Webinar: Run Analytics Faster with IIAS: IBM Next Generation Analytics Warehouse Feb 21st, 2019
Live Webinar on Tuesday, February 21, 2019
12:00 – 1:00 PM EST
IBM Integrated Analytics System is a unified hybrid data management analytics solution providing massively parallel processing. It comprises a high-performance hardware platform and optimized database query engine software that work together to support various data analysis and business-reporting capabilities. The subcomponents have been developed, tuned and tested to save setup and administration time and costs.
During the webinar, we will highlight 6 reasons clients prefer IBM Integrated Analytics Systems:
- Simplified installation, management & 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 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 and continuous availability – Support active and stand-by replicas for workload balancing and shifting workloads during planned outages, while reducing recovery time for unplanned.
Register now and invite your colleagues! We look forward to virtually meeting you.
REGISTER NOW & invite your colleagues. We look forward to virtually meeting you.
Brad Peterson Jenny Happas
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