RESEARCH & RESOURCES

Defining a Multiplatform Data Architecture and What It Means to You

TDWI Speaker: Philip Russom, Senior Research Director for Data Management

Date: Thursday, July 20, 2017

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

Webinar Abstract

A revolution is occurring in modern analytics, driven by our ability to capture new sources of information at a detail previously too complex and costly to imagine. As more data comes from new sources (from machines to social media) and is applied to new applications, data is evolving into greater diversity, including every variation of data type from unstructured to multistructured. Even as new tools to analyze and manipulate this newly available resource come online, it is not enough to look at the data manipulation layer alone.

To capture diverse data and leverage it for business value, users and their support organizations are diversifying their portfolios of data management tools and storage platforms, including traditional relational databases alongside new database types, Hadoop, and in-memory processing. The result is an eclectic mix of old and new data managed on traditional and modern platforms—sometimes collectively called a multiplatform data architecture (MDA).

For the many user organizations embracing a multiplatform data architecture, its complexity is challenging to design, maintain, govern, and integrate with other systems. Yet, users succeed by relying on best practices in data architecture and data governance, plus technologies that stitch together the grand design and make it high performance, namely data and application integration, central metadata, and in-memory functions for databases and analytics. An MDA is worth the effort because its diverse abilities provide optimized options for the capture and repurposing of diverse new data and the expanding range of its business use.

Attend this webinar to learn about:
  • What a multiplatform data architecture is, why it exists, and what its barriers and benefits are
  • Common enabling technologies and best practices for multiplatform data architectures
  • Approaches to architecting large, heavily distributed systems and data architectures as found in a multiplatform architecture, including the roles of traditional and modern data management techniques

Philip Russom, Ph.D.

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