Columnar database can use dictionary and other compression techniques to store data efficiently and this compression enables even faster columnar operations like Count, Sum, Min, Max and Avg. employee name) is significantly faster than traditional row-store database.
In a columnar database, all values in a column are stored together, so access of individual data elements (e.g. SAP HANA is a high-performance analytics platform based on an in-memory columnar database developed and marketed by SAP. This paper discusses how SAP HANA virtual data models can be used for on-the-fly analysis of live transactional data to derive insight, perform what-if analysis and execute business transactions in real-time without using persisted aggregates. To solve the problem of “actionable data”, enterprises can take advantage of the SAP HANA in-memory platform that enables rapid processing and analysis of huge volumes of data in real-time. The data exists in different transactional systems and/or data warehouse systems, which takes significant time to retrieve/ process relevant information and negatively impacts the time window to out-maneuver the competition. Organizations no longer have the problem of “lack of data”, but the problem of “actionable data” at the right time to act, direct and influence their business decisions. A data-driven organization can analyze patterns & anomalies to make sense of the current situation and be ready for future opportunities.
Informed decision-making, better communication and faster response to business situation are the key differences between leaders and followers in this competitive global marketplace.