Distributed data grids were developed to bridge the gap between large-scale applications and rapid access to data. This has been highly effective because ever since the advent of distributed data grids, large-scale applications have significantly reduced their latency.
A distributed data grid seems to have a simplistic architecture. However, there is more than meets the eye. Fortunately, there are readily available products that are available instantly to software developers and organizations. Here is a breakdown of how this technology works, its benefits, and its use cases.
Using distributed data grids for computing
Distributed data grids enable applications to use a cluster of computers within the grid to have a view of data stored in memory. This makes the availability of data universal throughout the cluster of the grid and easily accessible to applications. The core purpose of distributed data grids is faster computing speeds, especially if the data is consistently updated or edited.
The architecture of distributed data grids allows applications to read and write data in a central database through various nodes. In layman’s terms, a distributed data grid is a pool of computers that offer up RAM to boost the availability of the latest updated data. However, it goes a step further than just data processing and storage, but a data grid facilitates complex computing systems. The use cases of this technology are very vast, depending on the industry you are in.
Parallel processing of large data sets
Data grids have another major benefit that makes them a step ahead of their time. Normally, data processing tools are either write-only or read-only, which limits their capabilities. In traditional scenarios, you might end up using various database query languages and frameworks. With data grids, you can simplify this since you do not have to worry about the nitty gritties of data management.
However, at the same time, you gain access to highly advanced parallel processing of large data sets. Whenever an edit is made in any node within the cluster, all the other applications will be automatically updated. The data edits cycle through the node to the database and come out reflecting those changes in each data access node. All of this is processed with very little latency since all the relevant data is cached in the nodes and not retrieved directly from the database.
Improvement in application development
All of these benefits have a direct bearing on the development phase of applications and computer programs. Developing advanced systems like intelligent factory automation equipment software requires large amounts of data sources. For example, data from sensors, input from users, and other insights are gleaned from making the software work effectively. Without cached insights, it would be hard to gauge the efficacy of this system and to see where improvement is due.
At the same time, it could lead to equipment malfunction. However, with data grids, it gets easier to develop applications that use massive amounts of data. With plug-and-play data grids available as cloud-based SaaS systems, you can easily integrate this technology with your application. You do not need extensive experience in data management or related insights as long as you can work with API integrations.
Synchronizing data across nodes
Data synchronization is very important when dealing with various nodes to make sure there aren’t any inconsistencies. Unlike other data management systems that require manual synchronization through periodical updates, data grids do this automatically. Since all the nodes within the cluster are available in one central data location, the data is synchronized automatically.
All the nodes fetch data from one data store, mart, or warehouse, which facilitates an effective data sync system. When any new data is written or edited, the changes reflect instantaneously across all nodes. As a result, all the users have access to the latest information regardless of which node they are connected to. This automatic data syncing across nodes makes data grids suitable for enterprise-grade applications that need rapid access to the latest relevant insights.
Benefits of distributed data grids
There are various benefits of using distributed data grids, but some of the most common ones in most applications are the speed of data retrieval times. In high-speed enterprise applications and BI tools, every second counts, and by reducing latency, the time gap is bridged. At the same time, a data grid stores only relevant insights which contribute to distributing operational and current information. This blots out all the noise that could impact the accuracy of the data being disseminated.
The simplification of the current cloud-based SaaS products that use this technology allows for much rapid deployment. Therefore, you can get going with distributed data grids in no time with the right service provider. Overall, distributed data grids simplify application developer’s lives while also contributing to a seamless user experience down the line.
Applications of this technology
Distributed data grids have seemingly limitless applications in a wide range of industries. For example, this technology can work in the healthcare industry by making patient records universal in-hospital network chains. This would help doctors within that hospital network chain have access to the latest patient records. At the same time, this technology would help operational oversight to manage funds and the facilities effectively.
On the other hand, distributed data grids could also help in investment and trading industries since quick access to insights plays an important role. Expert advisors need streamlined relevant data, and this technology offers just that. There are various other usage scenarios you can imagine of this technology that meet the purposes of your organization. Fortunately, service providers can also customize these products to meet your goals. Check out more errors here.
Distributed data grids play a crucial role in bridging the gap between consumer satisfaction and realistic application development. Since this technology is very easy to integrate but yet effective in providing rapid access to data, it helps both parties. At the same time, it is easy to integrate, especially when it is being used as a plug-and-play service from SaaS providers.