Cisco Connected Streaming Analytics

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1 Data Sheet Cisco Connected Streaming Analytics Make more informed decisions in real time. Know what is happening in your business the moment it occurs, so you can take immediate action based on insights from highvelocity streams of live data from multiple sources. The value of data increases when it can be used proactively, and in real time, to promote actionable events for modern business processes. Data is created quickly and in the farthermost places, whether it is from Internet-ofThings (IoT) devices, appliances, transactions, or mobile usage. Yet there is still a need to reach and gain insights from all this information. That means you have to analyze data in new places and in a very distributed manner, but traditional analysis is usually done much, much later on centralized data. Streaming analytics solutions are able to capture perishable insights about real-time data that bring immediate context to all IoT, mobile, web, and enterprise apps. About Cisco Connected Streaming Analytics Cisco Connected Streaming Analytics (CSA) is an analytics platform that delivers predictive, actionable insights from high-velocity streams of live data from multiple sources, enabling real-time governance and immediate actions. It applies to use cases across a wide range of industries and business applications and adapts to customer environments: Streaming query processing supports active, continuous monitoring of live data. This provides instantaneous, real-time analysis and action, as well as efficient use of compute resources. The platform s framework and interfaces are ideal for use case development across a wide variety of business and network management functions and IoT industries. Streaming analytics provides real-time insights with big data views that support actionable events and dynamic dashboards that help you get more value out of your data.

2 s and Benefits Cisco CSA offers streaming query processing that provides continuous monitoring of live data, real-time analysis and action, and efficient use of compute resources (see Figure 1 for details). This compares against traditional business intelligence processes, which store first and query later, resulting in long-latency query processing and no real-time analysis or action. These traditional processes are also compute-intensive. CSA provides the following features: High-performance, real-time analytics with high scalability and low latency Best-in-class collection, with native adapters and interfaces to multivendor data sources Reliability with high availability, failover, and online backup and restore Distributed and tiered options to deliver horizontal scale and localized data processing Connected Streaming Analytics delivers real-time analytics for infrastructure, machine, and telemetry data: Data sources can be streaming or on disk; stream-stream, stream-table and table-table joins are supported. Third-party business intelligence visualization tools are supported through ANSI SQL: Tableau, MicroStrategy, Qlik, Pentaho, and others. Hierarchical stream processing enables primary insights up and down from the edge to the cloud through analytics at every element and node. Figure 1. Cisco Connected Streaming Analytics: Real-Time Streaming Analytics

3 Table 1 lists additional features and benefits of Cisco Connected Streaming Analytics in detail. Table 1. Cisco Connected Streaming Analytics s and Benefits Big Data Support Support for high volume of data Support for high variety of data Support for high and variable velocity of data Support to automatically handle late arriving data Support for high volume of data Accepts terabytes of daily traffic and can be integrated with modern data archives to support full big data Accepts structured and unstructured data, batch data, static data, and data in motion to support full big data Accepts real-time, changing network data to support full big data Provides a transparent capability to automatically update results for both late-arriving and out-of-order data. Accepts terabytes of daily traffic and can be integrated with modern data archives to support full big data Data Collection Streaming data collector library IoT handlers Streaming data collector framework Integration with existing systems and databases Facilitates collection of data: Across many types of networks, including IP next-generation networks, mobile, and video From a variety of network elements: routers, switches, data center servers, and more From a wide variety of data types available from the network, including user location; user device IP address; authentication, authorization, and accounting (AAA) information; and more Cisco has developed a large library of collectors to bring multivendor data into the system, including NetFlow, syslog, Next-Generation Network-Based Application Recognition (NBAR2), Event Data Recorder (EDR), and Extensible Messaging and Presence Protocol (XMPP) data. Release 2.0 includes newly developed handlers: MTConnect, MQTT, and CoAP and Kafka handlers to address machine-to-machine (M2M) use cases. Allows for easy development of an unlimited variety of collectors, using a configuration file or an XML file, or writing simple Java code. This variety accommodates a multitude of network-centered use cases. Supports systems integration with existing business data systems. For example, customer relationship management (CRM), marketing, inventory, financial system, video content libraries, and more. This supports use case and application development that combines real-time and historical data. Data Processing PostgreSQL code base Standards-based data engine that manages and aggregates raw data High-performance data processing CSA Stream Engine (TruCQ) is built on PostgreSQL code base. The new upgrade allows for new PostgreSQL features to be available in CSA. This is a major technical milestone. The data engine enables continuous analysis of streaming data, including queries that combine those streams with other streaming data or with historical/staged data. Based on industry-standard ANSI SQL technology and best practices, data is continuously processed. These queries allow and provide for the identification of meaningful events within those streams, including detection of complex patterns, event correlation, and abstraction. Provides high-speed data processing that enables the timely utilization of big data. Exporting Data Data storage Actionable, real-time events Although some Cisco Connected Streaming Analytics real-time streaming processing use cases do not require data storage, many do. In those cases, the raw data, the processed data, and the query results can be stored locally or in an external database (data warehouse, Hadoop, and more) for long-term storage and later data processing. With most real-time use cases, the point of doing things in real time is often to take immediate action. When the continuous query engine identifies predefined patterns, an event can be generated and forwarded to another system for action. These events can be user-defined XML. Examples of systems that might consume actionable events generated by the Connected Streaming Analytics engine include: A policy server (PCRF) to affect quality of service (QoS), gating, and throttling A network management system for fault management escalations Location-based mobile ad insertion

4 Reporting Streamified best-in-class analytics modules Includes analytics modules: ARIMA, Logistic Regression, and Linear Regression. A core focus of these analytics is anomaly detection and forecasting on both structured and unstructured data. They enable real-time predictive analytics that can power vertical-specific applications to deliver actionable insights. Reliability High availability and failover for all components Online backup and restore Integrated with Red Hat Cluster Suite, providing high reliability. Provides for the ability to do backup and restore without system interruption. Security Controls Secure Sockets Layer (SSL)/Transport Layer Security (TLS) protocol support Provides SSL/TLS database connections for secure authentication and data transmission. Integration and Customization/Application Building REST API framework Enabling product development and services teams to create at the edge/fog real-time IoT solutions. Includes data and management APIs to facilitate interactions with TruCQ. Open APIs and standard tools for customers and third parties to develop custom applications Cisco Connected Streaming Analytics is an open, standards-based data management and data processing platform. Developers can use standard tools and APIs such as SQL, XML, Java, and more to develop use cases, applications, and system integrations. Cisco or partner services for use case development No two service providers or enterprises have identical analytics needs: Markets are different. Data sources are different. Customers are different. Networks are different. Joint analysis (with Cisco or our analytics partners) is required to understand data sources, markets, and needs, and quantitative analysis is needed to determine models. Licensing Cisco Connected Streaming Analytics is a licensed software product that runs in containers (LXC, Docker, and more) and on virtual machines.. Cisco provides software, licenses, and customization/integration services. The software is licensed per application instance, with additional optional licenses available for add on handlers. For distributed environments, Cisco Connected Streaming Analytics may be deployed as regional collectors, each providing summary data to a central system. The regional collectors do not support dashboards or outbound event interfaces. A Cisco Connected Streaming Analytics deployment must have at least one central system. System Requirements Table 2 lists the hardware requirements for each server where you will install Cisco Connected Streaming Analytics, and Table 3 lists the operating environment requirements. Connected Streaming Analytics also supports clustering using Red Hat Enterprise Linux clustering. Table 2. Hardware Requirements for Cisco Connected Streaming Analytics 2.0 Core Physical memory Minimum: 8-core Intel/AMD 64-bit (x86_64) production: 16-core Intel/AMD 64-bit (x86_64) 8 GB minimum: 16 GB

5 Recommended: 32 GB Shared memory 4 GB Table 3. Operating Environment Requirements for Cisco Connected Streaming Analytics 2.0 Operating system Red Hat Enterprise Linux (RHEL) 6.5, 6.6, and 6.7 Web browser Internet Explorer 10 and 11 Firefox Extended Support Release 24.x and later versions Java Java 1.8 Google Chrome 44 Services and Support Using the Cisco lifecycle services approach, Cisco and its partners provide a broad portfolio of end-to-end services and support that can help increase your network's business value and return on investment. This approach defines the minimum set of activities needed, by technology and by network complexity, to help you successfully deploy and operate Cisco technologies and optimize their performance throughout the lifecycle of your network. Contact the Smart Services Bureau for more information. Industry Recognition CSA as a standalone streaming analytics platform is rated as a strong player by The Forrester Wave: Big Data Streaming Analytics, Q (PDF, 385 KB). For More Information For more information, visit cisco.com/go/streaminganalytics or the CSA DevNet page or contact your account representative. Printed in USA 07/16

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