Data Grid Primer: How To Leverage In-Memory Data Grid Technology

In-Memory Data Grid Technology

In 2012, companies generally had enough database capacity to handle and process daily customer activity; however, industry analysts at IDC have predicted a massive rise in mobile internet usage on smartphones and tablets.

These mobile data streams are being further augmented with distributed, interconnected systems, such as those now found in cars, household appliances, and utility sensors. The combined transactional volumes from this “Internet of Things” means that traditional database systems may soon reach their limits.

Enterprises are now looking to scale systems in a way that allows them to maintain performance and take advantage of the business opportunity presented by mobile. Whereas traditional data tiers are often too rigid, complex and expensive to meet the mobile data challenge, in-memory data grids are designed to provide the necessary levels of performance and flexibility at scale.

As a result, in-memory data grids are quickly becoming a popular approach to modernise software and accommodate complex performance requirements. Here are several reasons why this technology is gaining steam:

  • Provide accurate, real-time information: Having real-time access to accurate information often makes the difference between right and wrong decisions. Data grids move data closer to the application, provide fault tolerance, and enable fast, low latency access to business-critical information.
  • Meet high uptime and responsiveness expectations: To keep customers loyal and engaged, applications must perform with consistency and deliver seamless service, even during peak activity times and unexpected traffic spikes. A data grid can elastically spin up and down distributed nodes helping meet required response times.
  • Process significantly larger transaction volumes: As the amount of data grows, reads and writes to traditional back-end data stores can become a major performance bottleneck for applications. Data grids act as an intermediate layer between relational stores and front ends to meet data-retention requirements and promote extremely fast, scalable performance.
  • Efficiently integrate with a complex and rigid data-tier: Deploying new applications or updates should be a streamlined, straightforward process. Data grids reduce the overhead and headache that comes with data tier integration, allowing organisations to more quickly go to market.
  • Interoperate in mixed IT environments: Today’s organisations have a diverse IT environment. Applications and infrastructure may be on-premise, in the cloud, legacy or contemporary. Data grids can serve as a data abstraction layer and offer the flexibility to work in a variety of environments, applications, platforms, and databases.

An in-memory data grid is an intelligent distributed caching solution that lets applications meet tough requirements of performance, availability, reliability, and elastic scale. It can act as an intermediate layer between the relational database (RDBMS) and the application, holding data that is transient or temporal in nature, or needed for fast access, frequent access, or access across multiple geographies. The RDBMS may subsequently be freed up to store data infrequently accessed or modified.

Simply put, a data grid can act as a supercharger for an application. It boosts application performance to accommodate larger volumes of transactions, spikes in activity, and all at in-memory speeds. And, due to their distributed, fault-tolerant nature, data grids are well-suited to support today’s global, decentralised businesses.

Data grids can be used in a variety of different industries and use cases today. For example, providing a seamless customer experience across mobile and web applications, achieving reliable performance and scale through seasonal business fluctuations, or reducing the load on traditional relational databases to offer time-sensitive services to users. Data grid technologies are also increasingly used by global companies to manage and distribute resources across multiple data centres in real time.

Across a spectrum of industries, organisations today face challenges that traditional data tier scaling cannot easily resolve, whether that is providing access to real-time information, handling large transaction volumes, meeting high uptime expectations, or integrating with a diverse and dynamic IT environment.

In-memory data grid technologies empower organisations to achieve a high degree of flexibility and performance for their applications and streamline interactions with the data tier – giving them an edge they can use to take advantage of business opportunities and go to market faster.

Christina Wong

Christina Wong is a senior product marketing manager at Red Hat, responsible for Red Hat JBoss Data Grid and Red Hat JBoss Portal Platform. She holds an MBA from Babson College and has 10 years of engineering and business experience in high-tech.