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 Data Science Experience
Collaboratively analyze data using the built-in IBM Data Science Experience solution, or use existing Jupyter notebooks and 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 Common SQL engine
Use IBM Common SQL Engine for workload portability and skill sharing across public and private cloud.

Reduce disruptions by scaling out incrementally
Scale out compute power and storage in place to reduce disruptions to your analytics systems.
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