Data science is a huge field with a long reach in different industries but for most fields and applications, in-memory databases are where all bow down. Using this database model has become standard in many modern enterprise applications and mostly user-end web and native apps.
Despite the widespread use of in-memory databases, it is not popular to many individuals that are not part of the data science field. There is a lot that can be said about this data management model but here is a brief explanation of what is an in-memory database, its functionalities, and its benefits:
In-memory database overview
Quite briefly, in-memory databases were designed with the core purpose of powering applications that need quick access to data to maintain their reliability. Other tenets of this database model include the capability of conducting in memory computing which is a different ball game. Overall, this kind of data model can be summarized as a system that caches insights that stream in and get stored.For data to be cached in memory, it needs to be queried often or it should be anticipated for high volumes of queries. To act proactively, data is stored on a cache and then when applications call the information, it is readily available. There is a lot more to in-memory databases but this is the standard use of this data model. Other bells and whistles can be added for an improved experience whenever needed.
Using in-memory databases for complex computing
Fortunately, in-memory databases do not use the same restrictive architecture that archaic data management systems used before. For example, they can be used in a more complex system called an in-memory data grid which helps compute much faster. Using an in-memory database in this elaborate system allows for complex computing that can relieve some processing power on the machine being used.Since an in-memory data grid uses nodes used to cache the data to compute and process insights, the device being used does not face performance strain. Subsequently, the application works much more reliably and faces reduced downtime caused by system failures and crashes. At the same time, it does not impact the speed of that machine being used since it ‘borrowed’ the computing power from those nodes. There are different ways this is done, including computation colocation and MPP processing systems.
In-memory database applications
What are the most common uses of in-memory databases? The core purpose of this system makes it a great fit for applications that require rapid access, storage, and manipulation of the data. Such systems include Business Intelligence tools for tactical decision-making and real-life analytics. Alternatively, user-end applications in sectors such as investment and banking need the fast-paced architecture of in-memory databases.It is crucial for these apps to in-memory data caching for most queried insights to avoid slowing down the app in its entirety. At the same time, in-memory databases do not compromise on security because if the app gets shut down, the cache gets overwritten. A new cache starts again when a new session begins on the app, which shows the alertness of securing customer data that might have been cached.
What are the benefits of using an in-memory database?
The most common benefits of using an in-memory database are undoubtedly the reduced latency and overall reliability of applications. These benefits have a ripple effect on the end-users, which includes improved customer satisfaction. By extension, that event extends to revenue growth and improved company trustworthiness amongst the targeted audience.Other benefits include the flexibility of in-memory databases instead of relational or distributed data models. Although these database systems are quite effective, in-memory processes simply trump them due to integration capabilities (Disclaimer: Considering each application needs takes precedence in this matter). However, in general, in-memory databases have a larger latitude and longitude of being retrofitted in other data architectures. More benefits extend to cost savings of using in-memory databases instead of using Content Delivery Networks and other systems that might cost more in the long run.
Implementation at a large scale
When implemented at a larger scale, the benefits and practical usefulness of in-memory databases are defined to a greater extent. For example, think about the stagnant enterprise systems that were around less than a decade ago. Back then, a lot of applications used standard databases and they queried data directly from them.Perhaps the reason behind this was that the cost of RAM was quite high and it did not make sense for consumers to own devices with larger randomly accessible memory. With the prices steadily lowering, it has been possible to implement in-memory databases in real-life projects at a full scale. Nowadays, mobile devices have impressive RAM that even manages to cache different bits of data across multiple applications running in multitasking mode. That is why implementing in-memory databases at a large scale is finally happening.
Using in-memory databases in tiered data architecture
As briefly mentioned above, integration with in-memory databases comes relatively easy and this includes using this model in tiered data architecture. Such tiered data architecture could be data access layers that include an operational data store and other aspects. There is no one uniform method of implementing in-memory databases in tiered data architecture but it’s more of research and analysis of your needs. However, standard tiered data architecture might include an operational data store to gather insights from disparate systems.The data could then be warehoused and then replicated to an in-memory database for rapid access to end-user applications. You can even integrate an in-memory database into a fully-fledged Data Integration Hub to improve the performance of the applications it supports.
