Maximize Your Project's Success With the Right Database Selection

The database management system is chosen depending on the tasks of the business. For example, startups need a quick start with minimal investment. On the other hand, banks need to store data correctly; otherwise, customers may lose their money. Today, I want to talk about how to choose a DBMS for the project, taking into account the company's needs.

Databases for a Newly Created Company

Problem: The business model of many newly launched businesses may change after entering the market. For example, Facebook was initially created as a social networking site for college students. Still, it has since expanded to become a platform for people of all ages to connect with friends, family, and businesses.

Even if the business model remains the same, the company may have new challenges and needs. Therefore, at the start, it is risky to choose a database management system designed to solve narrow problems. The chosen DBMS may not be suitable for new business processes, and transferring data from one system to another can be difficult and expensive.

Solution: For a small startup, it is better to choose one of the relational databases, for example, PostgreSQL or MySQL. They are suitable for a wide range of tasks. For fast-growing companies, it makes sense to use two or three DBMSs at once —  for example, MySQL or PostgreSQL for long-term data storage and Redis for fast query processing.

Databases for Testing New Products and Updates

Problem: It is crucial for startups to quickly test hypotheses and new services in order to understand which products or services are in demand among customers and, at the same time to minimize costs. Large companies have their own IT infrastructure and a team of specialists to service it. They also need to test new products and service updates while maintaining the stable operation of existing solutions.

Deploying or expanding your IT infrastructure is an expensive and lengthy process as you need to buy and configure expensive equipment, maintain it during downtime and take care of backups.

Solution: At the start, it does not always make sense to purchase equipment and build your own IT infrastructure. You can rent a cloud database, which you can set up in two clicks, and pay only for the used computing power. You will save on the purchase and maintenance of equipment.

Databases for Storing Data

Problem: For some companies, the data must remain unchanged and each time reproduced in its original form. Again, for banks, it is critical to conduct all transactions correctly. Mistakes in inventory storage can disrupt retail operations.

Solution: Relational databases such as MySQL or PostgreSQL are suitable for such a business. They allow you to store structured information, which is always grouped based on the same criteria and does not change. For example, in a bank, it can be the information about the sender, the recipient, and the money transfer amount.

In MySQL and PostgreSQL, each row or column stores only one data type. Therefore, these DBMSs are stable; the occurrence of errors in the data is almost impossible. Due to their design features and high reliability, the safety and immutability of information are ensured.

Databases for Projects With Unpredictable Workloads 

Problem: In some areas, the load on IT infrastructure depends on the season. The most vivid example is Black Friday. During this period, sales in many online stores increase by 500%.

For such peak periods, additional processing power is required. Of course, you can scale the DBMS, that is, add resources to it —  new servers, but they are expensive and will stay idle for the rest of the year.

Solution: For projects with unpredictable workloads, scaling cloud databases is easier and cheaper. In the event of a sharp increase in traffic, the company receives additional resources from the cloud DBMS automatically or upon request. In this case, only actually spent resources are paid.

Databases for Analytics and Big Data

Problem: Companies analyze data to optimize business processes and predict the popularity of their products or services. For example:

• Transportation companies evaluate the movement of goods in different directions to develop new routes.
• Retailers analyze the demand in order to plan the assortment and merchandising strategy.
• In health, disease data is used to predict epidemics.

If there is plenty of information to analyze, then such data is called big. For the machine algorithm to draw reliable predictions, it is necessary to extract data quickly based on specified criteria. Standard databases like MySQL and PostgreSQL will not cope effectively with this task. It takes them a long time to process large amounts of information.

Solution: Here, you need a special analytical database, for example, BigQuery or IBM Db2. They quickly perform analytical queries and can process more than a billion lines in one second. This means that the company will be able to quickly receive and use the necessary information.

Databases for Fast Query Processing

Problem: Some services must quickly process user requests in real-time. This is important if you are developing a social network, dating application, streaming platform, or any product in which the user creates a personal profile. Such projects also have to deal with numerous transactions, real-time analytics, counters, authentication, and other operations involving a quick database response.

Solution: Redis is a distributed, in-memory key-value database that may offer high performance. Redis stores data in memory; that is why it responds to queries ten times faster than MySQL or PostgreSQL.

It can be used on its own or as an additional database in cases where the number of requests is growing sharply, and you need to process them quickly.

Databases for Special Tasks

Problem: Often, a business needs to solve narrow tasks. For example, to encrypt passwords, make ratings, analyze transport routes or monitor the movements of couriers in real-time.

Solution: You need a database that allows you to connect extensions or use special commands to solve non-standard tasks.

For PostgreSQL and Postgres Pro, many additional extensions have been developed. For example, cryptography extensions will encrypt personal data for secure transmission over the network. In the event of theft, attackers receive only anonymized information. The PostGIS extension is suitable for map services, for example, for searching through geographic data.

Redis has a set of special commands that allow you to use the database to solve narrow tasks in real-time. For example, ranking players in games, content analytics, geographic data management, etc.

Checklist

1. A startup may change its business model. Therefore, it is better to choose a DBMS suitable for solving a wide range of problems or use two or three databases at once.
2. Large companies and startups can test updates or new products in the cloud database without buying expensive equipment.
3. In a business where the load on the infrastructure depends on the season, it is essential to scale the database quickly —  this is convenient to do in the cloud.
4. Relational databases, for example, MySQL or PostgreSQL, allow storing information unchanged.
5. For working with big data, BigQuery or IBM Db2 can work perfectly well. They allow you to perform analytical queries quickly.
6. Redis, a high-performance DBMS, allows you to respond to user requests quickly; it stores data in memory.
7. You may use extensions for solving narrow tasks.

Final Thoughts

It is important to choose a database that is easy for your team to work with. Consider the learning curve for your developers and the availability of documentation and support. Ensure that your chosen database is compatible with the programming language and frameworks you plan to use.