In-memory Data Grid vs. Redis: For Better Data Governance

In today’s data-driven economy, businesses run the risk of losing out to the competition when they fall short of getting the best data processing solution. Depending on the type of business, companies’ computing requirements may vary, but there’s one thing that all businesses have in common—the need for fast and powerful data processing. Real-time data is the name of the game, and not playing cold means the difference between predictive business insight and tone-deaf solutions that can alienate your customers.

Technology presents business solutions today that have never been seen before, but progress comes at a cost, and that cost is increasing workloads and customer demand. The amount of data companies have to process gets larger by the day, the complexity of data remains the same, if not becoming more so. By addressing this complexity, companies can make data processing more efficient, faster, and more cost-effective. The most common solutions companies turn to are in-memory data grids like GigaSpaces or in-memory databases like Redis. Depending on the needs of the business, either one can be the ideal solution. There are advantages, however to choosing an in-memory data grid mainly because of its speed, reliability, and easy scalability. Before diving into the benefits and other features, it‘s best to first discuss the main differences of these two computing platforms.

Redis: The Cache of the Day

Redis is an open-source distributed cache and key-value store that supports data structures like sets, hashes, strings, lists, and streams. It’s an in-memory database that keeps a copy of application data in memory for storage or to offload reads from a backend system. Simplicity is one of its claims to fame, especially when it’s used in conjunction with the commercial version Redis Enterprise. The simplicity and low cost of Redis can be attributed to the fact that it’s written and implemented in C language, which is a common programming language for most software until today. It’s also single-threaded, but the utility of this decreases as the amount of data stored and processed increases. For most companies, however, a simple cache and handling a copy of the data is sufficient. If the main goal of the platform is having your own key-value store, Redis does the job.

As an in-memory database, Redis reduces the typical three-layer architecture of an in-memory data grid to two. While this simplifies the overall structure and minimizes moving parts to speed up data processing, the main problem is that Redis can be hard to implement in current systems and applications. Significant changes to the dataset must be done from existing databases to make this integration possible. Currently, Redis is used mainly for session caching, message queue applications, full-page cache, and counting.

There are a few things to consider when adopting Redis as a computing platform, and depending on your business needs, it can be a viable solution—especially if you’re looking for something open-source. One main consideration with Redis, and with an in-memory database in general, is that it’s a system of record. Implementing an in-memory database system means your business must also have a failsafe to protect data in case of unexpected downtimes. Redis is also designed for vertical scalability, which makes scaling the system expensive, especially if you choose just the open-source component. Vertical scaling, by nature, always has a breaking point, and that limits the possibilities for web and mobile applications that handle complex queries and simultaneous requests.

In-memory Data Grids: Scaling for the Future

The in-memory data grid is preferred by many companies due to its easy scalability and high-speed data processing. By using RAM, it eliminates the need for constant disk access, reducing the bottlenecks associated with constant data movement within the network and to and from disk storage. In-memory data grids are able to provide lightning speeds when it comes to data processing because it distributes data and workloads across a network of computers. This makes it a viable option for companies planning to accelerate existing services and applications because this distributed structure makes it easy to deploy. Despite this structure, in-memory data grids have a unified API that allows for the expansion of data and acceleration of analytics.

A notable feature of an in-memory data grid is the collocation of both the application and its data within the same memory space. This decreases latency and maximizes throughput, making the in-memory data grid a powerful and cost-effective solution for real-time data processing. Real-time data and the ability to process data as it streams empowers businesses to get real time insights that lead to sound business decisions. By addressing the complexity of data movement, data governance is made simple and more manageable. An in-memory data grid can also be deployed on hybrid environments that can include both cloud and on-premise systems.

Scaling is relatively easy with an in-memory data grid, and it’s arguably one of the biggest differentiators when choosing a computing platform. Computers within a network still retain their own data structures despite sharing memory and computing power. Data is also constantly synchronized across the network. Scaling an in-memory data grid can be as simple as adding new nodes to the computer cluster.

A Long-term Solution

Before choosing a computing platform, it’s important to know what you already have. The differences between an in-memory data grid and Redis may be technical, but whether the different features of different platforms will provide benefit will depend on the current business needs. Developing new applications will require the low-latency access to data that an in-memory data grid can provide. If what you need is the acceleration of online transaction processing (OLTP) and online analytical processing (OLAP), Redis could suffice. As the amount of data increases, however, businesses would be better off investing in an in-memory data grid.