Traditionally, businesses retrieve their data for processing from hard disks. The process works, but unfortunately, it is slow, especially when large volumes of data are involved. Consequently, the process is fast becoming inefficient and businesses can no longer fully exploit data benefits using this method.
Luckily, in-memory data processing technology is evolving fast as a viable solution to traditional methods of data processing. The new method uses computer RAM to store and retrieve data and thus achieving an increased speed of between 10 thousand to one million times more compared to the traditional hard disk method.
Understanding in-memory data processing
In-memory computation is the process of working on data purely on RAM or flash memory. As data quantities in businesses continually grow, processing it using traditional ways becomes ineffective and slow because hard disks have many limitations.
To eliminate this setback, businesses consolidate several computer RAMs so that data is read and written on a larger RAM. The result is data processes that are thousands of times faster compared to traditional methods.
Instead of storing data in disks, the RAM becomes both the storage and processing tool. Every data obtained from RAM is in real-time and can help businesses take quick and immediate responses to reports and recommendations.
Why do businesses choose in-memory data processing?
While implementing its marketing analytics strategy, businesses heavily depend on big data to understand market behavior, customer needs, and product improvement strategies. The increasing need for more data leads to another need for fast, predictive, and reliable means of processing, analyzing, and reporting on outcomes.
A business may opt for the more complex and slower method of processing data from disks but it’s time-consuming and slow. Businesses are always innovative and they choose the simplified way that can give optimized results.
The more hybrid option is in-memory computing which saves time by working at a significantly greater speed. The result is more volumes of processed data that not only saves time but also cost.
How does in-memory processing work?
The process purely relies on information stored in RAM and thus eliminates every slow process exhibited by hard disk processes. Usually, hard disks have limited storage and as they get filled with data, they process at slower speeds. A lot of processes are involved when locating data from a hard disk, reading it, or writing it.
In-memory data processing is not slowed by latency and because it’s in real-time, it is retrieved faster by ten thousand times. Several computer memories are consolidated as one and hence the application can break data into smaller portions that are then spread to each computer so that they all run in parallel. Because each small amount of data is processed from a different RAM, the speed is magnified exceedingly great. The entire process is economical, fast, and reliable.
Advantages businesses get by using in-memory processing
For businesses to perform better and faster, they need to constantly use available data to help them understand market trends, improve on product quality, and offer cutting-edge customer service. These results rely on systems that can process and analyze data fast. In-memory processing has availed that solution and provided better business intelligence and several advantages to businesses.
High speeds and more scalable results
In-memory computing does not rely on hard disk storage to store or retrieve data. Instead, it purely relies on RAM to deliver optimized speed and task scalability with uninterrupted performance. The RAM parallelized distributed processing enables high data scalability.
It uses technology that enables several computers in various locations to share memory and thus process data at 1000 times faster than any traditional methods.
Enabling technology
Without in-memory data processing, some technologies like blockchain and geospatial would not be possible to implement. These are some of the technologies that allow the smooth distribution of data without copying.
Because these technologies process huge volumes of data at any given minute, it cannot be stored or retrieved from hard disks because it would be too slow and would be possible to copy it. Also, these systems require processing information in real-time, which is currently only possible through in-memory computing.
Availability of data in real-time
In-memory data processing is providing predictive analysis to businesses at greater speeds than ever. RAM stores data that is ready to use by consolidating it from multiple sources and distributing it to several computer RAMs in different locations.
The result is big data streaming that is both current and old. RAM is easily compatible with machine learning analytics to help give real-time insights that are logical and can be applied instantly for the benefit of the business.
Easy detection of errors
Sometimes errors or omissions can happen in a business environment and it affects a company’s business cycle. With in-memory computing, the error can easily be detected, analyzed and the relevant action taken because it responds in real-time for real-time decisions.
Better security on business systems
Real-time data availability also helps businesses to prevent negative occurrences like cyber-attack, system breakdown, errors in order distribution and delayed services to customers.
Scenarios for in-memory computing use
Any type of business that is relying on big data to succeed in its business transactions can use in-memory data processing to achieve its goals. Its application is more relevant in businesses that deal with large numbers of customers and tens of thousands of small or big transactions daily.
Some of the companies that are already using this technology are companies offering telecommunication services, transport services like airlines, trains, or cruise ships, big retailers, insurance service providers, and financial service companies like banks, mortgage businesses, and money lenders.
They use in-memory data processing to detect risks like frauds, money laundering, online orders, payments, product promotion trends, and customer behavior. The technology is not limited to these companies and can be applied to any business that requires business predictions using big data.
The most recent user of in-memory computing is the manufacturers of self-driving cars because they need to process big amounts of data to predict car safety, errors, and the areas that require improvement. Cryptocurrency companies also rely heavily on big data, which is processed in-memory to predict outcomes, trades, and future trends.
