Run analytics faster Simplify installation, management and administration Realize the benefit of using a data warehouse that is pre-configured and optimized for large volumes of data. Drive cloud-ready flexibility Shift workloads within a public cloud, private cloud and on-premises environment, based on your application requirements. Benefit from IBM Watson Studio Collaboratively analyze data using the built-in IBM Watson Studio data to optimize the data science experience or use existing Jupyter notebooks to bring data scientists to your organization's data. Gain real-time value with machine learning Stream, analyze and learn from data sets, without explicit programming, so your data scientists can develop and improve machine-learning models on the platform where the data resides. Access, query and analyze data across your data warehouse and Hadoop with the IBM Common SQL Engine Use IBM Common SQL Engine for workload portability and skill sharing across public and private cloud. Reduce disruptions with data replication with continuous availability Support active and stand-by replicas for workload balancing and shifting workloads during planned outages, while reducing recovery time for unplanned outages. Use cases Better integrate your data Problem Aggregate siloed data across the organization. Solution Use a logical data warehouse with Hadoop, data marts or other associated deployments that interact and offer a unified view of your data — on premises or in the cloud. Enhance customer interactions Problem Drive personalized and timely customer interactions. Solution Create personalized customer experiences in real time by using your internal data with analytics that yields high performance and simplifies scalability. Speed time to innovation Problem Improve time to market for new product and process improvements. Solution Use embedded machine learning to accelerate performance on complex analytics and deliver insight more quickly to your data users. Improve processes Problem Meet growing requirements for operational and process improvements. Solution Build analytics for different data types and sets using virtualization and federation to unify data access across the logical data warehouse, the cloud and Hadoop. Technical details Seven compute nodes in one rack, containing: IBM POWER8® S822L 24 core server 3.02 GHz 512 GB RAM (each node) 2x1.2 TB SAS HDD Red Hat Linux OS Up to three Flash arrays in one rack, containing: IBM FlashSystem® 900 Dual Flash controllers Microlatency Flash modules 2-dimensional RAID5 and hot swappable spares for high availability Two Mellanox 10G Ethernet switches 48x10G ports 12x40/50G ports Dual switches (to form a resilient network) IBM SAN64B 32G Fibre Channel SAN 16 Gb Fibre Channel switch 48x32 Gbps SFP+ ports Expert resources to help you succeed Community All things data warehouse, featuring blog posts, technical information and support. Support One-stop shop for supporting documentation, downloads, tools and resources. IBM Knowledge Center The place to go for everything from operating basics to capabilities and features.