Ibm Software Defined Infrastructure For Big Data Analytics Workloads
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IBM Software Defined Infrastructure for Big Data Analytics Workloads
This IBM® Redbooks® publication documents how IBM Platform Computing, with its IBM Platform Symphony® MapReduce framework, IBM Spectrum Scale (based Upon IBM GPFSTM), IBM Platform LSF®, the Advanced Service Controller for Platform Symphony are work together as an infrastructure to manage not just Hadoop-related offerings, but many popular industry offeringsm such as Apach Spark, Storm, MongoDB, Cassandra, and so on. It describes the different ways to run Hadoop in a big data environment, and demonstrates how IBM Platform Computing solutions, such as Platform Symphony and Platform LSF with its MapReduce Accelerator, can help performance and agility to run Hadoop on distributed workload managers offered by IBM. This information is for technical professionals (consultants, technical support staff, IT architects, and IT specialists) who are responsible for delivering cost-effective cloud services and big data solutions on IBM Power SystemsTM to help uncover insights among client's data so they can optimize product development and business results.
IBM Software Defined Infrastructure for Big Data Analytics Workloads
This book documents how IBM Platform Computing, with its IBM Platform Symphony MapReduce framework, IBM Spectrum Scale (based upon IBM GPFS), IBM Platform LSF, the Advanced Service Controller for Platform Symphony work together as an infrastructure to manage not just Hadoop-related offerings, but many popular industry offerings such as Apach Spark, Storm, MongoDB, Cassandra, and so on. It describes the different ways to run Hadoop in a big data environment, and demonstrates how IBM Platform Computing solutions, such as Platform Symphony and Platform LSF with its MapReduce Accelerator, can help performance and agility to run Hadoop on distributed workload managers offered by IBM. --
Getting Started with Docker Enterprise Edition on IBM Z
What is the difference between a virtual machine and a Docker container? A virtual machine (VM) is like a house. It is fully contained with its own plumbing and heating and cooling system. If you want another house, you build a new foundation, with new walls, new plumbing, and its own heating and cooling system. VMs are large. They start their own operating systems. Containers are like apartments in an apartment building. They share infrastructure. They can be many different sizes. You can have different sizes depending on the needs. Containers "live" in a Docker host. If you build a house, you need many resources. If you build an apartment building, each unit shares resources. Like an apartment, Docker is smaller and satisfies specific needs, is more agile, and more easily changed. This IBM® Redbooks® publication examines the installation and operation of Docker Enterprise Edition on the IBM Z® platform.