Cloudera Sessions is coming to a City Near You!
Have you registered for the upcoming Cloudera Sessions roadshow yet? According to IDC Analysts, the market for Big Data will reach $16.9 billion by 2015, with an outrageous 40% CAGR. As the sheer volume of data continues to climb, enterprise customers will need the right software and infrastructure to transform this data into meaningful insights.
Cisco is partnering with Cloudera to offer a comprehensive infrastructure and management solution, based on the Cisco Unified Computing System (UCS), to support our customers big data initiatives. As a proud sponsor for this event, I would encourage you to join us at one of the following scheduled stops to learn more about our joint solutions for big data:
San Francisco 9/11
Jersey City 9/18
Milwaukee 10/17 (Note: changed from 10/2 to 10/17)
Cloudera has a fantastic agenda scheduled in each of the cities featuring keynote speakers that you won’t want to miss. I hope to see you there.
For the latest information regarding Big Data on Cisco UCS, I’ve added a couple links below for your reference:
Introducing Cisco UCS Common Platform Architecture (CPA) for Big Data, By Raghu Nambiar
Top Three Reasons Why Cisco UCS is a Better Platform for Big Data, by Scott Ciccone
Stop by and say hello and let me know if you have any comments or questions, or via twitter at @CicconeScott.
Tags: Big Data, Blade Servers, Cisco UCS, Cisco Unified Computing System, Hadoop, Rack Servers, UCS
Speed is everything. Continuing our commitment to make data center infrastructures more responsive to enterprise applications demands, today, we announced FlexPod Select with Hadoop, formerly known as NetApp Open Solution for Hadoop, broadening our FlexPod portfolio. Developed in collaboration between Cisco and NetApp, offers an enterprise-class infrastructure that accelerates time to value from your data. This solution is pre-validated for Hadoop deployments built using Cisco 6200 Series Fabric Interconnects (connectivity and management), C220 M3 Servers (compute), NetApp FAS2220 (namenode metadata storage) and NetApp E5400 series storage arrays (data storage). Following the highly successful FlexPod model of pre-sized rack level configurations, this solution will be made available through the well-established FlexPod sales engagement and channel.
The FlexPod Select with Hadoop architecture is an extension of our popular Cisco UCS Common Platform Architecture (CPA) for Big Data designed for applications requiring enterprise class external storage array features like RAID protection with data replication, hot-swappable spares, proactive drive health monitoring, faster recovery from disk failures and automated I/O path fail-over. The architecture consists of a master rack and optionally up to nine expansion racks in a single management domain, creating a complete, self-contained Hadoop cluster. The master rack provides all of the components required to run a 12 node Hadoop cluster supporting 540TB storage capacity. Each additional expansion rack provides an additional 16 Hadoop cluster nodes and 720TB storage capacity. Unique to this architecture is seamless management integration and data integration capabilities with existing FlexPod deployments that can help to significantly lower the infrastructure and management costs.
FlexPod Select has been pretested and jointly validated with leading Hadoop vendors, including Cloudera and Hortonworks.
Tags: Big Data, Cloudera, CPA, FlexPod, FlexPod Select, Hadoop, Hortonworks, netapp
At this year’s Hadoop Summit 2013
, I presented on the “The Data Center and Hadoop” which built upon the past two years of testing the effects of Hadoop on the data center infrastructure
. What makes Hadoop an important framework to study in the data center is that it contains a distributed system that combines both a distributed file system (HDFS) along with an execution framework (Map/Reduce). Further it builds upon itself and can provide other real-time or key/value stores(HBASE) along with many other possibilities. Each comes with its own set of infrastructure requirements that include throughput sensitive components along with latency sensitive components. Further in the Data Center, understanding how all these components work together is key to optimized deployments.
After studying many of these components and their effects, the very data we were alanyzing became a topic of a lot of our discussions. We combined application performance data, application logs, compute data AND network data to build a complete picture of what is happening in the data center.
With the advent of programmable networks (aka “Software Defined Networking”) it is not only important to make the network more application aware, but to also know where and how to analyze and make the right connections between the application and the network.
Tags: Big Data, Cisco Nexus, data center, Hadoop, Hadoop Summit, nexus, SDN, software defined networking
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