In-memory processing: The next step in data analytics and BI

In-memory processing represents the next step in data analytics and BI. Lean how you can leverage the power of real-time data processing.

In-memory processing represents the next step in data analytics and BI. With companies processing terabytes of data, it is important to invest in technology that can process large data sets, quickly. Sadly, traditional disk-based processing is no longer up to the task. While processing large data sets is manageable, the process is slow, inefficient and it prevents companies from taking full advantage of data analytics. Fortunately, there is an alternative in the form of in-memory processing – an evolving technology that is rapidly gaining traction.

What is in-memory processing?

In-memory processing is the processing of data using RAM or flash memory. It is an emerging technology that is replacing disk-based processing because it is better suited to the demands of BI and data analytics. Memory-based processing is said to increase data access speeds by 10,000 to 1,000,000 times over disk-based processing, making it better suited to the demands of analytics.

How is it different from database processing?

Disk-based processing refers to relational database management systems structured by query languages like SQL. Programmers must first load the data from the disk (usually done with several tablets and structures) before processing the data for business needs. It slows down the rate of data processing because loading large volumes of data from a disk creates bottlenecks and hampers performance. By contrast, in-memory processing loads data onto a RAM or flash memory minimising bottlenecks and increasing the rate data is processed.

Relational databases based on SQL are optimised for row-based databases, while in-memory relies on column-centric databases. Row-based databases arrange data in a single row, where different variables – for example, first name 1, last name 1, first name 2 and last name 2.

However, in-memory processing relies on column-centric databases or columnar storage. With this method of data storage, similar variables are grouped in the same category, for example, first name 1, first name 2, last name 1, last name 2. Row-based storage is best suited for transactional processing but is unsuitable for the demands of BI, which requires only partial processing but deeper, more complex calculations.

By contrast, column-centric storge is more suited to the demands of BI, making in-memory processing more suitable for BI and analytics.

What are the advantages of in-memory processing?

The biggest advantage of in-memory processing is speed. Working from RAM or flash memory removes many of the bottlenecks found in disk-based processing. Thus, businesses are able to analyse large datasets in real-time, which generates better insights from data analytics. Along with better processing speed comes higher storage capacity and better transfer speed. These advantages are possible because data can be stored in in-memory databases, while several processing units (computers) work together to deliver different clusters of data.

Big data comes in two formats, structured and structured. In the past, businesses have struggled to store unstructured data like images and videos in conventional databases. However, with in-memory data processing, this is no longer an issue because it is easier to store both structured and unstructured data. Thus, it is easier to get richer and deeper insights from data analytics.

Are there any drawbacks?

Despite the obvious benefits of in-memory data processing, we would be remiss not to mention some of the drawbacks of this method.

The main disadvantage of in-memory is its reliance on computer systems. If something were to happen to a computer, especially to the RAM or flash memory, then data is compromised. Hence, information is not as secure in-memory compared to on disk. The other disadvantage is cost – memory-based systems are incredibly expensive compared to their disk-based counterparts.

As such, the technology is only feasible for large corporations with massive data warehouses. However, I suspect that the price structures will change in the long-run because of technological advancements.

Key takeaways

In-memory processing allows companies to process big data at a faster rate, and deliver insights in real-time, something that is not possible with disk-based processing. The new technology uses RAM or flash memory to process data, significantly increasing processing speeds and removing bottlenecks found in older data processing methods. The faster results are possible due to new, and innovative technologies like columnar storage.

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