for such companies as the Walt Disney Company, Capital One, Home Depot, Burlington Northern Railroad, Merrill Lynch, Nordea Bank, and GE. He frequently speaks on big data trends and data security strategy at conferences and symposiums, has published several articles on security, revenue management, and data security, and has contributed to several books on the topic of data and analytics.
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