A Comprehensive Comparison of In Memory Databases
In-memory computing is by far one of the most effective data management solutions for large enterprise-grade deployments. The standard of in-memory databases has significantly increased over the years. As a result of those developments, various in-memory database and computing solutions have ensued.
These different in-memory databases and computing solutions have varying benefits you should be aware of before planning and executing a data management system entailing any one of them. Here are the major differentiating factors by comparison and the best applications for the different types of in-memory database systems.
High-availability in-memory databases
In-memory computing has been integrated with high-availability procedures to provide databases that have the least downtime possible. Faults do happen and data is inaccessible at times due to technical issues but high-availability in-memory databases have been designed to minimize this as much as possible. With failover protocols and technologies in place, data is easily persisted into other functional nodes for access regardless of hardware failure.
This data persistence also helps when power surges or mechanical failures affect a data storage location. Therefore, this type of database replicates data for all-around access to the information regardless of most problems that might arise. That makes this type of in-memory database suitable for critical applications that require reliable data sources with the least downtime.
Distributed Data Store
Distributed data stores use a similar tactic to high-availability in-memory databases by using various nodes to persist data. In this case, though, the data is not persisted with the sole intention of making it available even when problems occur. Instead, it is aimed at boosting the loading speed of applications powered by them.
It uses in memory data grid technology by using various nodes to persist data and make it available without overburdening the central storage unit. Distributed Data Stores are best used for enterprise applications with a large number of users relying on the same kind of insights. This type of in-memory computing can be used in a variety of use cases but mostly mobile applications such as eLearning platforms and so forth.
Digital Integration Hub
A Digital Integration Hub has the core purpose of decoupling applications from directly accessing data from the data source. To bring this into context, enterprise applications do not have to use more than one API to directly fetch data from databases. The reason being that a Digital Integration Hub eliminates the direct relationship between applications and databases by being the middleman.
This technology is focused more on aggregating insights into one easily accessible data fabric for easier application access. Using a Digital Integration Hub is most suitable when there are multiple enterprise applications, with each one using more than one data storage location. With the data aggregated into one data location, you do not have to use as many APIs to access the different databases.
Operational Data Store
Operational Data Stores are slightly different from all of these in-memory databases since this system focuses on current insights. When an Operational Data Store has been integrated with an application, it uses predictive modeling to bring up data that will be in use. As a result, when the user needs the data is already on-hand and can be made available to the front-end application.
Therefore, this in-memory data grid’s differentiation factor is availing real-time insights with significantly reduced latency. Although it does aggregate data from disparate sources and even facilitates hybrid data storage systems, its main aim is to make big data lean and easy to access.
Which type of in-memory database is best for analytics?
The best type of in-memory database for analytics is highly dependent on the type of insights you are looking into. Choosing an in-memory database solution highly depends on the data analytics tools you will be using as well to draw the necessary insights.
There is no one standard suitable in-memory database so you should do the comparisons yourself. To help you with this, amongst the in-memory databases mentioned above, Digital Integration Hubs, Distributed Data Stores are more suitable for historical insights. Therefore, these are best for drafting long-term reports, whereas Operational Data Stores are suitable for real-time tactical decision-making insights.
Choosing the best in-memory database for eCommerce
E-Commerce platforms, especially ones that have built a global presence or have high volumes of traffic, might be processing a lot of transactions in a short timeframe. Each transaction requires multiple key pieces of information, which might even be stored in different databases. Therefore, in this instance, implementing in-memory computing solutions like Operational Data Stores can help boost the performance of the API call.
ODS systems boost the performance of API calls by reducing the latency while providing the exact data required at that time. In a broader scope, this would support positive User Experience efforts. If the latency between the API calling and app functionality is minimized, eCommerce platforms can allow customers to checkout as quickly as possible.
What to consider when implementing these solutions
When implementing a data management solution powered by in-memory databases and computing, there are various factors to consider. Most of these factors gravitate around the application of your data management solution. Some in-memory databases and computing systems are predominantly aligned with data analytics, whereas others focus on transactional processing.
Consider the type of application you need in-memory computing for and then start comparing features from functional in-memory data solutions. Also, consider planning your data access layer architecture since this can shed more light on the perfect in-memory data management system to implement. For example, if you have more disparate data sources, a DIH might be better suited to aggregate the data and avail it to integrated applications.
The bottom line
In-memory databases have a variety of use cases depending on their architecture and overall purpose. You need to intently look into the features of each one to ascertain the one needed for your project. Remember to consider the general architecture of the in-memory database before implementing it in your current project. Compare whether your objectives align with that architecture first.
