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Keeping Up With Demand for Big Data Analytics

In my recent blogs about building the right data strategy and analytics solutions, I discussed how Cisco is helping our customers to meet one of the toughest challenges brought on by the Internet of Everything (IoE) – cost-effectively managing massive amounts of distributed data. With solutions such as Cisco Data Virtualization and Cisco Big Data Warehouse Expansion (BDWE), our customers can bring all of this data together in ways that are meaningful to them. Utilizing the network to securely connect data throughout the IoE and providing advanced analytics, we help our customers predict outcomes so they can drive better decisions in real-time.

To keep up with growing demands for big data analytics, now is the time to look at workload automation end to end. In this blog, I would like to share the other aspect of big data analytics; building the right workload automation strategy to integrate data, analytics and operations.

In today’s big data analytics environments, IT staff is regularly managing increasingly complex processes that are co-dependent on one another and span applications and departments. Rather than implementing silos of automation, we are helping customers take advantage of workload automation from a unified perspective. Our enterprise-wide workload automation solution, Cisco Tidal Enterprise Scheduler (TES), simplifies end-to-end data management and automates diverse business processes across a broad set of applications, systems and environments.

For example, many business intelligence and analytics applications operate 24 hours a day. To handle a high volume of these jobs – many with service-level agreements – customers need the tools to receive and respond to alerts from anywhere.  Using the Cisco Tidal Enterprise Scheduler client on their iPhones and iPads, our customers can receive alerts, check logs and remediate errors from anywhere. As organizations continue to require fast and timely business services, we put the power directly in the users’ hands. By using TES’ self-service portal, business users can monitor the progress of relevant workloads through a web browser and perform basic job control.  In addition to reducing the downtime of a business user having to wait for IT to address their issue, this also takes some of the burden off IT.

TES is a key piece of any company’s big data and analytics software strategy and a priority for us at Cisco. Cisco IT uses TES internally and, due to successful deployment, is now encouraging further widespread internal adoption. Since Cisco TES is the first workload automation solution in the industry to have a suite of Hadoop adapters (as well as other data source and application adapters), Cisco IT is now additionally leveraging TES in Cisco’s own internal Big Data initiatives to deliver an end-to-end big data workload solution -- as detailed here and here. We are continuously working to develop ways to integrate TES further with other Cisco Big Data solutions, such as the recently launched BDWE solution, to provide more value through holistic data management solutions.

Taking full advantage of big data delivers a tremendous competitive advantage to our customers.  Cisco TES facilitates the end-to-end management of the entire process from data acquisition, to value extraction, to action. Stay tuned as we continue to deliver new capabilities to further bridge today’s gap between enterprise IT capabilities and business requirements.

 

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To learn more about Cisco Tidal Enterprise Scheduler and the power of our integrated infrastructure for big data, check out our page.

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Enable Automated Big Data Workloads with Cisco Tidal Enterprise Scheduler

In our previous big data blogs, a number of my Cisco associates have talked about the right infrastructure, the right sizing, the right integrated infrastructure management and the right provisioning and orchestration for your clusters. But, to gain the benefits of pervasive use of big data,  you’ll need to accelerate your big data deployments and make a seamless pivot of your “back of the data center” science experiment into the standard data center operational processes to speed delivery of the value of these new analytics workloads.

If you are using a “free” (hint: nothing’s free), or open source workload scheduler, or even a solution that can manage day-to-day batch jobs, you may run into problems right off the bat. Limitations may come in the form of dependency management, calendaring, error recovery, role-based access control and SLA management.

And really, this is just the start of your needs for full-scale, enterprise-grade workload automation for Big Data environments! As the number of your mission-critical big data workloads increases, predictable execution and performance will become essential.

Lucky for you Cisco has exactly what you need! Read More »

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Announcing Cisco Tidal Enterprise Scheduler (TES) 6.2


Introducing New Adapter for Cisco UCS Manager, Lightweight Java Client, Multi-domain AD/LDAP Authentication & Runtime User Support for TES Workgroups   

Managing and automating mission-critical business process is key to the success of your enterprise. By managing the delivery of the right data to the right application at the right time you can ensure that everyone in your organization has the information they need to be successful.

UntitledEnd-to-end Workload Management with Tidal Enterprise Scheduler (TES) 6.2

With the release of TES 6.2, Cisco continues its leadership in workload automation (WLA) and job scheduling. With hundreds of in-production customers, running complex workloads for global trading exchanges, manufactures, health care providers and technology companies, Cisco TES has proven itself in some of the most demanding data centers in the world.

Already known and cited by analysts and customers for its ease-of-use, ease-of-installation, scalability and enterprise-wide visibility, Cisco TES 6.2 ships with major performance enhancements, added user management flexibility and greater infrastructure management reach. Enhancements in this release include:

Adapter for Cisco UCS Manager
The Enterprise Adapter for UCS Manager allows users to schedule UCS Manager component infrastructure jobs through Cisco TES so that users can leverage the scheduler’s capabilities to automate, simplify, and improve job scheduling and workload performance. The UCS Manager Adapter integrates with UCS Manager using the XML API and provides for the automation of UCS Manager activities for blade and rack-mount server management in the form of UCS Manager jobs. This allows you to control and manages server instances as part of an overall infrastructure and data processing workload management solution.

Untitled1Job Definitions for Cisco UCS Manager

Lightweight Java Client
For power users who are managing thousands of workloads and their associated objects in their database, the Java client syncs data directly from the Master, but it is many times faster than the client manager because all data is stored in-memory on the Java VM rather than to an external database. Many interactions through the Java client will see marked increases in performance including smooth scrolling with zero latency and faster search and filtering.

Untitled2Lightweight Java Client for TES 6.2

Flexible Security Feature
For large enterprises that segment globally or for any user who creates domains for their data center, Cisco TES now supports multi-domain coverage from a single client manager allowing greater flexibility and ease of use. And for greater runtime flexibility, Cisco TES allows users to associate runtime users to workgroups to be used while defining the workloads.

Cisco TES and Big Data
TES supports end-to-end workloads that include Big Data business processes. This release strengthens our support for Apache Hadoop, Cloudera as well as MapR. If you are traveling to San Jose for the Hadoop Summit next week stop by our booth (Cisco is a Platinum Sponsor) to see the latest Cisco solution designed specifically for the Hadoop platform. See Scott Ciccone‘s blog post for more details.

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June 3-5, 2014 --San Jose, California

 

Questions? ask-tes@cisco.com

For more on the TES 6.2 release visit http://www.cisco.com/go/workloadautomation

Data Center blogs I follow:
Raghunath Nambiar
Scott Ciccone

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Cisco @StrataConf : Best Practices for Hadoop In Production-Panel Discussion

Hey data heads! O’Reilly Strata Conference kicks off tomorrow at the Santa Clara Convention Center. If you plan to go you better have tickets because the event is sold out.

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If you are attending the three days of sessions, keynotes, receptions and exhibits, please stop by the MapR booth for some face time with one of our big data partners and plan to attend their sponsored panel discussion which includes Cisco IT and other MapR customers facilitated by Mike Gualtieri from Forrester Research.

Best Practices for Hadoop In Production

“Mike Gualtieri, principal analyst at Forrester Research, Inc., will engage a panel of production Hadoop users – including Cisco IT, The Climate Corporation, The Rubicon Project, and Solutionary – to discuss the challenges and best practices for deploying Hadoop in production. Join us for an engaging conversation on tips and tricks in deploying Hadoop in production. 10:40am, Wednesday February 12 in Ballroom H” See more here.

Representing Cisco IT will be Distinguished Engineer, Piyush Bhargava. Piyush is Chief Architect for Data Architecture & Innovation and is responsible for finding new ways of harnessing value from data and for designing architectures to support data processing, analytics & data science needs.

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If you can’t make it to Strata Conference or are double booked during this time slot you can get the goods on this topic from a number of other sources.

  • First, check out the webinar Cisco and MapR produced with Mike that highlights the Cisco IT case study. Mike outlines the 7 Best Practices for Productionizing Hadoop, MapR follows with their best practices for data tables and Cisco IT covers their big data use case. If you want to skip to the Cisco IT use case, it starts at 34:00.
  • Then, take a look at my blog post on the Cisco IT big data platform success story. Cisco IT has standardized on Cisco UCS servers and fabric interconnects for its big data infrastructure and uses MapR for data tables.

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For operational workloads, Cisco IT has standardized company-wide on Cisco Tidal Enterprise Scheduler with its unique set of adapters that include API integrations to MapR, Hive and Sqoop for big data workload processing.

Cisco Tidal Enterprise Scheduler for Big Data

  • Next, spend some time reading the Unleashing IT Big Data Edition. This special edition of Unleashing IT is all about big data and highlights our joint Cisco/MapR customer Solutionary who incidentally, will join Cisco IT on the Strata Conference panel discussion on Wednesday. This e-zine is packed full of big data thought leadership; best practices for infrastructure and operations; customer success stories and include our partners Intel, MapR and others.
  • Finally, peruse the Cisco big data website for all the latest on our solutions, partners, services and resources to help you make the best informed decision on moving your big data platform from development to production.

Big Data blogs I follow:
Raghunath Nambiar
Scott Ciccone

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Big Data Essentials: Don’t Let Your Sandbox Turn Into Quicksand

Check out our new “on-demand” webinar: Big Data Essentials: Don’t Let Your Sandbox Turn to Quicksand

What you will learn:

  • How to manage data as an asset and transform data into business value.
  • Why Big Data projects fail and how to avoid common pitfalls.
  • How to optimize a Big Data supply chain.
  • What infrastructure is required to run and automate data workload processing.

Big Data has become the next big thing, not only for the promise of finding the “needle on the haystack” of untapped revenues, but also for the possibility of uncovering and delivering exciting new business models.

For IT, Big Data can be both exciting and daunting: By delivering analysis from huge amounts of data more quickly than with existing Business Intelligence (BI) tools, data center managers can deliver new services to the business end users; daunting because researching, selecting, buying, staffing and managing Big Data tools sets pretty much demand a separate data center.

We invited our partner Informatica to share its knowledge on managing complex data supply chains have produced this webinar with insight from leading analyst firm Gartner, that will help you align how you think about your data and the infrastructure that it runs on.

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By incorporating these the Big Data essentials into your planning you can prevent your sandbox from turning into quicksand and avoiding project delays and cost overruns.

This webinar consists of three parts:

Doug LaneyResearch VP at Gartner for business analytics, begins with a lesson in Infonomics – managing data as an asset, and the skills required for Big Data analytics.

John HaddadSenior Director Big Data Product Marketing at Informatica, explains how to turn these data assets into actionable information generating business value and developing a Big Data supply chain.

Ronnie RayDirector Product Management for Cisco Cloud and Systems Management, completes the discussion by describing a Big Data infrastructure and operations platform that will exceed your business service levels.

View the webinar here.

Many of our customers already agree: Cisco is delivering Big Data infrastructure and data workload processing management with the Cisco Common Platform Architecture (CPA). CPA is based on Cisco Unified Computing (UCS), Unified Fabric, and Unified Management. The Cisco Big Data solution delivers:

Integrated Server Management

Cisco UCS unifies computing, networking, management, virtualization, and storage access into a single integrated architecture. It’s an ideal platform for Big Data applications.

Integrated Network Management

The same Cisco network architecture can serve both traditional applications and databases and Big Data processing solutions.

Integrated Data Processing Management

Cisco provides workload automation that manages the flow of data between a wide variety of applications into and out of your Big Data processing environments.

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