Big Data; In-Memory Computing; and ERP

• Julius Rassou
• 09-Sep-2017 06:16:46

Big Data; In-Memory Computing; and ERP

The core purpose of Big-data technologies is to ease the storage, processing, and analysis of the perpetually exploding volume of data; to make it accessible to multiple users in the respective format desired by each user; all in almost real-time speed. Thanks to the non-volatile random access memory technology propelled In-Memory Computing (IMC); realizing the purpose of Big-data technologies is no more a myth. In-memory computing technology utilizes non-volatile random access memory to store, process, and deliver data using the sophisticated Data Compression technique, at speeds that are more than 10,000 times faster than the typical disk drives / hard drives.

 Online Transaction Processing (OLTP) is a class of software programs capable of supporting accounting transaction-oriented applications on the Internet. OLTP systems are also used for order entry, financial transactions, customer relationship management (CRM) and retail sales. Online Analytical Processing (OLAP) is the technology behind the limitless report viewing, complex analytical calculating, and predictive budgeting and forecasting oriented Business Intelligence (BI) functions; and Data Mining, Data Discovery, and Data Warehousing services. As ERP embraces almost all the transactions and processes mentioned above, it happens to be one of the major contributors to the present day Big-data traffic, through OLTP and OLAP.

 The steady growth of customer bases at various levels has led to the perennial growth of data. Irrespective of the enormity of that data, the highly competitive market scenario demands its real-time analysis and user-specific distribution. To scale such disproportionate Business Requirement Vs Digital Support situation, the ERP enriched with In-memory computing power comes as a boon. In-memory computing powered ERP solutions are capacitated to perform client/industry/user-specific in-memory database management (IMDB), and in-memory database analytics, and seamlessly bridge any extent of the difference between business needs and data piling.

 There is a prevalent notion that in-memory computing is very expensive. But, with the passage of time, the costs of non-volatile RAMs have almost come down to the level of hard drive storage facilities. This makes the deployment of in-memory computing in ERPs very affordable; thus, making in-memory ERPs the preferred and affordable choice for all businesses. Consequentially business stakeholders can remain assured of scaling any kind of industrial Digital Transformation that they might have to come across in the future.