SAP HANA : Future of Data Warehousing and Bigdata Analytics

Webinar will discuss about: History and present of SAP Business Model. Overview of Technology underlying SAP HANA (In Memory Computing) , Bigdata, Space for Analytics. SAP HANA Modelling Environment (Architecture + Setting up Environment). Data warehousing Models (Star schema + concept of master data). Basic Graphical Views (attribute, analytical and calculation Views). Data base operation in Hana tool.
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Spotlight

OTHER ON-DEMAND WEBINARS

Big Data and Analytics in Pharma

FirstWord Pharma

Pharma has had access to Big Data for quite some time now, but the data is only useful if you know what to do with it. That’s something pharma companies are just starting to figure out. This webinar will help you get up to speed faster by discussing ways pharma companies can harness the astonishing amount of data they collect to build a lasting competitive advantage.
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Lynx: A FAIR-fuelled Reference Data Integration & Look up Service at Roche

Roche, as a leading biopharmaceutical company and member of the Pistoia Alliance, has a diverse and distributed ecosystem of platforms to manage reference data standards used at different parts of the organization. These diverse reference data standards include ontologies and vocabularies to capture specifics of the research environment and also to describe how clinical trial data are collected, tabulated, analyzed, and finally submitted to regulatory authorities. In the context of the EDIS program, Roche has bridged these parts to improve reverse translation from studies into research and also embraced FAIR to emphasize machine-actionability and data-driven processes.
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DataEd Webinar: Essential Reference and Master Data Management

Data tends to pile up and can be rendered unusable or obsolete without careful maintenance processes. Reference and Master Data Management (MDM) has been a popular Data Management approach to effectively gain mastery over not just the data but the supporting architecture for processing it. This webinar presents MDM as a strategic approach to improving and formalizing practices around those data items that provide context for many organizational transactions: its master data. Too often, MDM has been implemented technology-first and achieved the same very poor track record (one-third succeeding on-time, within budget, and achieving planned functionality). MDM success depends on a coordinated approach typically involving Data Governance and Data Quality activities.
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Real-time Machine Learning: Architecture and Challenges

Fresh data beats stale data for machine learning applications. This on demand webinar discusses the value of fresh data as well as different types of architecture and challenges of online prediction, it will also cover the tradeoffs between latency, staleness, and cost.
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