Showing 1 - 10 of 31 pages tagged with: Logical Data Warehouse
Business intelligence is a critical requirement for understanding business operations and taking timely decisions to stay competitive. However, it can be a challenge if business has to depend on IT to customize the reports every time, thus delaying the timely availability of insights. Self-service BI is needed to address this issue.In this presentation, the VP of Data Warehouse at Seacoast Bank, Mark Blanchette will present:How existing BI systems that are inflexible can constrain the availability of insights to businessBuilding a logical data warehouse as the enterprise data warehousing...
By Independent Industry Analyst Rick van der LansOrganizations can no longer afford to rely on a traditional data warehouse solution to support new business intelligence (BI) requirements along with their existing BI workloads. The rigid development, operation, and management process that characterizes traditional solutions is insufficient to support new BI requirements such as fast and agile report development, investigative analytics, data science and self-service BI.
The Logical Data Warehouse (LDW), a data system encompassing concepts of a traditional data warehouse, includes data from disparate data sources and core data warehouses.Read this eBook for a complete understanding of the LDW, especially regarding common architectural patterns, performance considerations, governance, self-service discovery, and customer success stories.
Organizations for all sizes and types face significant data integration challenges today. New data storage and processing technologies, such as Hadoop, Spark, etc., can offer organizations with more insights and actionable information to help drive their operations. However these new technologies also raise challenges; how can I best leverage this ‘new data’? How can I better utilize my existing data assets? How can I simplify the use of external data? And so on. Data virtualization can help address these challenges by enabling modern flexible data architectures such as data lakes and...
Drillinginfo uses data virtualization to manage and quickly provision data to the product development team and its customers. Drillinginfo will present how they have created a virtual data abstraction layer using data virtualization and reduced creation of web services for application development time from 1 – 2 weeks to less than a day.
CIT modernized its data architecture in response to intense regulatory scrutiny. They will present how data virtualization is being used to drive standardization, enable cross-company data integration, and serve as a common provisioning point from which to access all authoritative sources of data.
IT organizations find several challenges when responding to business needs. Data integration is paramount and this talk describes three different paradigms: using client-side tools, creating traditional data warehouses and the data virtualization solution - the logical data warehouse, comparing each other and positioning data virtualization as an integral part of any future-proof IT infrastructure.
In this session Alberto Pan, CTO at Denodo, covers the "Architecture and Performance Considerations in the Logical Data Lake." He explains in detail what a Data Lake Architecture looks like, how data virtualization fits into the Logical Data Lake, and goes over some performance tips. This session concludes with an example demonstrating this model's performance.
This session will explore key features in the Denodo Platform that help with the common challenges found in large deployments: hundreds of developers, thousands of queries, and multiple environments. The features that will be highlighted include integration with version control systems, metadata synchronization and migration, monitoring and diagnosing, and resource management.