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The traditional data management systems were created to work on data stored on disks that are by far slower compared to memory databases. The main focus on managing transactions was to improve concurrency and, specifically disk access performance.

Business owners and programmers paid little attention to making memory access efficient but throughout the years, data volumes kept growing. Today, many businesses are adopting in-memory computing more than ever. The in-memory data grid is important in turbocharging analytics so that businesses perform millions of transactions per second.

Adoption of the in-memory data grid

The in memory data grid features provide mature solutions that make it possible for businesses to achieve the data processing speed and volume needed for both digital transformation and all-rounded customer experience initiative between their channels. Specific companies are using in-memory computing platforms as a single data source pooled from multiple sources. They use the data to get insights for performance, brand exposure, company value in the market, and investor positions.

Beyond these insights, the companies manage to accelerate their business processes to real-time business transactions, back-office activities, and related accounting activities. The millions of transactions per second provided by the in-memory data grid’s super features have helped businesses achieve a greater magnitude of growth, many times higher than ever.

Data volume versus memory size

During the in-memory deployment phase, IMDG is set between application and data layers on a cluster of server nodes so that it shares memory with the CPU. The deployment can either be done on a private cloud, public, hybrid, or on-premise environment. When deployed on any of these environments, the IMDG copies data from various sources such as Hadoop and disks and saves it into random access memory. If scaling is required, a company only needs to add a new node to the server cluster.

Sometimes the data volume outgrows the available memory meaning IMDG has less memory to store the entire data. A company will have two options, in this case, to either purchase more memory or more disks. If a business decides to purchase more disk space due to the cost of memory, IMDG can still optimize the data by availing the frequently used data on RAM while the less used data resides on disk. The IMDG capabilities allow data volume to exceed memory size without affecting speed.

Solving memory bottleneck challenges

The latest statistics show the number of mobile phone users globally has grown to 83.96%. The number of customers using mobile phones to access services from businesses has grown too. As a result, companies are quickly developing mobile apps to bring services closer to customers. Connecting more customers into their systems has brought its share of challenges. The amount of memory available diminishes as the volume of data generated increases. The biggest cause of memory bottleneck in companies is insufficient memory due to a high number of running applications and thousands of transactions requested by customers.

Memory bottleneck hurts performance because it slows data migration between the CPU and memory. With an increased number of transactions, the company operations slow down, which can lead to an increased number of customer complaints and huge losses. Other causes of memory bottleneck could be due to limited allocated memory, poor applications configuration, malfunctioning system, or insufficient RAM. These challenges make it impossible for the memory to handle high volumes of transactions or to run its memory-intensive applications. The business experiences an increase in timeouts, less active queries, and longer execution time.

The in-memory data grid offers reliable solutions to memory bottleneck challenges. Although it leverages memory storage like many other applications, the IMDG is different from them all in various ways. It distributes its data on several servers. Data stored in IMDG is object-oriented and it’s not relational. It is possible to add or remove data stored in IMDG at any time without affecting operations. The distributed architecture of IMDG provides data replication due to its horizontal scalability features. It stores and retrieves data through its concept of the in-memory key that helps it solve all reliability issues such as memory bottleneck.

It doesn’t matter the number of queries per second made or the number of active applications running on RAM at any given time. Millions of customers can connect to the system through their mobile phones or any other IoT gadget and never experience downtime due to memory bottleneck. Due to the elimination of bottlenecks, the in-memory data grid can seamlessly perform millions of transactions per second.

Combined scalability

Growth is appreciated on the one hand but on the other, companies must be prepared to deal with challenges that accompany growth. The primary challenge recorded by every growing company is capacity. This is a broad term that could mean an array of things. First, it could mean understaffing where the number of employees can no longer handle the volume of transactions. It could also mean limited physical space that requires the office to increase its physical space. The financial limitation is another issue and climaxing it all is data storage limitation. To overcome data limitations, there has to be a long-term strategy that involves rolling out new apps or accelerating the current ones.

An in-memory data grid can be used as a solution to accelerate current apps and accommodate new ones. Its ability to distribute data in multiple server clusters helps accelerate scalability. IMDG combines both new and old applications into one platform, enabling them to take advantage of a distributed system and a reliable store that can process millions of transactions per second.

Millions of transactions per second now and in the future

The IMDG is built with features that enable it to grow as the business grows. This is because of its ability to accommodate both old, new programs and improved hardware. It’s an engine that can handle all complexities surrounding data flow and processing.

The modernized IMDG can apply both machine learning and deep learning to handle operational data in real-time. IMDG is a leading technology used by enterprises today to process millions of transactions per second. As the deployment of its deployment strategies intensifies, it is destined to remain a favorable technology for many businesses around the world now and in the future.

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