On June 20th, Cisco and MapR will join with Forrester Research Big Data analyst Mike Gualtieri to discuss “productionizing” Hadoop. But what does it mean?
Mike has developed a list of 7 architectural best practices that will help your enterprise quickly, and easily develop or move your Hadoop environment into standard data center processes. Following his guidelines, your can get your Hadoop environment up and running in no time, saving time by being proactive on the headaches and pitfalls that are unique to Big Data environments.
Joining Mike will be MapR CMO, Jack Norris discussing their best practices and how they line up with the Big 7 from Forrester.
Finally, Cisco IT will showcase a MapR production environment and how they have streamlined the complex Big Data workloads, automatically moving data into and running analytics out of their Hadoop environment.
Keeping the Hadoop production environment up and running smoothly is the name of the game here and in the face of resource constraints, Cisco IT has standardized on Cisco Tidal Enterprise Scheduler—with its seamless integrations into MapR, Hive, and Sqoop—giving your enterprise the ability to “productionize” complex workloads from any data source.
Join us as we walk you through the 7 architectural best practices for Big Data, MapR and Cisco Tidal Enterprise Scheduler.
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Tags: Big Data, cisco live, forrester, Hadoop, MapR, Tidal Enterprise Scheduler, unified management, workload automation
Guest Blog by Jack Norris
Jack is responsible for worldwide marketing for MapR Technologies, the leading provider of a enterprise grade Hadoop platform. He has over 20 years of enterprise software marketing experience and has demonstrated success from defining new markets for small companies to increasing sales of new products for large public companies. Jack’s broad experience includes launching and establishing analytic, virtualization, and storage companies and leading marketing and business development for an early-stage cloud storage software provider.
Big Data use cases are changing the competitive dynamics for organizations with a range of operational use cases. Operational intelligence refers to applications that combine real-time, dynamic, analytics that deliver insights to business operations. Operational intelligence requires high performance. “Performance” is a word that is used quite liberally and means different things to different people. Everyone wants something faster. When was the last time you said, “No, give me the slow one”?
When it comes to operations, performance is about the ability to take advantage of market opportunities as they arise. To do this requires the ability to quickly monitor what is happening. It requires both real-time data feeds and the ability to quickly react. The beauty of Apache Hadoop, and specifically MapR’s platform, is that data can be ingested as a real-time stream; analysis can be performed directly on the data, and automated responses can be executed. This is true for a range of applications across organizations, from advertising platforms, to on-line retail recommendation engines, to fraud and security detection.
When looking at harnessing Big Data, organizations need to realize that multiple applications will need to be supported. Regardless of which application you introduce first, more will quickly follow. Not all Hadoop distributions are created equal. Or more precisely, most Hadoop distributions are very similar with only minor value-added services separating them. The exception is MapR. With the best of the Hadoop community updates coupled with MapR’s innovations, the broadest set of applications can be supported including mission-critical applications that require a depth and breadth of enterprise-grade Hadoop features.
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Tags: Big Data, enterprise scheduler, Hadoop, informatica, job scheduling, MapR, Tidal Enterprise Scheduler, UCS, workload automation
It’s close to 11 p.m. on the last day of the quarter in a large corporation. IT gets an urgent request to postpone a closing of the books process because there’s a large order stuck in the CRM system.
This means that the order won’t hit the books and be recorded as a booking. The customer won’t get her order, the salesperson won’t get paid, and finance will show a missing number.
This generates an urgent call to the team that manages the workload automation platform: Hold the closing workflow! Stop the presses!
The admins have to get to their console to find the job and pause it. Not a huge deal, except there are thousands of jobs to be run and hundreds of business people calling on a regular basis, at all kind of hours.
Some customers have created help desks for their workload automation teams or they may even off-shore the call center to serve these kinds of requests.
No more. Introducing self-service for workload automation.
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Tags: data center, intelligent automation, job scheduling, orchestration, Tidal Enterprise Scheduler, unified management, workload automation
Last night we uploaded version 6.1 of Cisco’s Tidal Enterprise Scheduler. I’m pretty excited to introduce the new functionality of this tool and there’s a lot. Particularly with Hadoop support and Amazon EC and S3 support as well. If you are unfamiliar with TES, the datasheet is here.
But when talking about big data, I thought, I’d start small. Like iPhone small. Existing Scheduler customers and the curious, can download the free Apple iPhone app to control jobs. Here’s the AppStore description and link
Cisco Enterprise Scheduler is the premiere job scheduling and process automation software that provides a single point of control and monitoring for business operations. Enterprise Scheduler for iOS now allows Scheduler administrators and users to monitor and control their operations directly on their mobile devices. Enterprise Scheduler for iOS was designed for the mobile user experience, but retains core features of the Enterprise Scheduler web client that users are familiar with including:
* Monitor and view jobs, connections, events, schedules, queues, logs and alerts.
* Control all aspects of jobs, including holding, rerunning, canceling, and overriding jobs.
* Powerful search and filtering for all Scheduler objects.
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Tags: cisco enterprise scheduler, Hadoop, intelligent automation, iphone, Tidal Enterprise Scheduler, unified management, workload automation
In my last installment on Cisco Tidal Enterprise Scheduler, I talked about how our customers have increased their usage of this end-to-end solution by 2-3x.
Based on the upswing of Big Data usage and adoption in the market, this trend is likely to continue for quite some time. And Cisco finds itself with the right solution at the right time.
I’m live here from Strata Conn/Hadoop World 2012 in New York City where “Strata Conference explores the changes brought to technology and business by big data, data science, and pervasive computing. This year, Strata has joined forces with Hadoop World to create the largest gathering of the Apache Hadoop community in the world.” It’s THE place to be for the Big Data and Hadoop geek out.
At this sold out event Cisco is introducing our new 6.1 release of Cisco Tidal Enterprise Scheduler. This new release is packed with new features such as a very cool iPhone app, integration into Amazon EC2 and S3 and a self service portal (stay tuned for more blogs on this later). It also includes a new Hadoop adapter with API integration into Sqoop, Hive, HDFS Data Mover and MapReduce.
What’s that you say? Enterprise workload automation for Hadoop clusters? Why would I need that?
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Tags: Big Data, intelligent automation, workload automation