RESEARCH & RESOURCES

Available On-Demand - This webinar has been recorded and is now available for download.

Cloud or On Premises for Analytics: Why Not Choose Hybrid?

Webinar Speaker: David Stodder, Senior Director of Research for BI, TDWI

Date: Thursday, July 26, 2018

Time: 9:00 a.m. PT, 12:00 p.m. ET

Business users today are clamoring for more analytics so they can explore data in detail and build business-driven analytic models. However, many organizations are on the horns of a dilemma: Where should they put the data and process analytics workloads? The cloud offers tempting flexibility and price points—plus the “gravity” of cloud-based applications is pulling more data generation into the cloud, and it can make sense to do analytics processing on cloud platforms. On the other hand, on premises is where many organizations feel more comfortable and have more experience from data management, governance, and security perspectives. It’s a big decision.

What about having your cake and eating it, too? The answer to the big question is in many cases “both.” That is, choosing a hybrid architecture that integrates cloud and on-premises data. Most analytics projects need a variety of data; users will want to fill their data pipelines and run data preparation routines on data sets from both cloud and on-premises environments. In addition, many organizations do not have just one cloud storage platform. They might have data spread across Amazon AWS, Microsoft Azure, Google Cloud, private clouds, and more. A hybrid architecture can be a better option than choosing one or the other.

Join this TDWI Webinar and learn why a hybrid architecture could be the best choice for agile and scalable analytics. This webinar will discuss key issues to consider in making the choice as well as TDWI research about what peer organizations are doing.

Topics to be discussed include:

  • Considerations in choosing cloud, on-premises, or hybrid architecture for analytics
  • Addressing data pipeline challenges in supporting analytics and machine learning
  • Strategies for choosing the appropriate platform for data discovery and analytics workflow processing
  • Handling governance, security, and data management requirements

David Stodder

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