Caching Distributed Data for Easier Storage, Manipulation, and Access of Big Data

Big Data

Having a large data can be difficult task especially in storing, managing, and manipulating it and it gets confusion on the part of the cache / Photo by: Homestage via Pixabay


Dealing with high amounts of datasets is becoming burdensome as it gets difficult to effectively manipulate, store, and access information in large quantities. Along with it is the struggle to determine if a cache is necessary.

Distributed data caching can help in improving the scale for larger big data applications, as well as retrieving previous data states in case of system failure. These problems normally occur in local or in-process caching, in which a lone instance is operating alongside an application.

According to tech site Inside Big Data, distributed caching means data is dispersed across a network of nodes so that it will not depend on a single one in upholding its state. This dispersal provides redundancy if ever the hardware fails or power is cut off. Such an approach also helps in avoiding the need to appropriate local memory for information storage.

"Given that the cache now relies on a network of offsite nodes, though, it accrues technical costs where latency is concerned," the tech site says.

It adds that distributed caching is better than in-process caching in terms of scalability, and that the approach is usually employed by enterprise-level products. However, this application often comes with many licensing fees and other costs that typically blocks true scalability. There are also trade-offs needed to be made, which makes it difficult to implement solutions that are rich in feature and high-performing at the same time.

At this stage, Inside Big Data says it's important to note that in the case of big data tasks, vertical scaling (upgrading power-processing functions of machines that house a massive database) is less effective than horizontal scaling wherein the same database is divided and dispersed across instances, given the fact that parallelization and fast access to data are needed.

It seems reasonable that distributed caching is a better choice in serving the needs of customers that seek both security and redundancy. While latency is an issue, protocols like sharding and swarming could considerably diminish it for well-connected nodes.

"Above all, we need to be able to deliver flexible middleware solutions that allow commercial entities to connect their databases to always-online networks of nodes," the tech site explains, adding that this delivery would ease their burden and enable these entities to better serve data to end-users.