blog banner

Comparing Database Management Systems: MySQL, PostgreSQL, MSSQL Server, MongoDB, Elasticsearch, and others

In the world of software development, choosing the right database is a crucial decision that can significantly impact your application's performance, scalability, and ease of use. With many options available, it can be challenging to determine the best database management system (DBMS) that will perfectly suit your needs.

In this article, we’ll compare the 12 most commonly used DBMSs: MySQL, MariaDB, Oracle, PostgreSQL, MSSQL, SQLite, MongoDB, Redis, Cassandra, Elasticsearch, Firebase, and DynamoDB. We will focus on their business-related benefits and challenges while highlighting the ideal use cases for each. With this database comparison at hand, you will be able to make an informed decision for your project.

What is a database management system?

A Database Management System (DBMS) is a specialized software designed to store, retrieve, and manipulate data. It acts as a mediator between the database, applications, and user interfaces to manage and organize data effectively. The system provides a comprehensive suite of tools to govern databases, ensuring data security, consistency, and integrity.

A DBMS supports various applications, from simple storage and retrieval tasks to complex data-driven systems, by implementing efficient data access and management practices. Additionally, the system can handle concurrent users, maintain transactional consistency, and provide robust backup and recovery options, making it an essential component in any data-centric environment.

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

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 vs non-relational databases in a nutshell.

Relational vs non-relational databases in a nutshell.

Relational or SQL databases

A relational database management system (RDBMS) is an information repository that organizes data into tables consisting of rows (records) and columns (attributes that contain the properties of these records). Each table represents a relation, and the rows (also called tuples) hold individual records within that relation. RDBMSs have a predefined schema with a strict structure and clear dependencies between different data points.

So tables in relational databases are connected to other tables through primary key or foreign key relationships. A primary key is a unique identifier for each record in a table, ensuring that no two records have the same value for that specific column or set of columns. On the other hand, a foreign key is a column or a set of columns in one table that refers to the primary key in another table, establishing a link between them.

Despite these connections between tables, the term relational in relational database systems comes from the mathematical concept of relations. Dr. Edgar F. Codd proposed this idea as a new way to organize and manage data using principles from mathematics in his seminal paper "A Relational Model of Data for Large Shared Data Banks," published in 1970.

The second name of such systems is SQL databases. This is because Structured Query Language (SQL) is used to communicate with and manage these databases.

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 smaller to bigger machines often involves downtime. Scaling an SQL database between multiple servers (horizontal scaling) can be challenging as it requires data structure changes and additional engineering efforts.

Performance. Relational databases perform well 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, the performance may suffer when the amount of data and user requests grows.

Security. Due to the integrated structure and data storage system, SQL databases don’t require much engineering effort to render 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). 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 or non-tabular database uses different data models for storing, managing, and accessing data. The most common data models are

  • document-oriented — to store, retrieve, and manage data such 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 data sets 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. However, 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 across multiple servers, their never-decreasing popularity is understandable Also, NoSQL databases can be highly advantageous when it comes to building an MVP. They don’t require 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 following list of SQL databases:

  • MySQL
  • MariaDB
  • Oracle
  • PostgreSQL
  • MSSQL
  • SQLite

and will complement it with such NoSQL databases as:

  • MongoDB
  • Redis
  • Cassandra
  • Elasticsearch
  • Firebase
  • Amazon DynamoDB

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

Most popular database systems. Source: 2022 Developer Survey by StackOverflow

Most popular database systems. Source: 2022 Developer Survey by StackOverflow

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

Database management systems comparison

Database management systems comparison.

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

MySQL is one of the most popular relational database systems. Originally an open-source solution, MySQL is now 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 versions 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 leading platforms like 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. Theoretically, you can scale MySQL, but it will need more engineering effort than 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 an 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 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 will likely 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.

IoT applications. MySQL can be used for small to medium-sized Internet of Things (IoT) applications to store and manage sensor data, device information, and user interactions.

Keep in mind that while MySQL supports these use cases, its performance and suitability might vary depending on the specific requirements and size of the project.

MariaDB

MariaDB, an open-source fork from MySQL, is a great SQL database example with commercial support. It works under a GNU General Public License and has similar commands, APIs, and libraries to 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. Combined with encrypted tablespaces, tables, and logs, it creates a robust protective layer for data. Beyond that, MariaDB publishes 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 can also extend its functionality with plugins that are available at MySQL via 3rd parties only. MariaDB is shipped with storage engines for NoSQL backend, legacy database migration tools, sharding options, and much more.

High performance. Although MariaDB originates from the MySQL engine, it's gotten 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 a growing community. Although MariaDB has substantial open-source contribution, its community has yet to grow much. Since this database management system was established not so long ago, the number of professionals involved is relatively small.

Gaps between MySQL and MariaDB update versions. Though the MariaDB team is constantly merging its code with MySQL's, it’s already not that simple to keep them in line. Given the currently existing differences between MariaDB 10.6 and MySQL 8.0.32, further deviations are yet to come. Additionally, MySQL engineers introduced some native features to the code that are only available to commercial MySQL users. This can create compatibility issues or data 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. Among all the types of SQL databases, Oracle stands out. Currently, it supports multiple data models like document, graph, relational, and key-value within a 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 the Oracle 12c release, when the software entered the hybrid cloud era, new cloud computing technologies appeared regularly. 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. The 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

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

Financial institutions. Oracle is widely used in the financial sector, where data integrity and security are paramount. Banks, insurance companies, and investment firms often rely on Oracle to manage sensitive financial data and transactions.

Government and public sector. Oracle is often chosen for its robust features and security in government and public sector applications, including national security, healthcare, and transportation systems.

PostgreSQL

The PostgreSQL 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 platforms like Microsoft, iOS, Android, and many more.

Pros of Postgre

Great scalability. Vertical scalability is a hallmark of PostgreSQL. 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 many 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. The 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 in managing, monitoring, and scaling SQL and NoSQL open-source databases. To make data comparison and synchronization more effective, consider using DB Data Directive. In case you’re going to scale up your data to heavy workloads, the pgBackRest backup and restore system will be a nice option to choose from.

Open-source and community-driven support. Postgres is completely open-source and supported by its community, strengthening 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 strongly supports its participants, the documentation still lacks consistency and completeness. As the PostgreSQL community is rather distributed, the documentation doesn’t follow uniform 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 all, 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 DBMSs, 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 offering solutions 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 correspondingly 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. 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 the enterprise scale, MSSQL Server remains one of the most expensive solutions. Speaking of numbers, the Enterprise edition currently costs over $15, 123 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 builds a robust ecosystem with seamless integration of services, MSSQL emerges as a powerful database solution. With its cloud accessibility and advanced data retrieval tools, MSSQL proves to be a valuable asset for businesses, ensuring a sustainable and efficient system that aligns with evolving needs.

SQLite

SQLite is a self-sufficient, serverless, and no-configuration-required database management system. Frequently utilized as an embedded database, it is popular for small-scale mobile and desktop applications.

Pros of SQLite

Small in size and easily portable. SQLite is a streamlined database engine that operates without a separate server process. The entire database is contained within a single cross-platform disk file, enhancing its portability and simplifying its integration into applications.

Minimal resource consumption. SQLite is engineered for optimal memory and disk space efficiency, making it an ideal choice for applications with constrained resources, such as those found in mobile and IoT devices.

Reliable and user-friendly. SQLite is an ACID-compliant database, ensuring the integrity and consistency of data. Additionally, it is simple to set up and demands minimal configuration.

Cons of SQLite

Restricted concurrency. SQLite employs file-based locking, limiting its capacity to manage multiple concurrent write operations. This makes it less appropriate for applications with high write concurrency or multiple users accessing the database simultaneously.

Absence of advanced features. SQLite lacks some of the sophisticated features found in other database management systems, such as stored procedures, triggers, or user-defined functions.

Restricted scalability. Owing to its serverless structure, SQLite is not tailored for extensive applications or distributed settings. Its performance may diminish when handling substantial datasets or elevated levels of concurrent access.

Use cases

SQLite is well-suited for modest-sized applications and mobile and desktop applications that demand a lightweight, easily portable, and user-friendly database. It is also fitting for embedded systems and IoT devices with limited resources where implementing a server-based DBMS would be unfeasible.

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 data types 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. It’s a free Java-based DBMS with multi-replication and multi-deployment features as its strengths. 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 the system doesn't have bottlenecks for incoming information. So while data can be written to the database quickly, the system may take longer to process and retrieve that data. This can be further explained by the fact that Cassandra spreads the data across multiple nodes in a cluster. When you query the data, it may have to read from various nodes, which can slow down the read performance.

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 the feasibility of tracking 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 the 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 regarding 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 tools 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 12th 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.

Amazon DynamoDB

Amazon DynamoDB is a NoSQL database service managed by Amazon Web Services (AWS), designed for applications necessitating high scalability, low latency, and consistent performance.

Pros of Amazon DynamoDB

Exceptional scalability. DynamoDB can effortlessly scale up or down to accommodate any level of traffic and data, making it ideal for applications that experience rapid growth or fluctuating demand.

Low latency. DynamoDB delivers single-digit millisecond latency for read and write operations, ensuring quick and consistent data access, vital for real-time applications.

Fully managed service. DynamoDB handles operational tasks such as hardware provisioning, patching, and backups as a managed service, allowing you to concentrate on your application's development and features.

Adaptable data model. DynamoDB supports various data models, including key-value and document-oriented models, offering flexibility in structuring and querying your data.

Cons of Amazon DynamoDB

High cost. DynamoDB's pricing structure can be intricate and may result in higher costs compared to self-managed NoSQL databases, particularly for applications with variable or unpredictable workloads.

Restricted querying capabilities. Although DynamoDB supports basic queries and filters, it does not provide support for complex querying and aggregation operations needed in some use cases.

Vendor lock-in. As a proprietary AWS service, transitioning from DynamoDB to another database system might necessitate significant effort and planning.

Use cases

Amazon DynamoDB is well-suited for applications requiring high scalability, low latency, and consistent performance, such as serverless apps, eCommerce platforms, gaming platforms, and IoT solutions. It is also ideal for serverless architectures and applications that utilize other AWS services, as it integrates seamlessly with the AWS ecosystem.

How to choose a database management system

Apart from the options described in the post, there are a lot of other 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 an 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 applications 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.

Comments3

Sort by 
Subscribe to our newsletter

Stay tuned to the latest industry updates.

By clicking subscribe you confirm, that you understand and agree to the Privacy Policy

Latest Business Articles

Join us on the TechTalks

Discover new opportunities for your travel business, ask about the integration of certain technology, and of course - help others by sharing your experience.

Visit TechTalks
Write an article for our blog

Almost 50 guest articles published from such contributors as Amadeus, DataQuest, MobileMonkey, and CloudFactory.

Read how to become a contributor.