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    Home»Business»Factors Driving In-Memory Data Grid Market Growth
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    Factors Driving In-Memory Data Grid Market Growth

    JackBy JackApril 20, 2021No Comments5 Mins Read
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    Industry trends, such as mobile, cloud and a growth in the number of connected devices, are driving the need for faster, more scalable computer systems. Businesses need faster systems to provide the data processing required for modern consumers who expect immediate results.

    An in-memory data grid (IMDG) offers high availability and scalability as it allocates data across many sources. It makes it possible to build and run large-scale applications by deploying RAM and the processing power of numerous computers to offer better application performance. With an increasing demand for applications that are super-fast and can process a huge amount of data per second, the in-memory data grid market is growing all the time.

    What is an in-memory data grid (IMDG)?

    An In-Memory Data Grid (IMDG) stores data in-memory instead of utilizing disk as the primary storage mechanism. This makes it way faster than traditional DBMS systems. Keeping data in memory is only one reason why IMDGs perform much faster than disk-based databases.

    IMDGs are built in such a way that they support horizontal scaling and the ability to add nodes on-demand in real-time. By utilizing multiple CPUs across the cluster, they can process computations that work on different data sets in parallel.

    What’s driving the IMDG market?

    Many factors are driving growth in the IMDG market, particularly the increase in consumer expectations. Another key driver for IMDGs is the ability to implement them in a current system with few or no changes.

    An Increase in consumer expectations

    Advances in technology mean that consumer expectations have increased. Consumers expect constant access to the internet and mobile device integration.

    They want websites or applications that are quick, responsive and highly available.

    User interfaces need to be intuitive and feature-rich. They expect real-time responsiveness and seamless integration of applications. If a website is slow or a sales application is difficult to use, they will quickly move on.

    Growth of interconnected devices (the Internet of Things)

    Today devices of every kind are electronically integrated with each other and the internet. There’s an expanding range of highly varied data sources that need managing. Devices need to be always available and ready to process a large amount of data. They need connectivity, fast performance, security, and scalability. Supporting the Internet of Things requires robust IT architecture.

    Consumers get frustrated when they’re trying to use an application and it moves slowly, freezes, and ultimately fails. If a site freezes while making an online shopping purchase, the customer may leave the site. Shopping cart abandonment is common if a user experiences a glitch during an online retail sale. Very often, a system is slow because it isn’t architected to scale its application tier to meet user demand.

    The need for scaling

    In many systems, retrieving data means going to distant legacy mainframes, databases, or external data sources. Retrieving data for every request is expensive and time-wasting. Many data stores aren’t able to scale quickly enough to support the needs of businesses today.

    With an IMDG, it is possible to add nodes ‘on the fly,’ which enables scaling. Adding nodes provides virtually unlimited processing power and memory. Real-time scaling means that users don’t have negative experiences when workloads increase and processing spikes.

    IMDGs allows businesses to easily process data in real-time, conduct business market analysis, enhance application performance, and much more.

    The ability to offload shared services

    The growth of applications can cause strain on existing back-end databases, mainframes, and shared services. Offloading is the term used to describe removing the strain on them.

    Computer systems can leverage application-tier scaling and caching to offload pertinent data from the data tier to the application tier. This keeps live data near the user and reduces processing requirements on the data tier.

    For instance, an application may use the social network preferences of a user to pull up recommendations. Rather than retrieving data from the network over and over, keeping shared services data in memory for fast access makes sense. IMDG technologies cache frequently accessed data so that the application doesn’t need to go all the way back to the database for every request.

    IMDG use cases

    An IMDG is ideal for use in a situation where many users concurrently access an online system and keep accessing similar data. They don’t want to experience latency or failure when they do this. Here are just three of the many use cases of an IMDG.

    Real-time fraud and risk management

    Fraud management is increasingly becoming an issue of prime importance for businesses all over the world. The sophistication of people committing fraud is increasing and by the time an alert is generated, they have already moved on to the next target.

    Transaction processing gateways don’t only have to deal with a high volume of transactions but they must be able to determine their legitimacy in milliseconds. By analyzing transactions in real-time for suspicious patterns, businesses can deliver a decision on the legitimacy of a transaction immediately.

    Better reporting

    Reporting has become stricter and businesses need to report in a timely and accurate manner to the government or other regulatory agencies. By adopting an IMDG, they can perform analytics, unify risk data, and report much more easily than when using traditional approaches to manage data.

    Financial risk analytics

    As market data ages, it is less useful for driving trading decisions. Outdated information can negatively affect profits. Data ingestion and risk calculation must happen as quickly as possible.

    Using an IMDG solution allows for ingestion of streaming data as fast as it becomes available from an information provider. Performing risk calculations is possible in parallel in near real-time. Furthermore, complex modeling can be done with no drop in latency, just by adding additional nodes.

    A final word

    Leveraging IMDGs relieves scaling and processing bottlenecks in systems. This has a direct impact on customer experience by improving performance, reliability and scalability. Cost-effective IMDG solutions ensure that customers have the best user experiences. This gives businesses a competitive edge and offers them more chance of success.

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