In-memory OLAP is the method by which analytical data is loaded into memory for live calculations and querying. Because the data is loaded into memory, running queries (or, in OLAP terms, slicing and dicing), can be faster then with traditional relational on-line analytical processing (ROLAP), multidimensional OLAP (MOLAP) and hybrid systems. Since the data resides in the RAM, the system does not need to reach out to a database or a physical file which may further entail additional network operations and disk read/write operations. Furthermore, traditional cubes store pre-calculated data and results which can then be queried. This limits the number of pre-calculated combinations. With RAM-based analytics, these calculations can sometimes be just as quickly generated on the fly. Scope of the Report: This report studies the In-memory OLAP Database market status and outlook of Global and major regions, from angles of players, countries, product types and end industries; this report analyzes the top players in global market, and splits the In-memory OLAP Database market by product type and applications/end industries. A number of established and new vendors have come out with in-memory OLAP technologies. While the concept is at least a decade old, it is gaining more acceptance due to cheaper Random Access Memory (RAM) and faster CPU speeds. The global In-memory OLAP Database market is valued at xx million USD in 2017 and is expected to reach xx million USD by the end of 2023, growing at a CAGR of xx% between 2017 and 2023. The Asia-Pacific will occupy for more market share in following years, especially in China, also fast growing India and Southeast Asia regions. North America, especially The United States, will still play an important role which cannot be ignored. Any changes from United States might affect the development trend of In-memory OLAP Database. Europe also play important roles in global market, with market size of xx million USD in 2017 and will be xx million USD in 2023, with a CAGR of xx%. Market Segment by Companies, this report covers Altibase IBM Microsoft Oracle SAP SE Exasol Jedox Kognitio Mcobject MemSQL MicroStrategy SAS Institute Teradata Terracotta VoltDB Market Segment by Regions, regional analysis covers North America (United States, Canada and Mexico) Europe (Germany, France, UK, Russia and Italy) Asia-Pacific (China, Japan, Korea, India and Southeast Asia) South America (Brazil, Argentina, Colombia) Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa) Market Segment by Type, covers Transaction Reporting Analytics Market Segment by Applications, can be divided into BFSI Government and Defense Healthcare and Life Sciences Retail and Consumer Goods Transportation and Logistics IT and Telecommunication Manufacturing Energy and Utility
Table of Contents 1 In-memory OLAP Database Market Overview 1.1 Product Overview and Scope of In-memory OLAP Database 1.2 Classification of In-memory OLAP Database by Types 1.2.1 Global In-memory OLAP Database Revenue Comparison by Types (2017-2023) 1.2.2 Global In-memory OLAP Database Revenue Market Share by Types in 2017 1.2.3 Transaction 1.2.4 Re
