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HomeBusiness TechDigital TransformationHow in-memory technologies transform business

How in-memory technologies transform business

Photo by Tima Miroshnichenko: pexels

In recent years, most companies have undergone a digital transformation due, in some part, to its apparent benefits, but also due to necessity. Digital technology has impacted businesses fundamentally, and resisting the call of the times can lead to business stagnation and even loss of revenue in the long term. Forward-thinking enterprises are leveraging big data, machine learning, and analytics to further push their business forward. According to the International Data Corporation (IDC), over 50% of all global IT spending will go toward digital transformation. This says a lot about the digital trend in business today. Companies that fail to compete in the digital landscape will find that their core markets are now digitally dependent.

Looking back, the main objective of emerging technologies is to disrupt business enough to prove that the impact of digital technologies and philosophies in business is beneficial to all. Terms like big data, machine learning and artificial intelligence (AI) have become buzzwords in business today, and many executives are now considering what these concepts can bring to the table. Because data has a major business impact, in-memory data grids have essentially become a business enabler for companies going through a digital transformation.

A common challenge for every business is how to efficiently process big data—that is, massive amounts of data that are constantly moving and changing. This is where in-memory data grids come in. By processing data in parallel using random access memory (RAM), a data grid is able to process massive amounts of data at lightning speeds.

Keeping Big Data in Memory

It’s important to know how in-memory data grids work to determine their overall impact. Traditional data processing involved constantly moving data between RAM and disk, which resulted in bottlenecks that further slowed down processing. Data was stored on disk but needed to be moved to RAM for processing, and this movement from disk to RAM is the slowest step in the process. In-memory data grids let the data reside in RAM so that they can be processed immediately.

Essentially, in-memory data grids are composed of computers networked together so their combined RAM can be used to share data. Pooling RAM and computing power ensure the best and fastest possible application performance. This allows in-memory data grids to process data at high speeds for large-scale applications that require large amounts of RAM. By running special software on each computer in the network, the computers aren’t dependent on disk-based storage and there’s minimal data movement within the network. Each computer has its own data structures that are shared across the network, with the software keeping track of all the data at the individual level so data sharing is easy whether it’s being shared to applications or other nodes. Keeping data synchronized across the network and in each node makes data updates and retrieval a simple process compared to before, consequently speeding up application development because the bottlenecks caused by disk have been eliminated.

In-memory data grids are a common choice when it comes to the management of big data because it processes data through a distributed cluster. Huge amounts of data are processed against the full dataset, popularly known as “persistent store.” With persistent storage and the optimization of data so that it can reside both on disk and in memory, a data grid allows the amount of data to exceed the amount of memory. Speed is carried over even after a reboot because there’s no need to wait for the entire dataset to load into memory; after a system reboot, users can expect optimal performance immediately.

In-memory data grids also make scaling easier for businesses because data is distributed and stored on several servers. Organizations can simply add or reduce servers as needed. The platform’s networking and clustering capabilities also allow for additional features like high availability, data replication, failover, and synchronization of data between clients.

The In-memory Approach to Business

Fortunately, in-memory data grids have been available for years and have been used by businesses to help in the faster processing of data that leads to improved ROI. The faster the data can be processed, the sooner a business can make sound and data-driven decisions. This makes in-memory data grids a vital business tool, opening up use-case opportunities ranging from IoT to web-scale applications.

Agility and flexibility are two of the main goals of every business today, especially in this ever-changing, ever-evolving landscape. Tomorrow’s innovations could quickly and easily be yesterday’s technology, so businesses must learn to be nimble enough to pivot at a moment’s notice if need be. In-memory data grids tick all the boxes in this regard because it provides high throughput and never-before-seen data processing speeds while also lowering operating costs. As the cost of RAM goes down, it also becomes more and more feasible to implement an in-memory data grid as a business solution for a variety of use cases involving machine learning, web-scale applications, and in-memory computing in general.

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