On Demand
Most legacy enterprise data warehouse (EDW) architecture can satisfy many routine workloads associated with operational querying, reporting, and analytics. However, the accelerated growth of data volumes, diversified types of both structured and unstructured data streams, have motivated many business intelligence stakeholders to consider newer technologies that can accommodate these workloads. Today, companies are being asked to support more advanced predictive and prescriptive analytics while also maintaining a nearly flat budget.
David Loshin
Sponsored by
Hewlett Packard Enterprise
Cloud computing is a hot topic as organizations look to take advantage of the agility and pay-as-you-go model the cloud offers. Data warehousing is undergoing significant change—an increasing number of organizations are using the cloud for data warehousing at the same time as they begin to take advantage of new sources of data, advances in business analytics, and new database technologies. However, determining which projects are best suited to cloud-based computing and selecting a cloud solution are not easy tasks.
Colin White
Sponsored by
Snowflake
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