5 Things you should know about the IBM Data Engine for Analytics Solution - Power Systems Edition
- The IBM Data Engine for Analytics is a customizable infrastructure solution with integrated software optimized for big data and analytics workloads.
- The IBM Data Engine is ideal for workloads such as Hadoop or Spark and provides flexible and adjustable storage and compute resources that are easy to deploy and align to specific line business requirements.
- This means you can move or add more storage and compute when and where it’s needed to address multiple workloads as requirements change and evolve. It provides an easy onramp for clients to lay the foundation for bringing different big data and analytics workloads together onto one infrastructure.
- Built on IBM Power Systems, the IBM Data Engine for Analytics fully integrates the innovative capabilities of IBM Elastic Storage Server, IBM Platform Computing and networking components with Linux on Power scale-out systems. Clients can add big data and analytics software such as IBM InfoSphere BigInsights for Hadoop and Spark based analytics, IBM InfoSphere Streams for high ingest streaming analytics, and IBM Watson Explorer analytical components for advanced Natural Language Processing of unstructured data.
- The deep integration and optimization of analytics workload performance on Power Systems enables businesses to reach unparalleled performance to deliver insights to the business faster.
The following are the features and benefits of the solution:
- Complete, pre-assembled and tested infrastructure with big data and analytics software preloaded.
- On-site services for fast configuration and data center integration.
- Intelligent cluster management and automation for effective deployment.
- Easily set-up and manage workloads for multiple tenants.
- Adjustable resource allocation to meet diverse line of business demands.
- Scalable and extendable as needs change and as the enterprise grows.
- Reliability without data duplication.
- Tailored big data and analytics optimizations.
- Lays the foundation for consolidating traditional data analytics with new workloads such as Hadoop and Spark.
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