Integración y Gobierno de Datos en Big Data, Data Lakes y Hadoop. Por Claudia Imhoff. - Seguridad Mania.com - España y América Latina

While hardware and database vendors have made significant investments in performance and scalability, data integration platforms have historically focused on addressing an expansive set of functionality including reproducible and reusable ETL, data preparation, and data governance capabilities. Certainly, these functions are needed but so is performance, especially given the multiple deployment methods, infrastructure technologies, and large amounts of data available today.

We are on course for some very challenging data management scenarios: the use of private and public cloud installations mixed with on-premises deployments; NoSQL, open source technologies like Hadoop, hybrid data platforms, traditional relational databases all vying for your company’s data; and implementations with great pressure to be agile competing with the equally mandatory need for trustworthy data, solid documentation and governance of these environments.

In this presentation, we discuss the need for creating a managed data environment that supports the needs of all users of analytical data while ensuring the creation of governed, sharable, and portable data integration and governance work products.

Attendees will learn:

- The need to expand the analytics architecture to serve new BI and analytical capabilities

- The different types of data integration and governance needed for each component in the architecture

- How to manage enterprise policies for security, privacy, and availability while ensuring the lineage, terminology, documentation, and cataloging of all analytical sources of data

- The data management technologies available to ensure performance, scalability, versatile integration, and governance

Sponsored By IBM