On Demand
As big data continues to grow bigger, become more diverse, and more real-time, forward looking organizations are looking to manage and analyze this data using advanced analytics in an environment which might include multiple approaches and technologies. For real time streaming data this could include utilizing technologies that support in-memory processing, where data and mathematical computations are performed in RAM rather than on disk; enabling processing thousands of times faster than data access from disk.
Fern Halper, Ph.D.
Sponsored by
SAP and Intel
Business users today want to move past the limits of spreadsheets and canned business intelligence (BI) reporting to gain a richer, more personalized experience with data. Users want to explore data and discover new insights they can apply readily to improve business strategies, processes, operations, and customer engagement.
David Stodder
Content Provided by
TDWI, IBM, Information Builders, Oracle, Qlik Tech, SAP, SAS, Tableau Software, Tibco Spotfire
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