Business intelligence is a rapidly-evolving discipline. And, as companies strive to maximize the value of their enterprise information assets, new technologies and techniques continue to emerge at a rapid pace. The latest BI methodology to spark interest among users and industry experts alike is in-memory analytics.
The primary goal of in-memory analytics is to eliminate standard disk-based BI deployments, which are typically relational or OLAP-based. These traditional implementations come with numerous drawbacks such as poor flexibility, limited scope of analysis, and slow response times. With in-memory analytics, the reporting software performs all needed analytical functions at runtime – including data retrieval and storage, manipulation, calculation, formatting, etc. – within the memory of a 64-bit server.
Why are so many vendors and their clients suddenly embracing in-memory analytics? And what are the benefits to this approach?
The key advantage of in-memory analytics is speed. Because queries and related data reside in the server’s memory, report generation does not require any network access or disk I/O. This will dramatically increase the performance and reliability of the data warehouses and back-end databases in which the required report data exists – particularly when the report in question has a large answer set. Therefore, regardless of the size and complexity of the query, or the amount of information it will return, users who leverage in-memory analytics will get faster answers, without any negative impact to operational systems.
The second key benefit is affordability. In the past, memory costs were prohibitive, and 32-bit architectures offered limited processing power and storage. But today, the costs associated with memory continue to decline, while 64-bit computing delivers much greater memory stores. This makes in-memory analytics a less expensive and more feasible way to operate an enterprise business intelligence environment.
In-memory analytics can also dramatically reduce dependence on IT personnel, because it reduces the data management burden for reporting and analysis purposes. For example, it can potentially eliminate the need to build, deploy, and maintain OLAP cubes. And, it can cut down drastically on data warehouse maintenance.
The benefits of in-memory analytics are clear, and experts agree that more and more companies will utilize this technique in the coming years. In fact, Gartner anticipates that by 2012, 70 percent of Global 1000 companies will load detailed data into memory as the primary means of improving the performance of BI systems.
