On Demand
The growing hype surrounding the idea of a data lake (or data refinery) to enhance the data warehousing environment and to support big data is creating significant confusion in the marketplace. The main idea of a data lake is to act as a data landing area for the raw data from the many, and ever increasing number of, data sources in organizations. The data can then be transformed and distributed to downstream systems as required.
Colin White
Sponsored by
SAP and Intel
The rate of innovation in the data warehousing, business intelligence, and analytics space has been accelerating over the past few years. The commercialization of massive-scale data management and computing platforms, coupled with a lowered barrier to entry, means that more organizations are exploring newer ways to leverage descriptive and predictive models to drive profitable business decisions.
David Loshin
Sponsored by
SAP and Intel
As the Software as a Service (SaaS) model for business applications deployed in the cloud has grown and matured to support many operational business functions, the hosted or cloud-based model has begun to spread to data warehousing and business intelligence. A number of service providers are offering a blend of configured data warehousing platforms coupled with managed services that effectively free the consumers to focus on data analysis instead of data warehouse management.
David Loshin
Sponsored by
SAP and Intel
Organizations today are seeking to drive deep analysis, detect patterns, and find anomalies across terabytes or petabytes of raw big data. Whether you’re trying to discover the root cause of the latest customer churn or the hidden costs that are eroding the bottom line, you need analytic tools and techniques that work well with unstructured and multi-structured data in its original raw form.
Philip Russom, Ph.D.
Sponsored by
Tableau Software
A logical data warehouse is an architectural layer that sits atop the usual data warehouse (DW) store of persisted data. The logical layer provides (among other things) several mechanisms for viewing data in the warehouse store and elsewhere across an enterprise without relocating and transforming data ahead of view time. These views also serve as interfaces into disparate data and its sources. In other words, the logical data warehouse complements the traditional core warehouse (and its primary function of a priori data aggregation, transformation, and persistence) with functions that fetch and transform data, in real time (or near to it), thereby instantiating non-persisted data structures, as needed.
Philip Russom, Ph.D.
Sponsored by
SAP, Co-Sponsored by Intel
Self-service Business intelligence software is bringing analysts and business users together and driving the fundamental cultural shift making organizations truly data-driven. Broader access to reliable and curated data can improve business performance with top- and bottom-line impact. And more businesses are seeing this benefit as interest in self-serve BI tools grows, according to TDWI research.
Fern Halper, Ph.D.
Sponsored by
Looker
The three-decade-old enterprise data warehouse is evolving into an enhanced data warehouse architecture where Hadoop acts as a supporting platform for traditional data warehouse activities. The challenge with this enhanced data warehouse approach is how to store and access data transparently regardless of its location and how it is managed. This presentation explores why organizations are adding Hadoop to the traditional data warehouse, presents use cases for such an environment, and takes a detailed look at why organizations need a common and transparent interface to both traditional relational and Hadoop data management systems.
Colin White
Sponsored by
TDWI and IBM Content