In-memory Data Grids vs. NoSQL: What’s the Difference?

by Dan

In-memory computing has seen a rise in adoption in the past few years, and it continues to accelerate as mature solutions are created. Organizations are leveraging on the technology to scale their database processing operations at speeds never before imagined. In today’s world of big data, organizations rely on two major players to handle and interpret the large amounts of data gathered on a daily basis: in-memory data grids and NoSQL databases.

What are the main differences between these two solutions? We will go over their main differences by discussing pros and cons in practical use.

In-Memory Data Grids

In-memory data grids are data fabrics with a high throughput but low latency, placing the application and its data to be located in the same memory space. This allows for maximum scalability because it reduces the movement of data over the network and reduces the need to access high-latency, hard-disk-drive-based or solid-state-drive-based data storage. In-memory data grids are deployed on a cluster of server nodes sharing the cluster’s available memory and CPU. Scalability can be as simple as adding a new node to this cluster.

An in-memory data grid is highly distributed and, therefore, simple to deploy, cost-effective, and ideal for accelerating services and applications. One of its unique capabilities is a unified API for accessing data from external databases and data lakes, which allows for the expansion of data and acceleration of queries and analytics. Some in-memory data grids also provide support for data models directly ingested to the multimodel store data grid from real-time data sources into RAM to increase processing speed. Processing is much faster in RAM because it does away with delays caused by continuous disk reads and writes.

One caveat to shifting to in-memory data grids is the memory requirement; most in-memory data grids require a large enough memory to fit all data in the disk-based database. This is a major cost consideration since memory is still more expensive than disk-based storage. This issue can be resolved, however, by processing against the full data set despite some data residing on disk. This allows the amount of data to exceed the amount of memory during processing and allows the system to immediately process against the dataset on disk after a reboot.

NoSQL Databases

They say necessity is the mother of invention, and in the case of NoSQL, this is true. NoSQL is borne from the limitations found in conventional SQL databases, specifically, its rigid schema that makes SQL non-ideal for use with certain types of applications. NoSQL systems were developed for high operational speed and flexibility on the management of data. Companies rely on NoSQL for its efficiency in storage and management of data for large enterprise and ecommerce websites.

NoSQL allows data to be stored in an unstructured, schema-less fashion, which means that any type of data can be stored in any record within the database. Essentially, there are four main models for storing data in a NoSQL database.

Document Databases

In a document database, data is stored as free-form JSON “documents” where data inserted can be anything from integers to freeform text.

Graph Databases

In a graph database, data is represented as a graph of entities together with their relationships. Each node in this network or graph is an unstructured data cluster.

Key-Value Stores

Key-value stores allow the access of free-form values by way of keys; these values within the database can be anything from simple integers to complex JSON documents.

Wide Column Stores

In this model, columns of data are stored as opposed to conventional rows in an SQL system. Several different types of data columns can be aggregated as necessary for queries or data views.

The schema-less nature of NoSQL databases makes it ideal for scenarios where speed of data access is more important than transaction consistency or reliability. The disadvantage here is obvious, but organizations and enterprises have their own requirements. Not locking yourself into a schema also allows for easier modifications later on, as large amounts of data locked in a specific schema can be quite challenging to change.

What’s The Platform For Me?

Ultimately, the biggest consideration here is the fact that these two platforms aren’t mutually exclusive. If you’re looking for a long-term strategy for rolling out new applications and accelerating existing ones, it may be best to roll out a computing platform that combines the best of both worlds—the full relational database of NoSQL and the flexibility and scalability of in-memory data grids. In addition to this, companies and organizations should also consider the need for supporting in-memory technologies like a streaming analytics engine and a continuous-learning framework powered by deep learning. Supporting technologies will allow businesses to efficiently manage dataflow and event processing and provide them the capability to apply deep learning analysis to operational data in real time.

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