Webinar • Big Data: Big Data • TODOS

Data Lakes: Purposes, Practices, Patterns, and Platforms. Por tdwi.orgAgéndalo en tu calendario habitual ¡en tu horario!

Business and technology reasons for adopting data lakes and similar data collections, such as vaults and hubs
Jueves, 13 de abril de 2017, de 12.00 a 13.00 hs Horario de Virginia (US)
Webinar en inglés

We’re experiencing a time of great change, as data evolves into greater diversity (more data types, sources, schema, and latencies) and as user organizations diversify the ways they use data for business value (via advanced analytics and data integrated across multiple analytic and operational applications). To capture new big data, to scale up burgeoning traditional data, and to leverage both fully, users are modernizing their portfolios of tools, platforms, best practices, and skills.

One of the hotter areas in data modernization is the addition of data lakes to both green-field and pre-existing data ecosystems. Data lakes are already in production in many multi-platform data warehouse environments, advanced analytics applications, and the hybrid data ecosystems surrounding customer relationship management and sales force automation. TDWI feels that the data lake is here to stay, and many more organizations will adopt it for a growing list of use cases in enterprise analytics and operations.

The content of the Webinar is based on the research findings of a new Best Practices Report by TDWI’s Philip Russom called “Data Lakes: Purposes, Practices, Patterns, and Platforms.” That report was sponsored by vendor firms Diyotta, HPE, IBM, SAS, and Talend.

What You Will Learn:

-Business and technology reasons for adopting data lakes and similar data collections, such as vaults and hubs

-The roles of Hadoop and relational technologies, plus the hybrid data architectures that combine these to manage data lakes

-Statistics about users’ best practices, designs, architectures, adoption rates, preferred technologies, staffing, skills, sponsorship, governance, etc.

-Technologies in use with data lakes, including functions for data exploration, data prep, analytics, real-time, multi-structured data, and security

-Top Twelve Priorities for Data Lakes

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