Showing 1 - 10 of 14 pages tagged with: Hadoop
Vizient is the largest member owned healthcare company in the US, delivering industry-leading supply chain management services and clinical improvement services to its members.They grapple with more than 1 billion detailed line item spend data, 110 million detailed patient records, 9 million products with 100 million cross-references, and 110,000 non-acute care customers, submitted each month.The Sales Operations team needed various types of reporting, such as opportunities, leads and member-related reports. Additionally, they needed to analyze opportunity-to-outcome, member-to-product and...
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.
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.
Simplified Data Management with Hadoop and Data VirtualizationLearn how Vizient simplified data management and enabled faster data discovery with Hadoop and Data Virtualization.
ChallengesInefficient manual integration of disparate data spread across legacy mainframes, enterprise (CRM, ERP, Excel) and Hadoop sourcesDelayed data for business reportingHigh costs in building data integration workflowsSolutionDenodo deployed to modernize transportation scheduling system (5 – 10 year project)Platform used to normalize operational sources, mainframe tables and Hadoop systemsData Virtualization used to provide an abstracted view of legacy data while company migrates to a Linux based architectureBenefitsIncreased efficiency in extracting and combining data due to connectors...
Big Data projects can quickly turn into Big Data silos…unless you can work out how to easily share your HDFS files and Map/Reduce job results with the rest of the organization. See how Denodo’s Data Virtualization Platform allows you to turn your Big Data projects into valuable Big Data assets that can be shared with everyone in your organization.
Denodo´s partnership with IBM has resulted in solutions for integrating data in Pure Data System for Analytics (Netezza) and IBM's BigInsights. After extensive collaborative technical work and optimization, the performance for the Denodo Platform was tested in the IBM Labs and it exceeded expectations in all three use patterns initially contemplated to be co-marketed by IBM. These patterns are described in the IBM solution brief.
Big Data is the latest ‘must have’ on the CIO’s wish list. But how do you take advantage of the benefits that Big Data products, such as Hadoop, can deliver without getting stuck in an ‘elephant trap’?
In order to derive meaningful ROA from investments in Big Data, Cloud Computing, and NoSQL, organizations should consider adopting Data Virtualization from the get-go.