What is the database management system that compares data with a set of rules or values to find out if the data is correct?

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So you are building a software application. One of the first problems to be solved is how to store your data. Which database will you choose?

A Database Management System or DBMS is a type of software that communicates with the database itself, applications, and user interfaces to obtain data and parse it. The DBMS also contains the key instruments to govern the database.

For our comparison, we’ve picked the 10 most commonly used database management systems: MySQL, MariaDB, Oracle, PostgreSQL, MSSQL, MongoDB, Redis, Cassandra, Elasticsearch, and Firebase. Concentrating on their business-related benefits and the challenges, we’ll also outline the best use cases for each.

Since databases are just a part of the whole data management strategy, learn about this comprehensive approach in our dedicated article.

Types of databases: Relational vs non-relational

Basically, there are two types of DBMSs: relational and non-relational, also referred to as SQL and NoSQL respectively. Before discussing the most popular database options, let’s take a closer look at how relational and non-relational database systems differ considering commonly used data structures, performance, scalability, and security.

Relational or SQL databases

A relational database is a type of data store organizing data into tables that are related to one another, which explains the name. Structured Query Language is the core of these systems as it is used to communicate with and manage these databases, having given birth to their second name — SQL databases. RDBMSs have a predefined schema, meaning data resides in rows (records) and columns (attributes) with a strict structure. Here, each record usually holds a value for each attribute, resulting in clear dependencies between different data points.

Scalability. Relational databases usually scale vertically, meaning data lives on a single server, and scaling is done by adding more computer (CPU, GPU, and RAM) power to that one server. However, switching from small to bigger machines often involves downtime. Scaling an SQL database between multiple servers (horizontal scaling) can be a challenge as it requires data structure changes and additional engineering efforts.

Performance. Relational databases show great performance with intensive read/write operations on small to medium datasets. They also offer improved speed of data retrieval by adding indexes to data fields to query and join tables. However, when the amount of data and user requests grows, the performance may suffer.

Security. Due to the integrated structure and data storage system, SQL databases don’t require much engineering effort to make them well-protected. They are a good choice for building and supporting complex software solutions, where any interaction has a range of consequences. One of the SQL fundamentals is ACID compliance (Atomicity, Consistency, Isolation, Durability). The ACID-compliance is a preferred option if you build, for instance, eCommerce or financial applications, where database integrity is critical.

Non-relational or NoSQL databases

A non-relational database is a non-tabular database that uses different data models for storing, managing, and accessing data. The most common data models are

• document-oriented — to store, retrieve, and manage data as JSON documents;
• key-value — to represent data as a collection of key-value pairs, where keys are unique strings having corresponding data values;
• graph — to store data in the node-edge-node structure where nodes are data points and edges are their relationships; and
• wide-column — to store data in the tabular format with flexible columns, meaning they can vary from row to row in the same table.

As these databases aren’t limited to a table structure, they are called NoSQL. They allow for storing unstructured data such as texts, photos, videos, PDF files, and a bunch of other formats. Data is simple to query but isn’t always classified into rows and columns as in a relational database.

Scalability. When the number of data and requests increases, non-relational or NoSQL databases are usually scaled horizontally by adding more servers to the pool. They share data between various servers where each contains only a part of the data, decreasing the request-per-second rate in each server.

Performance. Non-relational databases are known for their high-performance: They have a distributed design, which lowers the performance load on the system and provides a large number of users with simultaneous access. Such databases can store unlimited sets of data that come in all types and shapes. They are also quite flexible when it comes to changing data types.

Security. Unlike relational systems, NoSQL databases have weak security, making them a major concern for many infrastructures. While they may provide ACID guarantees, they are typically available within the scope of one database partition, though some DBMSs offer advanced security features that meet strict security and compliance standards.

Since NoSQL databases allow for reserving various data types together and scaling it by growing around multiple servers, their never-decreasing popularity is understandable. Also, building an MVP it’s a great option for startups with sprint-based Agile development. NoSQL requires no pre-deployment preparations, making quick, time-lag-free updates to the data structure easier.

So, what are the most commonly used database systems in SQL and NoSQL? What are their main advantages and disadvantages, and how should businesses use them? Let’s take a deeper look.

Below, we’ll discuss the list of SQL databases including:

• MySQL
• MariaDB
• Oracle
• PostgreSQL
• MSSQL

and will complement it with such NoSQL databases as:

• MongoDB
• Redis
• Cassandra
• Elasticsearch
• Firebase

The screenshot below reflects the popularity of these and a few other databases.

Most popular database systems. Source: 2021 Developer Survey by StackOverflow

While more detailed descriptions of the aforementioned databases await you further in the post, the table here aims at providing a quick-look comparison against key criteria.

Database management systems in a nutshell

Now that you have a general understanding of the differences between relational and non-relational databases, we’re moving on to describe the main modern database management systems along with the pros, cons, and use cases of each.

MySQL

This is one of the most popular relational database systems. Originally an open-source solution, MySQL now is owned by Oracle Corporation. Today, MySQL is a pillar of LAMP application software. That means it’s a part of Linux, Apache, MySQL, and Perl/PHP/Python stack. Having C and C++ under the hood, MySQL works well with such system platforms as Windows, Linux, MacOS, IRIX, and others.

Pros of MySQL

Free installation. The community edition of MySQL is free to download. With a basic set of tools for individual use, MySQL community edition is a good option to begin with. Of course, there are other, prepaid options for Enterprise or Cluster purposes with richer functionality. Nevertheless, if your company is too small to pay for one of them, the free-to-download model is the most suitable for a fresh start.

Simple syntax and mild complexity. MySQL’s structure and style are very plain. Developers even consider MySQL a database with a human-like language. MySQL is often used in tandem with the PHP programming language. Because they share a gentle learning curve, it’s much easier to form a team to manage your database. Also, MySQL is easy to use. For instance, most of the tasks can be executed right in the command line, reducing development steps.

Cloud compatibility. Business-oriented by nature and originally developed for the web, MySQL is supported by the most popular cloud providers. It’s available on such leading platforms as Amazon, Microsoft, and others. This makes MySQL even more attractive and gives businesses room for growth.

Cons of MySQL

Scalability challenges. MySQL was not built with scalability in mind, which is inherent in its code. In theory, you can scale MySQL, but it will need more engineering effort as compared to any of the NoSQL databases. So, if you expect one day your database will increase substantially, keep this limitation in mind or choose another DBMS option.

Partial open-source support. Although MySQL has the open-source part, it’s mostly under Oracle’s license. This limits the MySQL community in terms of improving the DBMS. Why do you care? Because when you have completely open-source support, you expect many problem-specific implementations and community assistance. This is not the case when the software belongs to corporate owners and you’ll have to pay for support.

Limited compliance with SQL standards. Structured Query Language has specific standards. MySQL doesn’t completely follow them, i.e. MySQL provides no support for some standard SQL features. On the other hand, MySQL has some extensions and distinct features that don’t match the Structured Query Language standards. It’s not a big deal for small web applications. The issues may appear when you have to shift to other databases, which is likely to happen when your business starts growing.

Use cases

Small web-based solutions. MySQL database system is the best option when you’re designing a small, web-based solution with a small volume of data. For example, when building a local eCommerce store, MySQL may come in handy.

OLAP/OLTP systems. This is one of the best use cases for a MySQL database, as OLAP/OLTP don’t require complex queries and large volumes of data. Also, consider applying MySQL for the same reason if you’re building a business intelligence tool.

MariaDB

MariaDB, an open-source fork from MySQL, has commercial support. It works under a GNU General Public License and has similar commands, APIs, and libraries as MySQL.

Pros of MariaDB

Encryption. For MariaDB, open source doesn’t mean insecure. In addition to internal security and password check, MariaDB provides such features as PAM and LDAP authentication, Kerberos, and user roles. In combination with encrypted tablespaces, tables, and logs, it creates a robust protective layer for data. Above that, MariaDB publishes the related releases on each security update, keeping the security patches totally transparent.

Broad functionality. MariaDB has introduced a lot of new features in the last few years. For instance, GIS support suggests smooth coordinate storage and location data queries. Dynamic columns allow a single DBMS to provide both SQL and NoSQL data handling for different needs. You also can extend its functionality with plugins that are available at MySQL via 3rd parties only. MariaDB is shipped with storage engines for NoSQL backend, legacy databases migration tools, sharding options, and many more.

High performance. Although MariaDB originates from the MySQL engine, it got very far in terms of performance. Extensive optimization features improve thread pool management and data processing. Thus, when rows from the table are deleted, the operating system immediately accesses the free space, eliminating gaps in the tablespace. On top of that, the database management system suggests engine-independent table statistics. This feature enhances the optimizer’s performance, accelerates query processing, and helps customize data analysis.

Cons of MariaDB

Still growing community. Albeit MariaDB has a substantial open-source contribution, its community is yet to grow. Since this database management system was established not so long ago, the number of professionals is relatively small.

Gaps between MySQL and MariaDB update versions. Though the MariaDB team is constantly merging its code with that of MySQL, it’s already not that simple to keep them in line. Given currently existing differences between MariaDB 10.4 and MySQL 8.0, further deviations are yet to come. Additionally, MySQL engineers introduce some native features to the code that are only available to commercial MySQL users. This can create compatibility issues or migration problems from MariaDB back to MySQL.

Use cases

Since MariaDB is close to MySQL, it can be used to work with the same types of web-based applications. Additionally, you get extended location data storage, higher performance, and improved scalability.

Oracle

Oracle is a relational database management system created and run by the Oracle Corporation. Currently, it supports multiple data models like document, graph, relational, and key-value within the single database. In its latest releases, it refocused on cloud computing. Oracle database engine licensing is fully proprietary, with both free and paid options available.

Pros of Oracle

Innovations for daily workflow. Starting with Oracle 12c release, when the software entered the hybrid cloud era, new cloud computing technologies have appeared on a regular basis. With every new release, Oracle tries to keep up with the innovation pace while focusing on information security including active data guard, partitioning, improved backup, and recovery.

Strong tech support and documentation. Oracle ensures decent customer support and provides comprehensive tech documentation across multiple resources. So, you’ll likely find solutions to any issues that appear. You may also expect some community support.

Large capacity. Oracle’s multi-model solution allows for accommodating and processing a vast amount of data. Thanks to the recently released multi-tenancy feature, the database architecture now simplifies packing many databases and manages them smoothly. In combination with in-memory data processing capabilities, it creates a strong engine for synchronous data processing.

Cons of Oracle

High cost. Though the Oracle database has free editions, they are very limited in terms of functionality. Standard Edition, which doesn’t include all available features, costs $17,500 per unit. The Enterprise Edition is over $47,000 per unit.

Resource-consuming technology. Oracle database needs powerful infrastructure. Not only does installation require a lot of disk space, but you’ll also have to consider constant hardware updates if you deploy it on-premises.

Hard learning curve. Oracle database is not a system to start using right away. It’s better to have certified Oracle DB engineers to run it. Oracle’s documentation, while covering many issues, can sometimes be overwhelming and even confusing. So, to install and run an Oracle database, you’ll have to consider hiring dedicated experts.

Use cases

Given all those perks and pitfalls, you can consider Oracle RDMS as a reasonable solution for online OLTP, data warehousing, and even mixed (OLTP and DW) database application. If you have a billion records to hold and manage – and sufficient budget to support it – Oracle hybrid cloud software is a good option to choose.

PostgreSQL

This database management system shares its popularity with MySQL. This is an object-relational DBMS where user-defined objects and table approaches are combined to build more complex data structures. Besides that, PostgreSQL has a lot of similarities with MySQL. It’s aimed at strengthening the standards of compliance and extensibility. Consequently, it can process any workload, for both single-machine products and complex applications. Owned and developed by PostgreSQL Global Development Group, it still remains completely open-source. This DBMS is available for use with such platform systems as Microsoft, iOS, Android, and many more.

Pros of Postgre

Great scalability. Vertical scalability is a hallmark of PostgreSQL, unlike MySQL DBMS. Considering that almost any custom software solution tends to grow, resulting in database extension, this particular option certainly supports business growth and development.

Support for custom data types. PostgreSQL natively supports a large number of data types by default, such as JSON, XML, H-Store, and others. PostgreSQL takes advantage of it, being one of the few relational databases with strong support for NoSQL features. Additionally, it allows users to define their own data types. As your software business model may need different types of databases throughout its existence for better performance or application comprehensiveness, this option brings improved flexibility to the table.

Easily-integrated third-party tools. PostgreSQL database management system has the strong support of additional tools, both free and commercial. The scope of these includes extensions to improve many aspects. For example, ClusterControl provides impressive assistance at managing, monitoring, and scaling SQL and NoSQL open source databases. To make data comparison and synchronization more effective, consider using DB Data Difftective. In case you’re going to scale up your data to heavy workloads, pgBackRest backup and restore system will be a nice option to choose.

Open-source and community-driven support. Postgres is completely open-source and supported by its community, which strengthens it as a complete ecosystem. Additionally, developers can always expect free and prompt community assistance.

Cons of Postgre

Inconsistent documentation. While PostgreSQL has a large community and provides strong support for its participants, the documentation still lacks consistency and completeness. As the PostgreSQL community is rather distributed, the documentation doesn’t follow equal standards for all Postgre features.

Lack of reporting and auditing instruments. A significant shortcoming of PostgreSQL is the absence of revising tools that would show the current condition of a database. You have to continuously check if something goes wrong. There’s always a risk that DB engineers will notice a failure too late.

Use cases

Due to complicated queries and a wide choice of custom interfaces accomplished with predefined functions, PostgreSQL is a perfect match for data analysis and warehousing. If you are building a database automation tool, PostgreSQL is the best fit for it due to its strong analytical capabilities, ACID compliance, and powerful SQL engine. All in one, it significantly accelerates the processing of vast amounts of data. This DBMS is popular with financial institutions and telecommunication systems.

MSSQL

As a completely commercial tool, Microsoft SQL Server is one of the most popular relational DBMS, in addition to MySQL, PostgreSQL, and Oracle. It copes well with effective storing, changing, and managing relational data. To interact with SQL Server databases, DB engineers usually utilize the Transact-SQL (T-SQL) language, which is an extension of the SQL standard.

Pros of MSSQL

Variety of versions. Microsoft SQL Server provides a wide choice of different options with diverse functionalities. For instance, the Express edition with a free database offers entry-level tooling, the perfect match for learning and building desktop or small server data-driven applications. The Developers option allows for building and testing applications including some enterprise functionalities, but without a production server license. For bigger projects, there are also Web, Standard, and Enterprise editions, with a varying extent of administrative capabilities and service levels.

End-to-end business data solution. With a focus on mostly commercial solutions, MSSQL provides a lot of business value-added features. The optional selection of components allows building ETL solutions, forming a knowledge base, and implementing data clearance. Also, it provides tools for overall data administration, online analytical processing, and data mining, additionally providing options for report and visualization generation.

Rich documentation and community assistance. With Microsoft SQL Server aimed at comprehensive database maintenance, the full online documentation also reflects this concept. The consequently structured guidelines, numerous whitepapers, and demos give a full picture of the MSSQL data system. Also, Microsoft Premier provides access to dedicated Microsoft community support, which is an advantage when a DB engineer needs assistance.

Cloud database support. Being a part of the consistent Microsoft ecosystem, MSSQL can be integrated with Microsoft cloud, Azure SQL Database, or SQL Server on Azure Virtual Machines. The solutions allow shifting database administration to the cloud if your business software database becomes really overwhelming and hard to administer.

Cons of MSSQL

High cost. Being mostly used at enterprise scale, MSSQL Server remains one of the most expensive solutions. Speaking of numbers, the Enterprise edition currently costs over $14, 000 per core, sold as 2 core packs.

Unclear and floating license conditions. Another issue is the ever-changing licensing process. The pricing strategy itself is hard to understand and the elements included in a particular edition are floating, tending to shift from one to another.

Complicated tuning process. For those beginners who have to operate heavy data sets, working with query optimization and performance tuning may be problematic. As the process is not so obvious, it can create substantial bottlenecks early on.

Use cases

MSSQL Server is a reasonable option for companies with other Microsoft product subscriptions. As Microsoft creates a sustainable ecosystem with well-integrated services, the MSSQL here with its access to cloud and powerful data retrieval tools comes in handy.

MongoDB

A free, open-source, non-relational DBMS, MongoDB also includes a commercial version. Although MongoDB wasn’t initially intended for structured data processing, it can be employed for applications that use both structured and unstructured data. In MongoDB, databases are connected to applications via database drivers. They are widely available within the database management system. Multiple types of data are processed simultaneously and use the internal cache for this purpose.

Pros of MongoDB

Simple data access, storage, input, and retrieval. One of the benefits of MongoDB derived from its NoSQL nature is the fast and easy data operation. That is to say, data can be entered, stored, and withdrawn from the database quickly and without any additional confirmation. As with any other non-relational database, it places emphasis on RAM usage, so the records can be manipulated really fast and without any consequences to data integrity.

Easy compatibility with other data models. MongoDB is easily combined with different database management systems, both SQL and NoSQL types. Besides that, it has pluggable storage engine APIs. To make a long story short, this option allows third parties to build their own data storage engines for MongoDB. From a commercial point of view, it creates extra value for business software.

Horizontally scalable solution. Scalability – where data is spread out across a distributed network of manageable servers – is a facet of MongoDB’s fundamental nature. It becomes even more important for enterprises operating big data applications. Additionally, the database can allocate data across a cluster of machines. How can that help you? The data is distributed faster and equally, free of bulkiness. As it leads to faster data processing, the application performance is accelerated too.

Cons of MongoDB

Extensive memory consumption. The denormalization process, when previously normalized data in a database is grouped to increase performance, usually results in high memory consumption. Also, this DBMS keeps in memory all key names for each value pair. Beyond that, because there is no support for joins, Mongo databases have data oversupply, resulting in big memory waste and lower application performance.

Data insecurity. With a focus on fast data operation, MongoDB, like any other NoSQL DBMS, lacks data security. As user authentication isn’t a default Mongo option, and higher protection is available with a commercial edition only, you can’t consider it totally secure. Additionally, there are constant MongoDB update releases, with no guarantee that all amendments or data changes will work as they did before. Keep in mind that all manipulations should be formed around these updates, being covered with additional tests.

Complicated process to interpret into other query languages. As MongoDB wasn’t initially developed to deal with relational data models, the performance may slow down in these cases. Besides, the translation of SQL to MongoDB queries takes additional action to use the engine, which may delay the development and deployment.

Use cases

MongoDB works best in real-time data integration and database scalability. For instance, it’s the right option for product catalogs due to its capacity to stock a multiplicity of objects with various attribute collections. Also, consider here analytic platforms, as MongoDB’s speed provides dynamic performance that can help track the user’s behavior in real time.

Redis

An open-source, NoSQL, in-memory data structure store, Redis can also be used as a cache. Instead of documents, it uses key-value pairs. Its distinct feature is that there are several options for data structuring, such as lists, sets, and hashes.

Allowing for data replication and supporting transactions, Redis executes commands in a queue instead of setting it one at a time.

Pros of Redis

Rapid solution. Due to its replication and transaction features, Redis processes the data really fast. The absence of dependencies and in-memory data store type makes Redis a worthy competitor even among simple SQL alternatives.

Massive data processing. From the data perception and refining perspective, Redis can be considered a colossus. It can easily upload up to 1GB of data for one entry. Add built-in data caching and you get a powerhouse data machine.

Cons of Redis

Dependency on the application memory. Total reliance and dependency on the application memory is a real drawback. That is to say, your database will crash if its size exceeds the size of available memory.

No support for query language or joins. Regarding compatibility with other dataset types, Redis lags behind. Given that at some time your business may need scaling and using other data formats, having rapid entries as a single option leaves this issue open.

Use cases

Redis basically has a few different directions to work with. And the first of them is IoT applications. Here, heavy data from IoT devices can be transferred to Redis to process these records before keeping them in any steady data storage. Also, Redis is a perfect option for microservice architectures with scalable cloud hosting. As data here doesn’t have to be long-term persistent, Redis seems a reasonable decision.

Cassandra

Cassandra is a decentralized system developed by Apache. Cassandra is a free Java-based DBMS whose strength is in its multi-replication and multi-deployment features. These peculiarities allow for numerous query copying and deploying all of them at the same time. Being rapidly scalable, Cassandra allows for managing large data volumes by replicating them into multiple nodes. It eliminates the problem of database crash – if some of the nodes fail at any time, it’s replaced immediately, and the system keeps working as long as at least one single node is safe.

Cassandra uses its own query language, CQL. In its syntax, it’s very similar to SQL but doesn’t apply joins, replacing them with so-called column families. And the second difference is that not all columns in a table are stored for subqueries. Some of them are used as clustering columns, where adjacent data is put next to each other for fast retrieval. Why does that matter? It provides faster querying from massive datasets, accelerating data processing.

Pros of Cassandra

Data security. Due to its master node replication feature, Cassandra stays failure tolerant. It means that DB engineers can feel confident about data safety unless master nodes fail all at the same time. As long as it’s extremely unlikely, the database and the application built on it will stay sound and secure.

Flexibility and on-hand amendments. Cassandra’s simple syntax has the best of SQL and NoSQL. In addition to scalability, it largely contributes to dataset flexibility. Cassandra collects data on the go, and data retrieval shares the same simplicity, despite dataset size. This allows for enlarging the database to the fullest extent.

Cons of Cassandra

Slow reading. As Cassandra was initially designed for fast writing, its weakness lies in its incapacity for fast reading. One of the reasons for it is that there are no bottlenecks for information sent, so it needs more time to process.

Need for additional resources. As Cassandra processes multiple layers of data simultaneously, it demands enough power to do it. This means additional investment in both software and hardware. If this is the first time a company faces such a necessity and is not sure about the resources, then maybe it should consider other database systems.

Use cases

Thanks to even data distribution, Cassandra is relevant in applications where large volumes of information are processed. For instance, it’s a great choice for data centers. Also, Cassandra fits well with real-time analytics, as it allows linear scaling and data increase in real time. You may also consider it for applications with constant data streaming like weather apps. Another option is using it as a DBMS for an eCommerce store, as it allows for storing purchase history and other transactions. Add here feasibility to track such data types as order status and packages, and you’ll get the full solution with eCommerce delivery integration.

Elasticsearch

Elasticsearch is a NoSQL, document-oriented database management system having a full-text search engine at its heart. Built on the Apache Lucene library, it stores data as a JSON file, supports RESTful APIs, and uses a powerful analytical engine for faster data retrieval. Being open-source software, it includes both free and paid editions.

Pros of Elasticsearch

Scalable architecture. One of Elasticsearch’s peculiarities is its robust distributed architecture. Its key structure options, such as clustering, indexing, sharding, and many more, provide extensive horizontal scaling, which allows for accommodating terabytes of records with further automation. The architecture’s abstraction levels streamline system management on both individual and aggregate levels.

Fast data processing. Due to the distributed data structure and built-in parallelization, the Elasticsearch DB shows excellent performance results. Even when executing a complex data query, it generates lightning search result responses. This is partly available due to documents being maintained close to relevant metadata in the index, which makes them fast to find.

Cons of Elasticsearch

Lack of multi-language support. When handling request or response data, Elasticsearch DBMS lags behind. Though it’s perfectly combined with Cassandra DB to complement database performance, other languages and formats are not available for it. In these terms, it only supports JSON document format.

Limited consistent health check tools. When something goes wrong, as it may at any stage, Elasticsearch can only show status as “yellow” or “red.” Simply put, it has no reporting tools. Though issues are usually like memory threshold or disk capacity, DBA engineers complain about the situation.

Use cases

Due to its NoSQL distributed nature and flexible data models, Elasticsearch is a great tool for eCommerce products with huge databases that tend to use search engines. It’s very helpful when creating or updating a customer’s profile in terms of the workload that real-time engagement usually demands.

Firebase databases

Owned by Google, Firebase is a real-time Backend-as-a-Service used to develop web and mobile software. As far as NoSQL databases, there are two options: Firebase Realtime Database (providing real-time access to data residing in different platforms) and Cloud Firestore (offering more scalability and more complex data models). As such, both solutions fit nicely into the scenario when you need to deal with lots of data in real time: Changes for the databases are fetched as they happen. Google’s child stores data in JSON format and provides various data management offerings including a convenient data browsing tool.

Pros of Firebase

Beginner friendliness. Firebase can be a great option when there’s little software development expertise available, as it presents an easy-to-use environment to kick off the project.

Convenient data access. Both Realtime and Firestore are great options for storing and managing different types of data. There is a Firebase console for easy data access. Being cloud-based and NoSQL, they offer decent flexibility and scalability when the amount of data grows. Moreover, Firebase applications allow for working with responsive applications and keeping data updated even when there’s no Internet connection.

Top-notch documentation. The solution comes with well-written documentation that facilitates the work with provided services for all users. It includes guidelines, technical documentation, SDK references, information about integration, and much more. If we get back to the StackOverflow survey, Firebase is the 8th most popular database choice of developers. The size of the product community is significant, which makes it easy to find answers to problems that pop up.

Cons of Firebase

Limited querying capabilities. While this is valid only for Realtime Database, it’s still an issue if you are mainly planning to use this storage. The problem here is that you are restricted to making simple queries as there are no filter capabilities for more complex ones. This is because the entire database is a big JSON file with no options for data modeling.

Limited data migration. If you use Firebase to host all your data, migrating it to another platform can become an issue. The service lacks migration tools to transfer data or set the default database of a project.

Use cases

Firebase databases can be a good option to consider when your software deals with real-time data that needs to be synchronized between different browsers and devices. They are often chosen for such projects as messaging apps, social media apps, and gaming apps.

How to choose a database management system

Apart from the options described in the post, there’s a large number of database management systems out there. Each of them is good in its own way, having some drawbacks as well. Though we haven’t covered even a third of all databases, we tried to compare those commonly used for both small web applications and big data warehousing systems.

So, how do you choose the right one for your own software application?

If you are just starting a local eCommerce business, databases like MySQL can be a sensible jumping-off point that will also work well for web-based BI tools and OLTP systems.

In case you are striving to build the eCommerce giant with a complete buyer journey for your customer, you may go with Cassandra. To complement it with a powerful search engine, you may also attach the Elasticsearch database solution.

Speaking of Cassandra, it’s also a pretty respectable option for data centers and real-time analytics with oceanic volumes of data.

When speaking of analytic tools without multiple data layers, it may be reasonable to opt for NoSQL databases like MongoDB. It also performs well for product catalogs.

Following up the scope of data warehousing applications, MSSQL is also worth a mention, especially for companies with a number of other Microsoft subscriptions.

In terms of building an OLTP solution and data warehousing applications, Oracle is a good choice as well.

IoT application and microservice architecture that tend to scale its data hosting will summarize our list of best use cases with Redis.

Sure, there are more database systems to consider. It all depends on your business model and your business needs.

Which one do you use? Please share your ideas with us.

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