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In-Memory Technology and Big Data

Posted by Anahita | Posted in Business Intelligence, Data Warehouse | Posted on 14-05-2012

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In my previous blogs I wrote about the Big Data and the related keywords and technologies such as unstructured data, Hadoop HDFS, MapReduce, etc . In this post I am looking at what “in-memory technology” brings in to help analysing the big data.

Business Intelligence is all about getting the right information to the right people in the right time, so they can make timely decisions that will help business achieve its goals such as higher service efficiency, better customer experience, and higher quality of products.

Dealing with Big Data creates many challenges, but above all of all, it is the velocity challenge. Velocity is when there is a time lag between  when the data is created and when the business can look at it and analyse it in order to correct behaviour or make  related decisions.

There are many cases that business cannot afford to wait for data to be consolidated in a data mart or data warehouse, or the aggregates to become available after the OLAP cubes are processed. There are cases that the information is required in “real time” and this is where in-memory technology becomes important.

So what is in-memory technology? In short it is when the data is stored in memory instead of the hard disk. The limitation of 4GB maximum memory is removed with the introduction of the 64 bit operating systems and considering the fact that the price of the RAM is relatively low, huge amount of data  (Terabytes  or Thousands of Gegabytes) can be stored in memory and available to be processed in real time. Having the data available in memory means faster access to the very data that is required in real time.

In summary, I have explained about the meaning of the in-memory technology and why it is now an available option for business intelligence. In fact the real benefits of in-memory technology is  the real time availability of data for Operational BI situations. This is used when huge number of transactions are required to be monitored and analysed in real time. This is very appealing to financial services for monitoring the financial transactions, call centre staff for real time fraud detection when talking to customers,  or service companies who require to act quickly as the requirements for their service capacity changes.

The in-memory technology is available by various vendors in products such as Microsoft SQL Server 2012 xVelocity and SAP HANA in SAP Business Objects BI 4.0. These solutions are varied in nature and come with several different capabilities and features, but they all make use of the new advances in hardware and software such as in-memory technology and massive parallel processing to reduce the gap between the data and the processor in order to remove bottlenecks and increase operational productivity.  Implementing the technology via these vendors promises substantially faster results in query analysis, faster decision making with real time data and finally chnaging the way the organisations get access to data and make us of massive available information!