Journey to the Cloud, Self-Service BI, and Data Lakes with Data Virtualization

Data scientists’ time is valuable. Computing resources are expensive. With only 87% of projects ever making it to production (Source: VentureBeat), organizations often overcommit to costly projects that bear little fruit. Data science teams need a way to assess project feasibility without diving head first.

Organizations that operate worldwide typically need to manage data both locally and globally. Local business units and subsidiaries must address region-specific data and accounting standards, regulations, customer requirements, and market drivers. At the same time, corporate headquarters must share data broadly and maintain a complete view of performance for the entire enterprise. For many global firms, data is the business. They need state-of-the-art data management just to remain innovative and competitive. Hence, multinational businesses face a long list of new business and technical requirements for modern data management.

Modern data applications and analytics rely on a wide variety of data both inside and outside the company; organizations depend on enriched data sets for better insights. This need, in part, has driven many companies to move to cloud data warehouses and cloud data lakes. However, it’s no longer simply about migrating to the cloud. It’s about modernizing using a combination of industry-leading services in the cloud and cloud-native data management services to deliver better business decisions, faster.

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.