Global In-memory OLAP Database Market 2016-2020
About In-Memory Database
The in-memory database is one of the subcomponents of IMDM, the other subcomponent being in-memory data grid (IMDG). IMDM and in-memory application platform (IMAP) are the components of in-memory computing (IMC), which form the base of this technology. The primary purpose of in-memory processing technology is to enable real-time analyses and allow organizations to store data in RAM rather than in traditional disk storages, which reduces their operational costs.
Technavio’s analysts forecast the global in-memory OLAP database market to grow at a CAGR of 24.95% during the period 2016-2020.
Covered in this report
The report covers the present scenario and the growth prospects of the global in-memory OLAP database market for 2016-2020. To calculate the market size, the report considers the market value of in-memory computing in 2015.
The market is divided into the following segments based on geography:
Technavio Announces the Publication of its Research Report – Global In-Memory OLAP Database Market 2016-2020
Technavio recognizes the following companies as the key players in the global in-memory OLAP database market: Altibase, IBM, Microsoft, Oracle, and SAP SE.
Other Prominent Vendors in the market are: Exasol, Jedox AG, Kognitio, Mcobject, MemSQL, MicroStrategy, SAS Institute, Teradata, Terracotta, and VoltDB.
Commenting on the report, an analyst from Technavio’s team said: “Increasing demand for memory devices will be a key trend for market growth. The availability of in-memory processing with the help of online analytical processing (OLAP) tool at a lower price when compared with other BI tools, has increased the demand for this technology. As it can be easily deployed and maintained, the adoption of in-memory technology has progressed over traditional BI among data warehouse vendors. With the demand for the in-memory database, new providers of this technology are entering the market with features that can have an advantage over their competitors. For example, after SAP and Oracle, IBM and Microsoft have built in-memory technology into DB2 and SQL Server. With organizations preferring to store a large amount of data in the memory for faster computing, the demand for in-memory databases have increased.”
According to the report, efficient and faster analytics will be a key driver for market growth. In an in-memory technology, data is stored in columns using online analytical processing (OLAP), whereas the data is stored in rows in traditional online transaction processing (OLTP) databases. The OLAP approach includes features like compression, faster manipulation of large datasets, and advanced analytics. For instance, if a hospital wants to analyze the data from all the patients suffering from a particular disease and the success rate of treatments, OLAP technology is helpful as it supports historical data. The in-memory technology eliminates the need for organizing and filing the data in a way a user intends to process it. OLAP technology also removes the need for processes such as ETL (extract, transform, load). This ensures that data can be accessed and used faster as all the inactivity has been removed, and adding new data to the existing data is also greatly simplified.
Further, the report states that in-memory databases store an enormous amount of data in RAM. This could lead to the availability of a higher amount of RAM to handle a vast amount of data. Computers or servers with in-memory databases need to function on a 64-bit processor to use the larger memory requirement, and then the computer or server needs to be equipped with enough RAM to store the data for process queries. The increase in the number of transactions creates a high amount of data, which, in turn, creates the need for more storage memory. For example, if an organization has 1 TB of data to be stored in the database on two different physical machines, it means the database size needs to be 2 TB (1 TB of data x 2 copies of data), which requires the purchase of external storage devices. This can be a real challenge for SMEs when compared with large or medium enterprises due to the investment required for purchasing more storage memory.
Altibase, IBM, Microsoft, Oracle, SAP SE, Exasol, Jedox AG, Kognitio, Mcobject, MemSQL, MicroStrategy, SAS Institute, Teradata, Terracotta, VoltDB.