Accelerating the Path to Value with Hybrid Analytics Architecture
TDWI Speaker: David Stodder, TDWI Research Director
Date: Wednesday, November 30, 2016
Time: 9:00 a.m. PT, 12:00 p.m. ET
Webinar Abstract
In today’s demanding economic environment, companies that can develop and deploy analytics faster have a significant competitive edge. They can use analytics to detect patterns and changes in markets, learn customer preferences, be alert to fraudulent activity, and more. With the advent of cloud computing, users quickly gain access to new data sources and analytic techniques, enabling companies to finally unleash their analytics – they are no longer constrained by the limits of their on-premises computing, database platform, data warehouse, and data storage capacity. However, to avoid even more data siloes, data governance issues, and more, organizations should consider a hybrid analytics architecture that brings together on premises and cloud, enabling a more controlled journey to the cloud, while enjoying the flexibility, power, and speed they need to handle a range of analytics demands.
Hybrid analytics architecture is the approach that fits organizations that have significant on-premises data warehouse investments but want to take advantage of the cost and scalability opportunities of cloud computing and open source technologies. The problem has been that the two worlds have been separate; it’s been difficult to integrate on-premises analytics and data management with what organizations are doing on the cloud and open source, much less have the flexibility to choose the right platform for the right workload. Now, with the evolution of hybrid analytics architecture and “logical” data warehousing, organizations can execute strategies that require integration of on premises, cloud, and open source platforms.
Join this Webinar to learn about how you can accelerate the path to value with hybrid analytics architecture. This Webinar will cover:
- Hybrid analytics architecture: How technologies and practices are evolving to support the integration of on-premises and cloud computing for analytics
- Why hybrid is a good approach for organizations to meet their flexibility and scalability needs, including handling dynamic requirements and surges of activity
- How hybrid analytics architecture fits with the concept of the logical data warehouse
- How hybrid analytics architecture fits with big data and open source analytics and data management, including Apache Hadoop and Apache Spark
- What the hybrid approach means for skillsets, governance, analytics performance, and more
David Stodder
Content Provided by
TDWI, IBM