Big data is data that exceeds the processing capacity of traditional databases. Handling big data is becoming a huge challenge. Size, speed and variability are critical issues in the big data environment. The potential of big data is enormous but it needs to be processed in order to get value from it. In order to do this, rethinking data storage and implementing the correct infrastructures is necessary.

An in-memory data grid is considered the best option for facilitating big data. The global In-Memory Data Grid (IMDG) market is expected to register a CAGR of more than 11% during the period from 2020 to 2025, according to a Research and Markets report. IMDGs are suited to handling the size, speed and variability of big data.

Conventional databases are no longer sufficient

The amount of data generated is growing at an unprecedented rate. With more use of the cloud, businesses of all sizes are generating more data than ever before. This data can provide insights with great potential value for businesses.

However, data growth is creating certain challenges that make it hard for applications to meet expectations. Businesses are facing technical and economic challenges when it comes to scaling the data tier. Traditional data tiers are fairly rigid, increasingly complex and expensive. Businesses are in need of more flexible applications that they can use in a variety of different environments.

A growing need for real-time processing of data

Processing of data in real-time can facilitate many applications that are critical to business performance. Real-time insights can improve business performance in many different ways. They can improve customer experience, help identify fraud, enhance marketing, eliminate waste, and improve supply chain and risk management.

What is an in-memory data grid?

An in-memory data grid is a distributed caching solution that helps applications meet requirements of scale, performance, reliability and availability. An IMDG lives in a distributed cluster. Pooling of random access memory (RAM) means that applications are able to share data with other applications in the cluster. Specialized software runs on each computer in the cluster to coordinate access to data for applications.

Features of an in-memory data grid

An IMDG has a completely different architecture to an in-memory relational database. Some of the features of an IMDG are:

• Distribution and storage of data is scattered on multiple services.
• The data is stored in the main memory (RAM) of the servers.
• All servers operate their data in active mode.
• You can add or reduce servers based on necessity.
• Data is often stored in the form of objects, like maps, queues and lists.
• There are no traditional database features like tables, indexes or process managers.

Using main memory as a storage capacity means overcoming the weaknesses of limited space storage and reliability. The distributed architecture with horizontal scalability of an IMDG solves issues of limited capacity. A replication system as part of the grid deals with any reliability issues.

Why IMDGs are so beneficial

IMDGs can boost application performance to accommodate activity spikes, large volumes of transactions, and all at in-memory speeds. Their distributed, fault-tolerant nature makes them suited to underpinning today’s decentralized, global businesses.

One big benefit of IMDGs is the ability to receive real-time accurate information. As data grids move data closer to the application, this enables fast, low-latency access to information that could be critical to a business.

An important aspect of business today is to keep customers engaged and loyal. This means applications need to operate seamlessly and offer consistent service, even when activity is at its peak or traffic is spiking unexpectedly. An IMDG is flexible enough to meet required response times.

As the amount of data continues to grow, this can result in bottlenecks to traditional back-end data stores and performance issues for applications. Data grids can act as an intermediate layer between a relational database and an application, allowing for fast or frequent access, or access across geographies. By reducing the load on traditional databases, they offer quick, scalable performance and time-sensitive services to end users.

An IMDG can efficiently integrate with a complex, rigid data tier. It therefore makes data tier integration easier. Businesses today have diverse IT environments – on premise, in the cloud, contemporary or legacy. Data grids can be used in a variety of industries and use cases.

Use cases of IMDGs

• Payment processing: During a payment transaction, calculations must happen in a minimal time window. Fraud detection is one of the key factors in gaining a competitive advantage. Payment processors can run multiple fraud algorithms at the same time for more accurate scoring that reduces fraud and false positives. IMDGs allow processors to run multiple algorithms and still maintain a positive customer experience with a millisecond-level response.
• Large scale simulations: Such simulations can help to get a clearer understanding of what may happen in the future by considering a number of different factors. Firms in the financial services industry may run them to better understand the risks they face. As large scale simulations involve many calculations and variables, IMDGs can help to run them quickly.
• Web and mobile customer experience: IMDGs can provide a seamless customer experience across web and mobile applications.
• Seasonal fluctuations: IMDGs can achieve reliable performance and scale when businesses go through seasonal fluctuations.
• Global Management and distribution of resources: Global companies can use IMDGs to manage and distribute resources across multiple data centers in real-time.

A final word

As the amount of data grows exponentially, traditional databases will reach a limit. This makes it necessary to reconsider traditional approaches and technology. In-memory data grid technologies can provide the solution.

By loading terabytes of data into memory, IMDGs are able to work with most of the processing requirements of big data. They allow businesses to achieve a high level of performance and flexibility for their applications. They can streamline interactions with the data tier to take advantage of business opportunities that give them a competitive edge.