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
When customers look to deploy their Hadoop solutions, one of the first questions they ask is, which distro should we run it on? For many enterprise customers, the answer has been MapR. For those of you not familiar with MapR, they offer an enterprise-grade Hadoop software solution that provides customers with a robust set of tools for running Big Data workloads. A few months ago, Cisco announced the release of Tidal Enterprise Scheduler (TES) 6.1 and with it integrations for Hadoop software distributions, such as Cloudera and MapR, as well as adapters to support Sqoop, Data Mover (HDFS), Hive, and MapReduce jobs. All performed through the same TES interface as their other enterprise workloads.
Today, I’m pleased to announce that with the upcoming 6.1.1 release of Cisco’s Tidal Enterprise Scheduler, Cisco’s MapR integration will deepen further. Leveraging Big Data for competitive advantage and rises in innovative product offerings are changing the storage, management, and analysis of an enterprise’s most critical asset – data. The difficulty of managing Hadoop clusters will continue to grow and enterprises need solutions like Hadoop to enable the processing of large amounts of data. Cisco Tidal Enterprise Scheduler enables more efficient management of those environment because it is an intelligent solution for integrating Big Data jobs into an existing data center infrastructure. TES has adapters for a range of enterprise applications including: SAP, Informatica, Oracle, PeopleSoft, MSSQL, JDEdwards, and many others.
Stay tuned for additional blog posts on Cisco’s Tidal Enterprise Scheduler version 6.
Tags: Big Data, Cloudera, enterprise scheduler, Hadoop, MapR, mapreduce, sqoop, tes, Tidal
No doubt data is one of an organization’s most important assets. The trick is to turn it into timely and trusted information—information that can be used to rapidly uncover new markets, attract and retain customers, reduce operating costs, shrink time to market, and make smarter strategic decisions. In short, leveraging data can sharpen a company’s ability to navigate markets.
So when we combine Informatica’s world-class data integration platform with Cisco® Tidal Enterprise Scheduler, we are enabling organizations to gain a competitive advantage in today’s global information economy by empowering them with relevant and trustworthy information to support all their business decisions.
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Tags: business intelligence, enterprise scheduler, informatica, process automation, Tidal, workload automation
Looking back, the market was probably caught off guard a bit by an acquisition of an automation software company by the worldwide leader in networking. Looking around, the market may still be wondering: why was the acquisition made? Did all that cash the financial analysts keep talking about finally burn a hole in Cisco’s proverbial head as well as pockets?
Neither conjecture is true, of course. As usual, Cisco mined the market for the next catalyst (pun fully intended) to transform its infrastructure, starting with the data center. The result was a formula for data center transformation that solves some of the most pressing problems in data center management both today and well into the future. Here’s the formula: take one compute platform highly tuned for on-demand cloud environments, add third-party application deliver, then perform a little fusion with support solutions that support the Day 2 operations requirements for automating manual tasks. The result is an automation of the many repetitive tasks that are now being done manually, allowing data center administrators to invest the majority of their resources in aligning IT operations with business goals and creating new ways to generate revenue rather than in just maintaining the infrastructure. Read More »
Tags: enterprise orchestrator, enterprise scheduler, intelligent automation, Tidal, workload automation
There is a myth that all data processing occurs in real time. But the reality is that batch and event based processing are still very much alive and the majority of data processing is still done through batch processing. Our average customer uses Cisco Tidal Enterprise Scheduler to execute ~50K jobs on a daily basis, but we also have large financial services companies automating the execution of over 100K jobs daily, not bad right?
So, with over 50 percent of all business processes leveraging batch operations, it is essential to keep your batch production running smoothly in order to keep your business running smoothly. You cannot afford to have failed jobs. Failure is not acceptable as it directly impacts the business and can impact revenue – such as the inability to process orders or to generate invoices. What are we getting at here?
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Tags: enterprise scheduler, intelligent automation, job scheduling, Tidal, workload automation