The in-memory analytics market is rapidly expanding and is set to reach $10.85 billion by 2027 compared to $1.89 Billion in 2019, an annual growth rate of 24.4%, according to new market research.

 

When it comes to processing data, speed is essential, especially in this modern world where analysts need to process thousands of rows of data at once. 

 

Every business should strive to increase its data collection and data analysis capabilities; digital transformation is mandatory to survive and thrive. Analytics-driven organizations harness data and leverage it to boost revenues, optimize operations, and provide better products and solutions for customers. Businesses that are digitally transformed use the power of analytics to generate significant competitive advantage. 

 

The velocity and volume of data are ever-increasing, surpassing all previous expectations. However, more data means more complexity and time spent processing requests. 

 

Here’s where in-memory analytics comes into play. This new technology is a Business Intelligence methodology used to solve complex and time-bound business situations. 

 

In-memory analytics applications work by increasing the speed, performance, and reliability when validating data. Business Intelligence distributions are specifically disk-based, which means the application queries data are stored on physical disks. In comparison with the in-memory analytics where the data exists in the server’s random access memory (RAM).

 

Why is In-Memory Analytics More Popular Now?

 

According to a new market study, in-memory analytics is attained through the growth and acceptance of 64-bit architectures, which can handle more memory and large files, and is an overall cost reduction compared to a 32-bit. 

 

In-memory analytics solutions are designed to improve the speed and recovery of the BI system in comparison to standard disk-based business intelligence, which in some scenarios takes a long time to process extensive database systems.

 

SMEs are the main drivers for in-memory analytics market growth. Due to their reduced size and eagerness to experiment, small and medium businesses are more prone to adopt new technologies faster. 

 

Another factor that adds to the growth of in-memory analytics is the increased implementation of Real-Time Analytics to track businesses’ digital transformation, set up metrics, and follow progress to reach goals. 

 

How Does it Work?

 

In-memory computing is based on two principles: data storage and scalability (the capability of handling increasing volumes of data). This is accomplished by using the RAM and parallelization. 

 

This technology processes data using RAM, eliminates disk-based bottlenecks, and obtains a much higher processing speed.  

 

According to Intel, with in-memory computing, data is stored directly in system memory. This architectural approach dramatically reduces latency by eliminating the time spent seeking data on the disk and then shuttling it closer to the CPU. 

 

One of the reasons why in-memory processing is not yet widely adopted is because it operates on DRAM memory (Dynamic Random-Access Memory), which is not cost-efficient for large volumes of data. However, technological advancements and the decrease in DRAM costs will sustain the technology’s growth in the coming years. 

 

Other technical components that contribute to in-memory analytics speed of processing are columnar data storage and massively parallel processing. According to Intel, instead of the traditional two-dimensional data structure, with rows and columns, in-memory analytics data has a one-dimensional, linear structure. In-memory analytics makes full use of multi-core, multi-thread processor capabilities, which drastically reduces latency.

 

How Can We Help?

 

Before starting any analytics initiative organizations need to have their data capabilities and data needs assessed. We can help you determine where to start your digital transformation, or help you take it to the next level if you have already embarked on one. Contact us if you want to become a data-driven organization to maximize your results and gain a competitive advantage across all business avenues. 

About the author

Sebastian Stan

Sebastian is a journalist and digital strategist with years of experience in the news industry, social media, content creation and management, and digital analytics.



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