Big data has become big business as businesses mine vast stores of data for insights that can help identify trends, predict behavior, and empower decision makers. And the Internet of Everything (IoE) is creating new analytic use cases and possibilities that were inconceivable just a few years ago.
Cisco’s rich portfolio of big data and analytics solutions can help you unlock your competitive edge:
- From strategy to infrastructure
- From edge device to data center
- From access to analysis
Cisco’s unique approach to big data and analytics will be on display February 17-20 at Strata+Hadoop World in San Jose, California. This is the easiest way to learn about these solutions directly from Cisco experts and also see how these offerings stack up compared to other vendors.
One-on-One Demonstration and Discussions
Stop by the Cisco booth (#831) to get a first hand look at several key offerings in Cisco’s portfolio including:
- Data and Connected Analytics Portfolio
- UCS Integrated Infrastructure for Big Data
- Kaon v-Rack® mounted switches, routers, servers, and storage products.
I’ll be there, along with a number of other Cisco subject-matter experts. We would enjoy learning about your challenges and exploring how, with the right hardware, software, consulting, and services, we can help you transform IoE data into actions that create new capabilities, richer experiences and unprecedented economic opportunities.
If you aren’t already registered, take advantage of use code “Cisco20” for a 20% discount on 2 Day and All Access Passes.
Learn about Connected Analytics in the Solution Showcase Theater
Cisco® Connected Analytics for Events, a cloud-based analytic solution that venue operators use to enhance fan experiences, improve advertising and promotion efforts, identify operational and security issues, and provides the foundation for Why Event Analytics Matter on Wednesday, February 18 at 5:35 PM in the Solutions Showcase Theater by Rohit Shrivastava, General Manager of Cisco’s Connected Analytics Business Unit.
Listen to Cisco’s Point of View on Big Data with Analytics in an IOE World
Harness the Power of Big Data with Analytics is the title of Cisco’s keynote session on Thursday, February 19, at 4:50 PM in Room 210 B/F. This presentation addresses the challenge organizations are experiencing due to unprecedented complexity in managing their data, with the rise of Big Data, Cloud and overall hyper connectivity of our world.
Cisco is building solutions to help our customers adopt Big Data solutions, solve business problems using Analytics, and harness the power of an intelligent infrastructure to provide highly differentiated Data and Analytics solutions. In this session, Mike Flannagan, General Manager of Cisco’s Data and Analytics Business Group, will provide an overview of these solutions, help demystify the relationship of Big Data and analytics and bring it to life through customer stories.
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Learn More from My Colleagues
Check out the blogs of Mala Anand, Mike Flannagan and Nicola Villa to learn more.
Tags: analytics, Big Data, Cisco, Hadoop, Internet of Everything, Strata
OpenSOC, an open source security analytics framework, helps organizations make big data part of their technical security strategy by providing a platform for the application of anomaly detection and incident forensics to the data loss problem. By integrating numerous elements of the Hadoop ecosystem such as Storm, Kafka, and Elasticsearch, OpenSOC provides a scalable platform incorporating capabilities such as full-packet capture indexing, storage, data enrichment, stream processing, batch processing, real-time search, and telemetry aggregation. It also provides a centralized platform to effectively enable security analysts to rapidly detect and respond to advanced security threats.
A few months ago we were really excited to bring OpenSOC to the open source community. Developing OpenSOC has been a challenging, yet rewarding experience. Our small team pushed the limits of what is possible to do with big data technologies and put a strong foundational framework together that the community can add to and enhance. With OpenSOC we strive to provide an open alternative to proprietary and often expensive analytics tools and do so at the scale of big data. Read More »
Tags: analytics, Big Data, Hadoop, Managed Security Services, MTD, OpenSOC, security
Want to get the most out of your big data? Build an enterprise data hub (EDH).
Big data is rapidly getting bigger. That in itself isn’t a problem. The issue is what Gartner analyst Doug Laney describes as the three Vs of Big Data: volume, velocity, and variety.
Volume refers to the ever-growing amount of data being collected. Velocity is the speed at which the data is being produced and moved through the enterprise information systems. Variety refers to the fact that we’re gathering information from multiple data sources such as sensors, enterprise resource planning (ERP) systems, e-commerce transactions, log files, supply chain info, social media feeds, and the list goes on.
Data warehouses weren’t made to handle this fast-flowing stream of wildly dissimilar data. Using them for this purpose has led to resource-draining, sluggish response times as workers attempt to perform numerous extract, load, and transform (ELT) functions to make stored data accessible and usable for the task at hand.
Constructing Your Hub
An EDH addresses this problem. It serves as a central platform that enables organizations to collect structured, unstructured, and semi-structured data from slews of sources, process it quickly, and make it available throughout the enterprise.
Building an EDH begins with selecting the right technology in three key areas: infrastructure, a foundational system to drive EDH applications, and the data integration platform. Obviously, you want to choose solutions that fit your needs today and allow for future growth. You’ll also want to ensure they are tested and validated to work well together and with your existing technology ecosystem. In this post, we’ll focus on selecting the right hardware.
The Infrastructure Component
Big data deployments must be able to handle continued growth, from both a data and user load perspective. Therefore, the underlying hardware must be architected to run efficiently as a scalable cluster. Important features such as the integration of compute and network, unified management, and fast provisioning all contribute to an elastic, cloud-like infrastructure that’s required for big data workloads. No longer is it satisfactory to stand up independent new applications that result in new silos. Instead, you should plan for a common and consistent architecture to meet all of your workload requirements.
Big data workloads represent a relatively new model for most data centers, but that doesn’t mean best practices must change. Handling a big data workload should be viewed from the same lens as deployments of traditional enterprise applications. As always, you want to standardize on reference architectures, optimize your spending, provision new servers quickly and consistently, and meet the performance requirements of your end users.
Cisco Unified Computing System to Run Your EDH
The Cisco Unified Computing System™ (Cisco UCS®) Integrated Infrastructure for Big Data delivers a highly scalable platform that is proven for enterprise applications like Oracle, SAP, and Microsoft. It also provides the same required enterprise-class capabilities–performance, advanced monitoring, simplification of management, QoS guarantees–to big data workloads. With lower switch and cabling infrastructure costs, lower power consumption, and lower cooling requirements, you can realize a 30 percent reduction in total cost of ownership. In addition, with its service profiles, you get fast and consistent time-to-value by leveraging provisioning templates to instantly set up a new cluster or add many new nodes to an existing cluster.
And when deploying an EDH, the MapR Distribution including Apache™ Hadoop® is especially well-suited to take advantage of the compute and I/O bandwidth of Cisco UCS. Cisco and MapR have been working together for the past 2 years and have developed Cisco-validated design guides to provide customers the most value for their IT expenditures.
Cisco UCS for Big Data comes in optimized power/performance-based configurations, all of which are tested with the leading big data software distributions. You can customize these configurations further, or use the system as is. Utilizing one of Cisco UCS for Big Data’s pre-configured options goes a long way to ensuring a stress-free deployment. All Cisco UCS solutions also provide a single point of control for managing all computing, networking, and storage resources, for any fine tuning you may do before deployment or as your hub evolves in the future.
I encourage you to check out the latest Gartner video to hear Satinder Sethi, our VP of Data Center Solutions Engineering and UCS Product Management, share his perspective on how powering your infrastructure is an important component of building an enterprise data hub.
In addition, you can read the MapR Blog, Building an Enterprise Data Hub, Choosing the Foundational Software.
Let me know if you have any comments or questions, or via twitter at @CicconeScott.
Tags: Big Data, blade server, blades servers, C240 M3 Rack Server, Cisco UCS, Cisco Unified Computing System, Cisco Unified Data Center, Cisco Unified Fabric, Enterprise Data Hub, Gartner, Hadoop, MapR, rack server, UCS Central, UCS service profiles
More data allows for better and more expansive analysis. And better analysis is a critical success factor for businesses today.
But most data warehouses use the once-in-never-out principle when storing data. So whenever new business activities occur, new data is added without removing old data to make room. New data sources, such as data from social media networks, open data sources, and public web services further expand the warehouse. Unfortunately, all this growth comes at a cost.
Is there a way you can have your cake and eat it too?
With Hadoop and Cisco Big Data Warehouse Expansion, you can.
Disadvantages of More Data
While everyone understands the business advantage that can be derived from analyzing more data, not everyone understands the disadvantages that can occur including:
- Expensive data storage: Data warehouse costs include hardware costs, management costs, and database server license fees. These grow in line with scale.
- Poor query performance: The bigger the database tables, the slower the queries.
- Poor loading performance: As tables grow, loading new data also slows down.
- Slow backup/recovery: The larger the database, the longer the backup and restore process.
- Expensive database administration: Larger databases require more database administration including tuning and optimizing the database server, the tables, the buffer, and so on.
Three Options to Control Costs
The easiest way to control data warehouse costs is to simply remove data, especially the less-frequently used or older data. But then this data can no longer be analyzed.
Another option is to move the lesser-used data to tape. This option provides cost savings, and in an emergency, the data can be reloaded from tape. But analysis has now become EXTREMELY difficult.
The third option is to offload lesser-used data to cheaper online data storage, with Hadoop the obvious choice. This provides a 10x cost savings over traditional databases, while retaining the online access required for analysis.
This is the “have your cake and eat it too” option.
The Fast Path to Transparent Offloading
Cisco provides a packaged solution called Cisco Big Data Warehouse Expansion, which includes the data virtualization software, hardware, and services required to accelerate all the activities involved in offloading data from a data warehouse to Hadoop.
And to help you understand how it works, Rick van der Lans, data virtualization’s leading independent analyst, recently wrote a step-by-step white paper, Transparently Offloading Data Warehouse Data to Hadoop using Data Virtualization, that explains everything you need to do.
Read The White Paper
Download Transparently Offloading Data Warehouse Data to Hadoop using Data Virtualization here.
To learn more about Cisco Data Virtualization, check out our page.
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Tags: Cisco Big Data Warehouse Expansion, Cisco Data virtualization, data analytics, data virtualization, Data Warehouse, Hadoop, rick van der lans
There is a great debate in the security world right now: have SIEM and logging products run their course? Will Hadoop ride to the rescue? Can machines “learn” about security and reliably spot threats that no other approach can find?
Gartner calls this phenomenon Big Data Security Analytics, and they make a strong point to define BDSA solutions as a three-layer pyramid. At the bottom is the “data lake,” which is what most people equate with Hadoop. The next layer is context—the addition of relevant business, location, and other non-traditional security information to increase the precision of the next layer: applications and analytics (such as Machine Learning). It is this top layer where the real value of BDSA is realized in terms of finding new threats and remediating them before they do damage.
Read More »
Tags: big data analytics, Cisco EIR, Cisco Entrepreneurs in Residence, Hadoop, PetaSecure, security