Potentials and Risks of Implementing In-Memory Databases in Business Applications

Nowadays, each of us interact with data in different aspects of life and this trend has been growing dramatically in the last century. With the rapid development of technology and its reflection on usage rates, the amount of produced data is growing in a way that becomes difficult to manage. It has become a matter of managing this increasing data and making sense of those that will be useful to organizations. Because most of the organizations depend on data for its very survival. Data is used in order to make strategic decisions about the future direction and this data must be current and accurate. At this point, data is becoming an asset that has many challenges for companies.

There are databases to handle all of these challenges. Databases are organized collections of information, or data, and they are used for storing, maintaining and accessing any sort of data. They collect information about people, things or places such as sales transactions, customer data, financials and products. This information is gathered in one place so that it can be observed and analyzed. It can be said for databases that they are an organized collection of information. In general, a database is controlled by a Database Management System(DBMS).

Since their inception in the early 1960s databases have evolved dramatically and this evolv spawned many different types of databases. Early databases were network and hierarchical ones. They were so simple but not flexible for business applications. Afterward, relational and object-oriented databases became popular. More recently, NoSQL and cloud databases became a response to the growth of the internet. The transformation of databases continues strongly and tries to keep up with the speed of the data.

These databases make sense with the presence of disks that store data on computers. They provide fast and direct access to data. When there is a data need, it’s being called into RAM, local system memory, in order to be processed by the Central Processing Unit(CPU). But at this point accessing data on disk creates a bottleneck in the data processing about the time it takes. Data transfer rate can be crucial for most business applications because real time analysis is required in some sectors such as trading and healthcare. In order to facilitate this process, there are several types of disks.

Hard Disk Drives(HDD) are used as memory in traditional databases. They have two main functions: storage for data and space for the computer’s operating system and applications. Storing huge amounts of data at low prices is the main advantage of HDDs. But the speed of reading and writing is slower than the RAM. As a faster version of HDDs, Solid State Drivers(SDD) is another type of disk. It stores the data on flash memory chips and maintains the data in a permanent state, even when the power is off. Although it’s better than HDDs, it’s more expensive and has less storage capacity for big data. The main problem of these disks is calling data from disk that causes latency problems for real time access. As a solution to these problems, in-memory databases transformed the way of data storage.

In-memory database systems(IMDB) are database management systems where all data is stored entirely in the main memory. Unlike other memory types, in-memory databases are so fast because it requires fewer CPU instructions and eliminates the time it takes for accessing data from the disk. This time uncover the concept of latency. Latency can be described as the lag between an action of a user and the response of an application to this action. It is extremely important for some business domains in order to get real-time data and analyze it. In-memory databases give the ability to access the data quickly in-memory.

There are many positive aspects of in-memory databases. As stated above, the speed of retrieving data from the database is so high and it makes it without the need of performing disk operations. Accordingly, it allows for real-time analysis and reporting of data. On the other side, different parts of databases can be managed by direct pointers with in-memory databases. They also simplify redundancy and availability that eliminates downtime and possible loss of revenue. Additionally, one of the most important differentiators of an in-memory database is immediate transactional consistency that allows applications to make accurate decisions involving finite, shared resources at massive scale. This is especially useful in 5G environments.

One of the main disadvantages of in-memory database systems is volatility. Data can be lost if there is loss of power or computer’s RAM crashes. This problem can be handled easier in traditional databases by restoring data from the disks. It is also important to note that the cost of memory is much higher than the cost of a hard disk. The cost of in-memory systems is 80 times higher than traditional disks. That’s why the usage of in-memory databases is limited to special sectors that require real-time and fast analysis. At last, in-memory systems have much less space then traditional disks, it’s common to run out of space. At these times, any new stored data will be lost and write operations will fail.

In-memory systems have use cases in many different sectors and business applications. From healthcare to retail, persistent memory facilitates real-time analytics to keep the business forward. To give some examples, the data coming from IoT sensors can be so large. In order to compute these data quicker, in-memory databases are really useful. Another wide application area is e-commerce. The speed of retrieving data on each page view can affect the revenue of the business directly. Additionally, detecting fraudulent transactions can be of vital importance for financial institutions. Real-time streaming analytics improve business health and uncover insights possible. The gaming industry also uses in-memory systems, because leaderboards change so fast and it requires reading big data when millions of users are accessing the game at the same time.

With this wide range of usage areas, in-memory databases have some risks in special situations. As mentioned above, power failures or shutdowns can cause data losses. For mission-critical applications, this can be unacceptable. On the other side, transferring data over a network will affect accessing data by additional latency of the transfer overhead. All these risks and advantages must be considered in detail for the sector that will be used.

In this article, I tried to talk about in-memory databases. In the continuation, I will try to publish more specialized articles. Keep motivated and followed!

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The resources I used are here:

In-Memory Database : https://www.heavy.ai/technical-glossary/in-memory-database Concise Guide to Databases : https://www.britannica.com/technology/database What is an in-memory database? : https://www.voltactivedata.com/blog/2021/06/whats-an-in-memory-database/ In-memory Databases : https://www.mongodb.com/databases/in-memory-database SSD : https://www.javatpoint.com/ssd In-memory databases and innovations in Business Intelligence : https://www.dbjournal.ro/archive/19/19_6.pdf Comparative Study of Different In-Memory (No/New) SQL Databases : https://www.sciencedirect.com/science/article/abs/pii/S0065245817300463