It is almost impossible to develop a software project without the use of DB, especially the scalable one. Instead of explaining obvious facts, let’s consider more complicated and obscure aspects. For instance, what developers should know before choosing the data management system for their project and how to choose the right one.
SQL are relational databases, which are primarily used to operate a huge amount of data, which is easy to structure. SQL stands for Structured Query Language and this type of DBs is based on specific Query Languages, which are the main tool of communication within the DB itself.
Relational Databases are mainly easy to work with, and their business logic is obvious. They are more preferable choice in most cases due to their scalability and clear nature. Moreover, they are designed specifically to operate an array of data. Moreover, in cases, when developers have to combine different types of databases like SQL and NoSQL(it is a surprisingly common thing), they are preferable as the main database. As a result, the relational database management system is treated as the main data storage, while No SQL DB is used for some specific purposes.
NoSQL databases are also divided into a few different groups. There are document-oriented, key-value, wide-column, and graph NoSQL database types. The name of this type appeared to avoid confusion between the new DB types and relational ones.
Clearly, NoSQL databases are proposing more variety of use cases and solutions, yet mainly they are used for specific tasks, or when it is difficult to structure data. Thus, we can assume that they are more agile and, partly, unique. Each of them has its own characteristic, which helps to differentiate them from others.
For instance, document-oriented DBs are treating data, which is stored in JSON file format. As a result, developers can simply create their own collections and better navigate the data, even if it is not structured.
Key-Value databases are able to code and decode the input data in their own manners. As a result, even poorly arranged data will be organized due to specific algorithms. In other words, the key-value database “attaches” specific keywords to each piece of input info. For example, if we add 3 different characters: Kevin, Bradley, and Cooper, most likely they will be displayed under different names like User 1, User 2, and User 3. So, when they are needed, the DB will automatically decode the message and find all related info.
The main feature of the wide-column database type is the fact, that it stores data in columns, instead of rows. This can highly improve numerous operational aspects like scalability, performance, and others.
Finally, graph databases are storing and operate data in their own distinctive way. Data, known as nodes, as well as the relations between them, are represented in a graph. Thus, it is much easier for developers to rapidly figure out the content of the project, and what are the connections between the nodes. Yet, it is not the only aspect, which makes developers’ lives easier. Additionally, graph databases better comprehend the sub-relations between the components. To make it simple, graph DBs are able to effortlessly define the complex relations between the nodes like “related by”, or “similar to”. Also, they can define indirect relations, when the connection between “Node A” and “Node B” lies in their similarity with the “Node C”. Such simplicity can be also easily noticed on the code level.
The comparison between SQL vs NoSQL databases is actually a very common thing. When choosing a database for your project, you will definitely face the need to select one of them. Previously, we have already explained it in detail and hope, that our summary will help you to make a more conscious choice.
Also, there are alternative types of databases, which are less popular and more limited number of proposals. Among them, we can name object-oriented and centralized DBs, hierarchical, distributed, network, and cloud databases. However, due to the limited offers and specific usage purposes they are worth mentioning, not a detailed explanation. The only exception possible is related to the cloud DB type, which is gaining popularity at the moment. The main and probably only distinction of this type, though, is that they are mainly proposed as a service and are based on Cloud computing.
Frankly speaking, the list of such aspects is countless. Each small and at first glance unimportant detail may cause numerous issues in the future. Nonetheless, it is possible to narrow down such a list only to the main and most crucial aspects, or even categories.
The first and most essential aspect is to check if the database you are considering is compatible with the tech stack you are going to use. You need to check both software and hardware in order to ensure the correct work of DB and avoid possible unneeded changes.
For instance, the database you want to choose may be based on a different programming language or won’t support other software, used for application development. As a result, you will need to implement changes within the project up to the global architecture replacement.
Yet, it is one of the most obvious aspects as well. There is no doubt that the first thing anyone should do when it comes to any creation or development - is to make sure that every element fits its place and can cooperate with others.
Also, it involves possibilities of various add-ons and integrations, as well as the overall potential of the database.
Also, it is very important to research the database scalability possibilities. Sooner or later, your project will grow. Therefore, it is highly recommended to plan an approximate future scale of your app and to take these calculations into account while picking a database.
Thus, the more scalable your database can be - the better. Yet, sometimes, there is no need for large-scale DBs. For instance, when we are talking about single-feature and simple apps, which have preset data and won’t accept new inputs.
Last but not least, among the most crucial aspects is how secure the database is. In other words, does it have any built-in security measures, and how effective they are?
There is no doubt, that your development team will definitely implement some general and custom security precautions. Nevertheless, they are not always related to data storage. In fact, according to various specialists, databases are among the most vulnerable parts of the app. For example, Data Disclosure is included in the TOP 10 OWASP vulnerabilities list.
Obviously, built-in levels of database security probably won’t save your application from well-organized and professional attacks. However, it will definitely help you to avoid unneeded risks and small raids.
It is one of the most widespread and famous SQL databases. This database can be considered a veteran due to the fact, that it exists since 1986 when was presented the first Postgres project.
Thanks to its long existence, combined with multiple features and useful solutions, PostgreSQL managed to gain a massive audience, which is still active and shares its experience on various thematic forums. Moreover, some users developed their own extensions and add-ons for the database. Yet, the diversity of additional content is not limited to amateur-only. As a matter of fact, the creators of the PostgreSQL database are constantly improving and extending the functionality of their product. For instance, Postgres conforms to 170 out of 179 mandatory features, which is among the best results. At the moment, there is no single DB, which conforms to all of these features.
It is designed and distributed as an open-source model. The creators gave all the instruments, including the source code, to the public. This highly increased the possibilities of various enthusiasts, which was the right decision. Also, among the main and famous features is the fact, that PostgreSQL is 100% free of charge. Thus, you will never have to pay for it.
The high number of modifications helped to transform Postgres into a utilitarian database, which can do almost everything. For instance, it can be used on multiple OS, can manage both structured and unstructured data, and has the ability to behave as some NoSQL database like Key-value, or Document-oriented. As a matter of fact, the list of advantages and features of Postgres is countless. A small insight: Most of our developers highly value Postgres for its features and extra benefits from using it. In fact, when we were writing this article, we asked our team. Answering our questions, most developers undoubtedly said that PostgreSQL is the first thing that comes to mind when they hear anything about databases.
Talking about the drawbacks, though, the opinions may vary. For example, some users complain that UI elements are not perfect and there is a lot to improve in this field. Among the more objective disadvantages, we may say, is that it is noticeably slower in comparison with more modern competitors. Obviously, such weakness can cause result in unpleasant experiences, especially when we are working with a large array of data.
As we mentioned before, the possible use cases of Postgres are countless due to its flexibility and numerous built-in features. Yet, we can assume that there is a preferable list of industries. PostgreSQL is a great solution for Finance, suited for working with Scientific data, Manufacturing, or Government GIS data.
Neo4j is one of the top graph databases. The increasing popularity of graph databases can be easily explained by their main feature. As a matter of fact, most of these DBs are showing the stored data in graphs. As a result, it becomes much more comfortable to navigate and monitor the data. Moreover, graph databases can simply track the relations between nodes(data type), no matter how complicated they are: both straight node-to-node relations, or complicated relations between nodes by specifics(similar to or by, or other properties).
The main feature of Neo4j, as well as its graph competitors, is its simplicity. The query language, Neo4j is based on, is very easy to learn and understand. Actually, it is not the only user-friendly part of the database. It seems that UI/UX principles are the basic development approach, used by the developers of graph DBs. This type of database is very easy to apprehend, very scalable, flexible, user-friendly, and has great performance results. It is also secure enough and has numerous extension proposals. On the flip side, Neo4j uses a master-slave architecture design. As a result, it can cause bottlenecks due to the specifics of this architectural pattern. Also, most graph DBs, including Neo4j, struggle with horizontal scaling. Eventually, users have to be very careful with the settings and configuration of the database. Otherwise, it can result in memory overusing. Yet, it can be easily fixed using the support of other users or detailed documentation.
As with any other database, Neo4j can be used for myriad purposes. Yet, the most popular cases are fraud detection, using it as a real-time recommendation engine, for master data management, to empower network and IT operations, and for Identity and Acess management.
Oracle is another example of an industry veteran, which exists for more than 40 years. Nonetheless, it is hard to compare modern versions with the early builds. Such a long existence term clearly resulted in various changes and innovational technologies implementations during the development.
As for now, Oracle is among the most popular databases. Originally, it was developed as relational DB but nowadays it is known as a multi-model database management system. In fact, due to constant development and improvements, Oracle has grown from a database to a full-stack complex combination of databases, additional tools, and extensions. It is still used as data storage, yet has the possibility to change main tools in order to perform specific tasks. On the contrary, constant development and superstructures caused redundant complexity. Thus, Oracle requires experienced developers. Despite the available documentary, newcomers will most likely struggle with various issues and encounter difficulties. In other words, if you are looking for a fast and easy solution for your project - Oracle is probably not your choice. Additionally, OracleDB may be difficult to manage.
Actually, it is hard to define the specific industries it is suited for due to the fact that Oracle is flexible enough to be adjusted for any sphere. Therefore, the only possible characteristic is that Oracle may be the ultimate tool for developing a very complex and scalable project, that has to operate large arrays of data, and is developed by experienced specialists.
MySQL is probably the best relational database management system for beginners. It is very easy to learn its basics and to perform all needed operations simply by using only them. MySQL is also able to represent data both in rows and columns.
Frankly speaking, MySQL seems to be the simplest database with very limited requirements, yet proposes numerous general services. The great advantage of MySQL over its competitors is that its ease to use is combined with great results in various fields like security, functionality, etc. For instance, despite the default size limitation of 4GB, developers can manually change it up to 8TB, which is a very surprising result for such a database.
Moreover, it is free of charge and works on numerous OS. Even if compared with rivals, MySQL is quite performative, reliable, and fast. Talking about the drawbacks, we can list a few of them. First of all, even previously mentioned scalability is too limited. In other words, MySQL won’t be able to manage a large array of data, not talking about Big Data. Also, this database is still limited in instruments, whether they are developing or debugging. Clearly, bigger competitors like PostgreSQL provide their users with numerous additional tools.
As we mentioned before, it has limited scalability options and hardly deals with massive data. Consequently, it is a bad idea to use it for projects, which require operating Big Data. Alternatively, MySQL is among the ideal tools for small and uncomplicated projects. Moreover, it is a great solution for newcomers and amateurs.
MongoDB is a NoSQL document-oriented database. It is beloved for its high-performance indicators and great ease of use. As document DB, Mongo supports storing data in document formats. Yet, among its main features, there is a possibility to work both with unstructured and structured data. In fact, MongoDB does not limit its users and allows them to store almost any data format they need.
This database is also simple enough, so even inexperienced developers will be able to comprehend it after examining the detailed documentation. Moreover, even if the team will struggle with some issues and won’t be able to deal with problems on their own, developers can always rely on specified support from the creators. So, we can assume that there are no boundaries even for newcomers.
Among other useful features, it is possible to highlight ad-hoc queries, which highly optimizes the process of obtaining data, providing it on demand. For instance, most other databases operate routine queries, which are not as fast and provide the data after other processes were completed.
Nevertheless, there are a few crucial drawbacks too. For example, Mongo relies on indexes. To make it simple, even a small mistake may cause performance issues and noticeably slow down the database. Also, data duplication is very laborious. Unlike most competitors, MongoDB can’t simply duplicate data, because in this case, the relations will be damaged. As a result, if developers need such a process to be done, they will have to do it manually, spending time on additional coding and detailing.
Finally, this database has very high indexes of data usage. Additionally, it has data size and nesting limitations as well. Thus, a single document can’t weigh more than 16MB, and document nesting is limited up to 100 levels.
Talking about its possible usage purposes, we can refer to its creators’ recommendations. It can be used to create catalogs on eCommerce websites, in the GameDev industry, for various analytical applications, etc. Yet, probably the most favorable usage scenario - is as a tool for the Internet of Things, or its analysis.
According to various feedbacks, Apollo is not simply a graph database but has already grown into a whole ecosyste that consists of multiple solutions either tools or libraries. For instance, apart from detailed documentation, which has already become a must-have service, at least in the field of databases, its creators also provide their own libraries. Additionally, Apollo support third-party extensions as well. For example, It is compatible with Node.js, Express.js, and related libraries.
Despite the fact, that it is one of the youngest DBs on the list, Apollo is also one of the most progressive ones. For instance, it is highly integrational, with the possibility to be integrated into numerous services like Microsoft Azure, AWS Lambda, Express, Restify, and many others. However, it does not affect its own capabilities. In other words, Apollo is able to be either integrated or used as a basis for the new project.
The declarative nature of data queries is one of the most recognizable Apollo features. Yet, it is not the most important one. Probably its most essential distinction is GraphQL and its usage possibilities. In fact, Apollo enables the automation of various processes with it.
If we try to talk about the advantages of this database in a more general way, we can state that it is very easy to work with. Also, Apollo shows great performance and compatibility results. But still, Apollo’s disadvantages are as vital as its features. The first thing that comes to mind is that it is still actively developing. So, while most competitors are hunting bugs and deciding what bonuses to add, the Apollo development team is constantly bundling and changing the overall structure. This results in rapid and unobvious changes, which may surprise inexperienced developers. Therefore, despite its simplicity, it is highly advised only for professional teams, who will be able to find different solutions with the use of their background knowledge.
The size of the database can be also counted as a drawback. Clearly, when you are trying to create a whole ecosystem instead of a single product, you will have to sacrifice its size limitations. Ultimately, there are no direct competitors, which can both be a strength and a weakness. It is still unclear whether it is a technology of the future, or simply an advanced but limited product, suited for specific purposes only. Someday, the answer to the question will become obvious but it isn’t for now.
The New York Times, Medium, Nintendo, Express, and Major League Soccer - all of them are somehow related or fully based on Apollo. Due to the fact, that this database is relatively young, it is hard to predict or describe the use cases in more detail or characterize their specifics. Nevertheless, as we can see, some major players in different industries decided to try it out.
Obviously, this list of databases is far not final. Yet, it is not supposed to be such. The main purpose was to help with choosing the possible data storage solutions for your future project. Thus, we briefly explained some basic aspects to consider and enlisted the approximate list of databases, which are the best examples and meet at least half of the stated requirements.
But still, we highly encourage you not to consider our word for the final verdict and try to find alternatives. In fact, if you check our case studies, you will find out that even we sometimes choose various DBs, which are not mentioned above.
Remember, that the context matters the most.
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