Choosing the correct database is not an easy decision to make and yet it has long-term consequences for your business. To put it in perspective, it is not enough to choose a database that suits you now. It is necessary to keep in mind the idea of what your business may become in several years, as you don’t want to rethink your whole database strategy just because your company has outgrown the database you are using. It doesn’t really matter whether your business is big or not because one way or another you will need to store all your data somewhere. Fortunately, the database market offers many options to choose from. In this article we will tell you about database choices for large data volumes, different kinds of databases and where it is better to use them.
How to Define Your Data Strategy
It’s important to determine a data strategy that matches your business before considering operational databases. Understanding the type of data that should be recorded is equally important for defining a data strategy and for evaluating operating databases. Here are some tips to help you determine your requirements:
Structured Data Compared to Unstructured Data
Unstructured data is not organized in a certain way and is not followed by any previously defined data model. Therefore, unstructured data is quite hard to run queries on; however, if you know precisely what to look for, it will not be a problem. Examples of unstructured data include publications in social networks, metadata, etc.
Volume of Data in Your Database
When evaluating databases, you need to understand the size of the database your business needs now and the size it will need in the future. Queries run slower when data volumes grow. This is due to the fact that the estimate of the database volume affects performance and speed. Nevertheless, each database gives storage of different volumes, which allows you to select and customize it to your needs.
If your business requires a permanent database to work in real time, it is better to choose a database that is optimized for analysis.
Relational Compared to Non-Relational Databases
Now that we have talked about some of the core requirements for a data strategy, let’s look at another type of database:
Relational Databases (or SQL) were created in the distant 70s of the last century. As was said previously, this database type was created to store structured data. SQLs usually represent real-world objects, such as information about a person or things that a person bought, grouped into tables, the format of which was previously designed. Here are two reasons for choosing a relational database:
- The need for the database to meet the requirements of ACID. This reduces the probability of unexpected system behavior and ensures the integrity of the database. It is different from the approach used in NoSQL, which focuses only on flexibility and speed.
- Data that you work with is structured, and the structure is not subject to frequent changes. If your organization is not in the stage of exponential growth, there are probably no compelling reasons to use a database that allows you to fairly freely handle data types and is aimed at processing huge amounts of information.
The most well known SQLs are PostgreSQL and MySQL.
Non-relational databases (or NoSQL) have also gainied in popularity in recent years. Basically, NoSQLs are popular for companies that are developing so fast that they are unable to stop and work with data schemes. Scaling and the possibility to work with data “right here and right now” are required conditions for the existence of such companies, which is why they choose NoSQL. So, what are the benefits of NoSQL?
- Storage of large volumes of unstructured information. The NoSQL database does not impose restrictions on the types of stored data. Moreover, if necessary, you can add new data types during the process.
- Using cloud storage. Cloud storage is an excellent solution, but it requires the data to be easily shared between multiple servers in order to provide scaling. The NoSQL databases were specially created for using, testing and developing local hardware, and then moving the system to the cloud, where it works.
- Fast development. If you are planning to develop a system with agile methods, then using a relational database can slow down your work. Non-relational databases do not need the same amount of preparatory actions that are usually needed for SQL databases.
NoSQL databases are able to include many types of data without losing the ability to scale and allow users to make changes in the process. The most well known NoSQLs are MongoDB and Redis.
Choose the right database
Here we also want to highlight the most popular databases and tell you a bit more about their advantages and disadvantages in order to help you choose the right database for your enterprise.
It is easy to install and it works fine without special settings. With the proper approach MySQL can flexibly adjust to your needs. But there are also some pitfalls: in some cases it may slow down your project, no matter how well you have tuned the DBMS and the data structure.
MySQL is for you if:
- you do not want to delve into DBMS settings;
- you think structurally;
- integration with MySQL is in any programming language, framework, CMS, CMF and so on;
- you need DBMS to manage small structural data (up to 1 or 2 gigabytes).
Negative points? There are some, and you should choose another DBMS if:
- the performance is really low, regardless of the settings;
- changing the data structure can be quite a labor-intensive process, especially with a huge number of relationships between data in different tables and even with the simple addition of fields;
- there is sensitivity to server instability, especially when using XtraDB from Percona. If MySQL is not completed correctly, you can break tables and databases so much that you can only restore it from a full backup. There are tools that in simple situations will help to restore working capacity, but they do not always help.
It is similar to MySQL, but you have to be able to customize it properly. It is a very stable database, in contrast to MySQL. It is also considered to be the best database engine for large data. And this can be a deciding factor for you when choosing.
PostgreSQL is for you if:
- you need a reliable store;
- you can configure and use PostgreSQL;
- you need well structured data, but with some flexibility in the data schema (JSON / BJSON);
- with the help of third-party libraries it is simple and convenient to expand into clusters and do table shading.
There are also disadvantages, but there are not many of them:
- the need to work with this DBMS to adjust it well – otherwise, it’s better to use MySQL;
- the default authorization system can cause difficulties when using or configuring.
Easy to install, working fine without special settings. And if you go deeper, and learn, then you can adjust a lot. MongoDB is also considered to be the best database for large amounts of text and the best database for large data.
MongoDB is for you if:
- you do not have a clear, pre-defined data structure, or you assume that the data composition can be changed a lot;
- you are planning a fairly serious amount of data (tens or even hundreds of GB).
There are some disadvantages too:
- there are no simple transactions, at least in the classic form, as in MySQL / PostgreSQL – when you add a lot of data that depends on each other, there may be certain difficulties that you will have to solve on your own;
- the connectivity of data is practically non-existent.
Most often this DBMS is used as a caching layer to work with data from another, slower DBMS. It is rarely done, but it can still be used as a database for the data. At the same time Redis knows different types of data, including lists, queues. It is very fast, and it can store data on a disk with support for additional recording.
Redis is for you if:
- the data volume is small and very simple;
- there is simple implementation of master-slave replication.
There are some disadvantages too:
- the amount of data should not exceed the amount of free RAM on your server;
there is a fairly weak data integrity;
- transactions and related data do not work well – more precisely, there is Pipeline and Multi / Exec, but it’s still not quite a transaction in the classical sense.
Hopefully, you now have a better understanding of which database is most suitable for your business project. In today’s world, those who continually move forward receive the greatest rewards. So do not delay, implement your ideas!
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