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IMDG vs. IMDB: Which is Best for My Business?

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As data becomes more a driver of businesses than ever before, the game becomes a race of data processing systems with the finish line being real-time data analytics. Without the proper tools to process the growing amounts of data, businesses doom themselves to band-aid solutions and unfounded business decisions that hinder instead of improve overall operational efficiency.

Data may be complex, but technology has made significant innovations through the years when it comes to data processing and analytics. However, businesses still face challenges related to unpredictable workloads, made more complicated by a world disrupted by a global pandemic. The crisis has affected industries around the world and has kept markets in constant flux. Customer sentiment and demand have become volatile elements of the equation–an equation that was hard to decipher and solve in the first place, pandemic or not. In-memory data grids or IMDGs reduce the complexity of data processing and analysis to help businesses build solutions with maximum efficiency at reduced costs. Organizational data requirements have changed, and traditional solutions like in-memory databases (IMDBs) simply can’t keep up with today’s high expectations.

In choosing the ideal computing platform, the question almost always devolves to one of choice: IMDG or IMDB? As such, it’s vital that you know the difference between the two so you can determine which one is best for your specific use case.

The Argument for In-memory Databases

An IMDB is a database system that distributes data across multiple computers to speed up data processing. It provides support for popular data-processing API’s like machine learning, key value, and ANSI-99 SQL so it’s preferred by organizations that are either re-engineering legacy applications or developing new ones. Similar to an IMDG, it doesn’t rely on disk-based storage so it provides fast data access and storage for systems that don’t have a disk drive.

The storage of data in-memory, however, is where the similarities of the IMDB and IMDG end. When it comes to architecture, they are worlds apart–and this is arguably the main advantage of an IMDB. In an IMDB, the typical three-layer architecture of an IMDG is reduced to two, which simplifies the structure and minimizes the moving parts within the system. While this helps make data processing significantly faster, it also makes it harder to implement in current application systems. Integration to more modern architectures requires significant dataset changes from existing databases.

The main consideration when looking at an IMDB as a solution is that it’s a system of record. This means that an IMDB implementation also requires an organization to have a failsafe that will protect data in case of unexpected downtimes. There are also sustainability challenges with an IMDB implementation because the system is designed for vertical scalability. This makes scaling your system very expensive, since it will require upgrading regularly to the latest available hardware and software. By nature, scaling vertically will always have a breaking point, and once this point is reached, the only alternatives are to start from scratch or find a suitable platform that will allow you to start where you left off. Still, an IMDB can be a viable solution, depending on an organization’s needs. Scalability issues, however, severely limit the possibilities for web and mobile applications that handle complex queries and concurrent requests.

The Argument for In-memory Data Grids

The IMDG is a preferred solution by companies due mainly to its ability to process data at high speeds and its relatively easy scalability. High-speed data processing is made possible in IMDG implementations by distributing data and workloads across a computer network. Despite this distributed structure, however, IMDGs still have a unified API that makes it easier to manage data expansion and analytics acceleration. The distributed structure also makes IMDGs easy to deploy, so it’s an attractive solution for companies that want to accelerate existing services and applications. Its use of RAM instead of disk also reduces bottlenecks caused by frequent data movement to and from disk and across the network.

Easy scalability is also a feature of the IMDG that companies prefer because it helps in creating cost-effective solutions that will provide value in the long run. This is a major differentiator when choosing the ideal computing solution for your business. An IMDG runs specialized software on all computers within the network so data is always synchronized and each computer retains its own data structure even if the computers share memory and processing power. Scaling an IMDG implementation is as simple as adding a new node to the computer cluster.

One of the main draws of the IMDG, however, is its ability to maximize throughput and decrease latency at the same time. This is done by allowing both applications and their data to collocate in the same memory space. By doing so, IMDGs address the complexity of data movement and helps simplify data governance to allow for real-time data analytics. Having real-time data and the power to process them in seconds allows businesses to make data-driven decisions based on real-time insights gathered from relevant data. An IMDG implemented on a hybrid environment can also run on on-premise and cloud systems, making it an ideal and cost-effective solution for companies that rely on processing large amounts of data.

Computing for the Long Haul

In choosing a computing platform, the first step should always be assessing your current systems and how they work for you. If what you need is something as simple as accelerating online analytical processing (OLAP) and online transaction processing (OLTP), an IMDB may be enough. If, on the other hand, you’re developing new applications and want to do it at scale, an IMDG can provide the required performance and low-latency access to data to make that possible.

The differences between an IMDG and IMDB may be technical, but knowing whether these differences will affect your choice is entirely dependent on the current—and future—needs of your business.

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