White Paper: Enhancing Functionality and Security of Enterprise Data Holdings

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1 White Paper: Enhancing Functionality and Security of Enterprise Data Holdings Examining New Mission- Enabling Design Patterns Made Possible by the Cloudera- Intel Partnership Inside: Improving Return on Investment Enhancing Support to Enterprise Missions Dramatically Improving Data Security 1

2 About This Paper The partnership between Cloudera and Intel Corporation announced in 2014 enables a closer coordination of engineering and development activities over the full stack of technologies supporting modern enterprise analytic solutions. This paper, produced by three chief technology officers with extensive experience in fielding data solutions into government agencies, reviews some key developments resulting from this new engineering work and provides design considerations for enterprises seeking to modernize in ways that economically enhance functionality and security. The Hadoop-Based Enterprise Data Hub The open source Apache Hadoop framework has become the foundation of mission-focused data modernization activities throughout industry and government. This emerging platform has widespread community adoption and a large development community focused on continual improvement, with Cloudera leading the community through investing in new open source capabilities designed to meet enterprise mission needs. Cloudera s leadership in this data management platform provides a full spectrum of capability to execute analytic workloads over data holdings and establishes the foundations for the next generation in data management, the enterprise data hub (EDH). The functionality, security, and economies of an EDH are making this the preferred approach to modern data challenges. Now new capabilities in the chip, storage, and network products made by Intel are making exciting contributions to this EDH approach, including faster data processing, complete data encryption, and compression of all data. Intel and Cloudera Working Together In 2014 Intel and Cloudera jointly announced a cooperation that saw Intel making investments in Cloudera and also an intent to work with Cloudera to integrate Intel contributions into core Apache Hadoop projects and Cloudera s CDH. Together, Intel and Cloudera continue to drive innovation through open- source technologies with a focus on security, performance, governance, and mission- focused applications. Cloudera is working closely with Intel to ensure open source solutions like Hadoop make the best use of Intel server technologies. By empowering architects with hardware and software designed to work together, missions can be economically served with more actionable. Cloudera and Enterprise Data Management Cloudera is known for leveraging the best of the open source community to meet the needs of high performance and always-available data solutions. CDH and Cloudera 2

3 Enterprise form a data management platform that can store, process, and analyze all enterprise data. This enables the establishment of an enterprise data hub (EDH): a comprehensive platform that stores and retains data, in any state, form, or volume. The EDH enhances the ability of enterprises to correlate and fuse information. It also integrates with existing infrastructure and tools, and empowers a wide variety of workloads. An EDH from Cloudera provides the enterprise-required features of enhanced security, data provenance, auditing, access control, and cluster performance optimization necessary to bring big data computing to the entire organization. The attractiveness of this solution comes from its ability to meet mission needs in economical and efficient ways and an open design that ensures future missions can be supported without forklift upgrades. Characteristics of the platform include: Unified Approach: This platform provides one integrated system, bringing diverse users and application types to one pool of data and a common infrastructure. Critical data and derivative insights can be shared easily and are no longer locked away in data silos of distinct systems; older data frameworks required different databases for different types of data work, while an EDH hosts multiple application types. Streamlined Design: The EDH approach means no data movement is required. Organizations can store any amount or type of data, in original form, for as long as desired. Enable easy replay or rework of analytical processes and accelerate modeling and transformation pipelines Enhanced Security and Governance: The platform is built to enhance perimeter security, authentication, authorization, data lineage and data protection. Security is integrated with existing enterprise solutions. This is a compliance-ready solution, supportive of audit requirements involving data (including FISMA). Managed Environment: High availability, fault tolerance, self-healing storage, and automated backup and disaster recovery are built into the platform. Open Design: Open source to ensure data and applications remain free from vendor lock-in, and open architecture to ensure connections with all existing technology capabilities and future innovation. 3

4 Intel and The World Of Data Processing Servers built with Intel CPU s, 10/40 GbE networking, and storage technologies provide operational efficiencies that can improve the performance of most any solution. Several new features however, are especially important for data processing and analytics solutions. Intel processors enable faster computation and the ability to process more data elements at once via ongoing advances in CPU microarchitecture and core count. Additionally, Intel also includes technologies to increase data throughput and bandwidth with reduced latency. Furthermore, to address security concerns and regulatory requirements for Hadoop environments, Intel also supports the acceleration of encryption/decryption by offloading advanced encryption from software to the processor silicon. Intel Xeon Processor E5 v3 Family Support to 10/40 GbE networking Intel technologies for solid state drive (SSD) storage and high-speed (10GbE and 40GbE) networking communications systems further optimize the overall throughput of the system. Taken together, these technologies optimize the total infrastructure performance from the CPU to the edge of the network, resulting in dramatic performance results in Hadoop environments. These improvements have been demonstrated in benchmark tests. For example, in tests documented at hadoop.intel.com a 97% reduction in processing time was shown in terabyte sort evaluations. Elastic scaling to adopt to fluctuating workload ensures high performance Performance and I/O enhancements High bandwidth/low latency of input and output Rapid encryption and decryption to encourage pervasive data protection Error- correcting code (ECC) memory Analytics focused instruction set (SSE4) A Full-Stack Solution Focused on Performance and Security The review above of the powerful capabilities of an EDH and the increasing performance and new capabilities of Intel s technology lets us address the next logical question: What is now possible when solutions are engineered to take advantage of both of these capabilities? The most important outcomes of the well-engineered hardware and software EDH are mission-enabling outcomes. We capture some of these outcomes below in the categories of Computing Convergence, Systems Rationalization, and Accelerated Processing. Computing Convergence Fielding your EDH on a well-engineered hardware/software stack allows more workloads to be executed over shared data. Computing convergence also means processing and analytics can both be optimized for all these workloads. This convergence has many mission-focused implications: 4

5 Decision-makers in any organization can be provided with true 360-degree views on topics of interest since all relevant data, operational and historical, can be rapidly found, correlated, and visualized. Law enforcement organizations can reduce time to discovery in criminal investigations and discover new information relevant to cases, since data is now more easily correlated and different types of workloads can be conducted over all the data. Intelligence organizations can more quickly correlate adversary activity and make more reasoned assessments of adversary intentions since multiple sources of intelligence can now be analyzed at the same time. Intelligence can be done on activities and actors in their entirety versus just isolated events. Financial analysts and acquisition professionals can run their analytical queries faster and execute rapid iterative model building and testing within the same system. Cybersecurity analysis, fraud detection, and other solutions that look for anomalies can process all relevant data at once resulting in higher accuracy and fewer false positives. The advantage of full historical data analysis can be achieved. Sampling is focused on statistical needs rather than as a result of system constraints; analyzing all relevant data at once is now possible. Systems Rationalization Since an enterprise data hub approach means separate systems can be reduced (with multi-workloads possible over single repositories of all data), management time and financial resources can be optimized. This systems rationalization has many missionfocused impacts: Mission agility is improved since it is easier to find the right data (and the data is instantly available since it does not need to be retrieved/loaded/indexed). Data of all types (including imagery, video, text) can be used all at once. Agency agility is enhanced due to new ability of developers to field new solutions faster (since new solutions are built over a known pool of all the data and developers can use any number of computing tools and paradigms to find the best mix and fit for any conceivable workload). Any agency that has multiple data storage systems (i.e. all agencies) can significantly reduce the cost and management overhead of multiple separate systems. Accelerated Processing The combined benefits from optimized CPU performance, improved storage throughput, and reduced network infrastructure latency of the Intel Architecture result in performance benefits that allow users to transform the ways in which they operate. Some mission impacts: 5

6 Organizations can make more use of incoming data faster since it can be processed as it arrives and is stored. Incoming sensor data, for example, can be correlated and processed in real time to support ongoing operations. Higher performance provides improved time-to-action, enhancing support to agency missions when users get results at speeds that appear to be instantaneous, they can now ask iterative queries of very responsive systems. Faster processing means more data can be stored in secure and accessible ways utilizing encryption and compression. Among the many benefits of this approach is the ability to always have access to the data needed without having to retrieve data from archives or other data stores. Additional Considerations on Cost The fact that the storage and computing capabilities include increased security and performance as well as significant cost savings make fielding solutions based on this combined architecture particularly virtuous. Calculation of agency return on investment and savings will vary depending on your design and mission needs, but here are some factors that will play into your calculation: Significant storage savings are common, especially when compared legacy designs. Use of proven reference architectures result in faster fielding of a working solution, translating to lower costs to field. The reduction of cost to store and access data as a feature of an enterprise data hub means more data is available there is no need to leave any data behind. The time to retrieve (via backup storage including tape) is eliminated. Reduced time required to analyze data translates to reduced costs for manpower and, by extension, training, thereby allowing existing manpower to focus on interpretation and mission-specific outcomes. Reduction of silos of data results in savings over maintaining multiple systems, which translates to lower costs for storage, processors, and time to move and analyze data separately. 6

7 Strategic Considerations For Data Modernization Projects The capabilities of Cloudera s enterprise data hub and the well-engineered enhancements of Intel s server, networking, and storage infrastructure technologies lead to several design options that planners should consider early. Our recommendations for planners include: Understand and focus on current use cases: Our review of workloads above should help planners identify and clarify the most important/prioritized use cases as design goals for your project. Determining the prioritized data flows for the first use cases will help ensure success on a project is demonstrated early. Ensure the design focuses on output: Identify the analytical queries and algorithms required to generate desired outputs. This will enable the capturing of the advanced analytics requirements and interactive query needs the system must meet and ultimately dictate the rollout of a converged computing strategy. Assess your business rules for operating over the data and interacting with the solution. Agencies now have the capability to encrypt 100% of data and to assign and control access and audit it in new ways. And the applications and solutions that run over this data can also have access controlled by this end-toend solution. Plan for future expansion of use cases: First successes will be measured based on how well they meet current agency needs. But the power of a wellengineered enterprise data hub is that it can support many new use cases and future workloads. The key action in planning for expansion is to listen to the challenges faced by mission owners, and be prepared to iteratively incorporate into the solution new workloads and new data flows provided by them. Consider the full design: Consider compute, networking, data storage, and the software framework together as the data platform. Ensure to ask for design help. Repeatable patterns from other enterprises are available for reference. Engineers from Cloudera, Intel, and their partners can help refine and turn functional reference architecture into a technical design that will rapidly bring new functionality to the agency mission. Concluding Thoughts By utilizing the embedded capabilities of the Intel hardware ecosystem harnessed to the Cloudera enterprise data hub, agencies can realize efficiencies in performance, throughput, and, most importantly, security, in all areas of the architecture. This approach enables a high performance data hub with advanced means of serving business and mission objectives. 7

8 More Reading For more federal technology and policy issues visit: CTOvision.com- A blog for enterprise technologists with a special focus on Big Data. CTOlabs.com - A reference for research and reporting on all IT issues. J.mp/ctonews - Sign up for the government technology newsletters including the Government Big Data Weekly. About the Authors Bob Flores is a co-founder and partner at Cognitio. Bob spent 31 years at the Central Intelligence Agency. While at CIA, Bob held various positions in the Directorate of Intelligence, Directorate of Support, and the National Clandestine Service. He was the agency s Chief Technology Officer. Bob serves on numerous government and industry advisory boards Bob Gourley is a co-founder of Cognitio and editor and chief of CTOvision.com He is a former federal CTO. His career included service in operational intelligence centers around the globe where his focus was operational all source intelligence analysis. He was the first director of intelligence at DoD s Joint Task Force for Computer Network Defense, served as director of technology for a division of Northrop Grumman and spent three years as the CTO of the Defense Intelligence Agency. Bob serves on numerous government and industry advisory boards. Roger Hockenberry is a co-founder and partner and CEO at Cognitio. Following a two-decade career in industry first as a technology consultant and later as a management consultant and Managing Partner at Gartner Roger spent four-years in government service in the intelligence community where he was charged with driving the realization of the vision he had helped craft as a consultant. For More Information If you have questions or would like to discuss this report, please contact me. As an advocate for better IT use in enterprises I am committed to keeping this dialogue up open on technologies, processes and best practices that will keep us all continually improving our capabilities and ability to support organizational missions. Contact: Bob Gourley CTOlabs.com 8

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