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Building Architectures to Solve Business Problems
Raghunath Nambiar is a Distinguished Engineer at Cisco's Data Center Business Group. His current responsibilities include emerging technologies and big data strategy.
Ajay Singh, HortonworksAjay Singh is Director, Technology Alliances at Hortonworks. Ajay is responsible for design & validation of ecosystem solutions to optimally integrate, deploy & operate Hortonworks Data Platform.
Manankumar Trivedi, Cisco SystemsManan is a member of the solution engineering team focusing on big data infrastructure and performance. He holds masters of science degree from Stratford University.
Karthik Kulkarni, Cisco SystemsKarthik Kulkarni is a Technical Marketing Engineer at Cisco Data Center Business Group focusing on Big Data and Hadoop technologies.
The authors acknowledge contributions of Ashwin Manjunatha, and Sindhu Sudhir in developing the Cisco UCS Common Platform Architecture (CPA) for Big Data with Hortonworks Cisco Validated Design.
The CVD program consists of systems and solutions designed, tested, and documented to facilitate faster, more reliable, and more predictable customer deployments. For more information visit:
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This document describes the architecture and deployment procedures of Hortonworks Data Platform (HDP) on a 64 node cluster based Cisco UCS Common Platform Architecture (CPA) for Big Data. The intended audience of this document includes, but is not limited to, sales engineers, field consultants, professional services, IT managers, partner engineering and customers who want to deploy HDP on the Cisco UCS CPA for Big Data.
Hadoop has become a strategic data platform embraced by mainstream enterprises as it offers the fastest path for businesses to unlock value in big data while maximizing existing investments. The Hortonworks Data Platform (HDP) is a 100% open source distribution of Apache Hadoop that is truly enterprise grade having been built, tested and hardened with enterprise rigor. The combination of HDP and Cisco UCS provides industry-leading platform for Hadoop based applications.
The Cisco UCS solution for HDP is based on Cisco Common Platform Architecture (CPA) for Big Data , a highly scalable architecture designed to meet a variety of scale-out application demands with seamless data integration and management integration capabilities built using the following components:
• Cisco UCS 6200 Series Fabric Interconnects —provide high-bandwidth, low-latency connectivity for servers, with integrated, unified management provided for all connected devices by Cisco UCS Manager. Deployed in redundant pairs, Cisco fabric interconnects offer the full active-active redundancy, performance, and exceptional scalability needed to support the large number of nodes that are typical in clusters serving Big Data applications. Cisco UCS Manager enables rapid and consistent server configuration using service profiles and automation of the ongoing system maintenance activities such as firmware updates across the entire cluster as a single operation. Cisco UCS Manager also offers advanced monitoring with options to raise alarms and send notifications about the health of the entire cluster.
• Cisco UCS 2200 Series Fabric Extenders —extends the network into each rack, acting as remote line cards for fabric interconnects and providing highly scalable and extremely cost-effective connectivity for a large number of nodes.
• Cisco UCS C-Series Rack-Mount Servers —Cisco UCS C240M3 Rack-Mount Servers are 2-socket servers based on Intel Xeon E-2600 series processors and supporting up to 768 GB of main memory. 24 Small Form Factor (SFF) disk drives are supported in performance optimized option and 12 Large Form Factor (LFF) disk drives are supported in capacity option, along with 4 Gigabit Ethernet LAN-on-motherboard (LOM) ports.
• Cisco UCS Virtual Interface Cards (VICs) —the unique Cisco UCS Virtual Interface Cards incorporate next-generation converged network adapter (CNA) technology from Cisco, and offer dual 10Gbps ports designed for use with Cisco UCS C-Series Rack-Mount Servers. Optimized for virtualized networking, these cards deliver high performance and bandwidth utilization and support up to 256 virtual devices.
• Cisco UCS Manager —resides within the Cisco UCS 6200 Series Fabric Interconnects. It makes the system self-aware and self-integrating, managing the system components as a single logical entity. Cisco UCS Manager can be accessed through an intuitive graphical user interface (GUI), a command-line interface (CLI), or an XML application-programming interface (API). Cisco UCS Manager uses service profiles to define the personality, configuration, and connectivity of all resources within Cisco UCS, radically simplifying provisioning of resources so that the process takes minutes instead of days. This simplification allows IT departments to shift their focus from constant maintenance to strategic business initiatives.
The Hortonworks Data Platform (HDP) is an enterprise-grade, hardened Apache Hadoop distribution that enables you to store, process, and manage large data sets.
Apache Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed for high-availability and fault-tolerance, and can scale from a single server up to thousands of machines.
The Hortonworks Data Platform combines the most useful and stable versions of Apache Hadoop and its related projects into a single tested and certified package. Hortonworks offers the latest innovations from the open source community, along with the testing and quality you expect from enterprise-quality software.
The Hortonworks Data Platform is designed to integrate with and extend the capabilities of your existing investments in data applications, tools, and processes. With Hortonworks, you can refine, analyze, and gain business insights from both structured and unstructured data - quickly, easily, and economically.
With the Hortonworks Data Platform, enterprises can retain and process more data, join new and existing data sets, and lower the cost of data analysis. Hortonworks enables enterprises to implement the following data management principles:
•Retain as much data as possible—Traditional data warehouses age, and over time will eventually store only summary data. Analyzing detailed records is often critical to uncovering useful business insights.
•Join new and existing data sets—Enterprises can build large-scale environments for transactional data with analytic databases, but these solutions are not always well suited to processing nontraditional data sets such as text, images, machine data, and online data. Hortonworks enables enterprises to incorporate both structured and unstructured data in one comprehensive data management system.
•Archive data at low cost—It is not always clear what portion of stored data will be of value for future analysis. Therefore, it can be difficult to justify expensive processes to capture, cleanse, and store that data. Hadoop scales easily, so you can store years of data without much incremental cost, and find deeper patterns that your competitors may miss.
•Access all data efficiently—Data needs to be readily accessible. Apache Hadoop clusters can provide a low-cost solution for storing massive data sets while still making the information readily available. Hadoop is designed to efficiently scan all of the data, which is complimentary to databases that are efficient at finding subsets of data.
•Apply data cleansing and data cataloging—Categorize and label all data in Hadoop with enough descriptive information (metadata) to make sense of it later, and to enable integration with transactional databases and analytic tools. This greatly reduces the time and effort of integrating with other data sets, and avoids a scenario in which valuable data is eventually rendered useless.
•Integrate with existing platforms and applications—There are many business intelligence (BI) and analytic tools available, but they may not be compatible with your particular data warehouse or DBMS. Hortonworks connects seamlessly with many leading analytic, data integration, and database management tools.
The Hortonworks Data Platform is the foundation for the next-generation enterprise data architecture - one that addresses both the volume and complexity of today's data.
The current version of the Cisco UCS CPA for Big Data offers two options depending on the compute and storage requirements:
•High Performance Cluster Configuration—offers a balance of compute power with IO bandwidth optimized for price and performance. It is built using Cisco UCS C240M3 Rack-Mount Servers powered by two Intel Xeon E5-2665 processors (16 cores) with 256 GB of memory and 24 1TB SFF disk drives.
•High Capacity Cluster Configuration—optimized for low cost per terabyte, is built using Cisco UCS C240M3 Rack-Mount Servers powered by two Intel Xeon E5-2640 processors (12 cores) with 128GB memory and 12 3TB LFF disk drives.
Note This CVD describes the installation process for a 64-node High Performance Cluster configuration.
The High Performance Cluster configuration consists of the following:
•Two Cisco UCS 6296UP Fabric Interconnects
•Eight Cisco Nexus 2232PP Fabric Extenders (two per rack)
•64 Cisco UCS C240M3 Rack-Mount Servers (16 per rack)
•Four Cisco R42610 standard racks
•Eight vertical power distribution units (PDU) (country specific)
Each rack consists of two vertical PDU. The master rack consists of two Cisco UCS 6296UP Fabric Interconnects, two Cisco Nexus 2232PP Fabric Extenders and sixteen Cisco UCS C240M3 Servers, connected to each of the vertical PDUs for redundancy; thereby, ensuring availability during power source failure. The expansion racks also consists of two Cisco Nexus 2232PP Fabric Extenders and sixteen Cisco UCS C240M3 Servers are connected to each of the vertical PDUs for redundancy; thereby, ensuring availability during power source failure, similar to master rack.
Note Contact your Cisco representative for country specific information.
Table 1 and Table 2 describe the rack configurations of rack 1 (master rack) and racks 2-4 (expansion racks).
