FEB 27, 2013 9:40am ET

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In-Memory Technology: Keeping Pace with Your Data

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Given the speed at which markets shift and customer sentiment changes, it is easy for executives to despair at how long it takes to track, filter and uncover new business insights. For most companies, there is a constant fear of being behind the curve with regard to the data that pours into the business each day.

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Comments (3)
Interesting article. While there is a great deal more noise about in-memory lately - it has indeed been around a long time. The fact that SAP is marketing heavily with it's version, and that Accenture is making a fair amount implementing it with SAP installed accounts to remedy issues around BW obfuscates the much larger capabilities beyond the typical database operations - to those of more advanced analytics.

Having data in-memory is not new, indeed IBM systems from the 1960s did that in essence, but what has changed of late is the precipitous DROP in the cost of commodity DRAM memory. True in-memory computing is not about SSD or cache, it's about pure, MPP applications of RAM.

Optimized, in-memory RDBMS MPP systems have been maturing for 20 years, but the market awareness generated from "Big Data" and Hadoop have brought them to the forefront - as that technology needs an "accelerator" for ad hoc, on-demand analysis. Persistence is still important - and as that persistent store moves away (rapidly) from the Data Warehouse to Hadoop, it avails the separation of storage and analysis of data - enabling the use of the most optimal architectures for each. Namely, in-memory MPP for analytics and commodity MPP on open source Hadoop for the storage (persistence).

Some interesting (albeit mundane) case studies exist at www.kognitio.com/tra, www.kognitio.com/BT and www.kognitio.com/AIMIA.

Posted by Michael H | Wednesday, February 27 2013 at 4:30PM ET
Good article. You only describe the advantages of in-memory databases, thou. I would also consider some limitations that needs to be considered before the implementation. When you talk about the speed and flexibility of a in-memory database, I see three limitations: - the quality of data can impair the decision process; - the data is live, it's subject to change without warning; - you have only access to the data of your ERP, you might still need to integrate other data; - you have access only to the history of data that your ERP provides; you might need more for your decision process. Don't get me wrong. I love in-memory databases and I think that they deserve a relevant space in a BI strategy. On the other hand, I've started building data warehouse 15 years ago because: - data needed to be integrate from different system reconciling semantic and structural differences; - data needed to be cleansed - we need to create the data history - we had to create a separate reporting system because creating a BI system on top of the transactional system was technically difficult. How do you think that an in-memory database can address those points?
Posted by daniel m | Thursday, February 28 2013 at 5:32AM ET
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