This checklist defines data security and data-centric security and discusses best practices and enabling technologies to help make data more secure.
This checklist will help you and your team plan and launch successful data lake projects with your legacy data sources. It reviews the critical success factors for these projects as well as the risks and issues to mitigate.
Download this e-book to learn some key strategies for analytics success in the cloud, as well as an illuminating case study study of the benefits of a hybrid approach from the award-winning AMC Network.
A data lake ingests data in its raw, original state, straight from data sources, with little or no cleansing, standardization, remodeling, or transformation. These and other data management best practices can then be applied flexibly as diverse use cases demand.
Most data lakes are built atop Hadoop, which enables a data lake to capture, process, and repurpose a wide range of data types and structures with linear scalability and high availability.
Big data presents significant business opportunities when leveraged properly, yet it also carries significant business and technology risks when it is poorly governed or managed.
Download this e-book to read articles, opinions, and interviews on data management trends and how enterprises are changing the way they deal with data.
This TDWI Checklist Report provides seven best practices for introducing, implementing, and leveraging streaming analytics as a component of an enterprise business intelligence and analytics architecture. These recommendations will help organizations understand where streaming analytics fits into their analytics frameworks and how it can be integrated with the complementary components of their organization’s analytics environment.
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