Storing data is something humans have been doing for thousands of years.

From carving information on rocks to developing language, writing on paper, and eventually storing data on disks, it’s an essential part of how we preserve and share knowledge.

But enough of the history lesson—if you're a developer, whether beginner, intermediate, or expert, you've likely encountered SQL and NoSQL databases, even if you didn't realize it at the time.

So, how do you make a wise choice when it’s time to develop an application? Are you starting a new project from scratch? Is it an integration into an existing system? Are you working on a project that needs structured data and integrity? Or maybe you're developing an event-driven application?

There are plenty of use cases to consider when choosing the right database for your application. For the sake of simplicity, we’ll focus on three common types of applications and the best database options for each:

• Small-scale or prototype applications

• Applications needing data integrity and robustness

• Event-driven applications

Small-Scale or Prototype Applications

If you're building an MVP or testing an idea, you'll likely understand the importance of making a quick prototype to validate your concept before committing fully. Speed is crucial at this stage, and the same applies to how you handle data storage. Here are two common challenges:

1. You need a simple database with a minimal setup that can be easily moved around without complex configuration—after all, you're just trying to validate an idea.

2. You’re unsure about the structure of your data, and you want flexibility for evolving fields without worrying about strict data types or rigid schemas.

Personal Recommendations

For the first case, I recommend SQLite. It’s a simple, file-based storage solution that doesn’t require setting up a server. All data is stored in a file, making it perfect for lightweight applications that need quick deployment.

You’ll often find SQLite in embedded systems and mobile apps because it’s fast, easy to use, and doesn’t require much configuration. Despite its simplicity, SQLite can handle a surprising amount of data, making it a solid choice for early-stage projects.

In the second case, MongoDB is an excellent choice, especially if you’re unsure about how your data will evolve. MongoDB is schema-less, allowing you to store and modify data without worrying about rigid structures upfront.

It’s easy to set up and works well for both small-scale prototypes and larger applications. That said, MongoDB has its trade-offs—at scale, you may run into performance challenges, and its lack of strict data constraints can lead to inconsistent or unstructured data. However, MongoDB’s flexibility is often worth the trade-off for a prototype.

In addition to SQLite and MongoDB, you might also want to explore other powerful tools for rapid prototyping, such as LevelDB, Couchbase Lite, Firebase Realtime Database, and Supabase. These databases offer unique strengths like fast key-value storage, offline-first capabilities, real-time data syncing, and easy-to-use SQL features, making them excellent choices depending on your project’s specific needs.

During the prototyping phase, the focus should be on speed and flexibility rather than strict data integrity or performance optimization. Once your prototype is validated, you can revisit your database decisions as you move into more structured and large-scale development phases.

This brings us to the next step: building applications that require data integrity and scalability.

Applications Needing Data Integrity and Robustness

As your project moves beyond the initial stages and becomes a more critical system, the need for data consistency, integrity, and scalability becomes essential. Whether you’re managing sensitive information or developing a business application with complex data relationships, your database choice now needs to support strict data management practices. At this point, two common scenarios come into play:

1. You have a clear understanding of your data structure and require a database that ensures data integrity, consistency, and complex querying capabilities.

2. Your application is growing in complexity, and while you still need flexibility, you also need the ability to scale while handling large amounts of unstructured or semi-structured data.

Personal Recommendations

For the first case, PostgreSQL is an excellent choice. It’s a robust, open-source SQL database known for its ACID compliance, meaning it guarantees reliable transaction processing. PostgreSQL also excels in handling structured data, supports complex queries, and offers full-text search capabilities.

It is ideal for applications that require strict data integrity and need to process large volumes of data reliably. Additionally, its support for JSON allows flexibility with semi-structured data, making PostgreSQL a versatile tool for growing projects.

In the second case, MongoDB continues to be a strong contender, particularly for applications handling big data or evolving data models. While often used in early-stage projects for its schema-less design, MongoDB can scale horizontally to manage vast datasets across multiple servers.

It is widely used by large-scale companies like Uber and Toyota, which rely on its ability to handle unstructured data and adapt to changing requirements. That said, as your application grows, you may need to carefully manage MongoDB’s performance and data consistency, especially if you’re dealing with highly critical data.

While PostgreSQL and MongoDB are popular choices, there are other SQL and NoSQL databases worth considering for applications needing robust data integrity and scalability. MariaDB and Oracle SQL are both highly regarded for their performance in enterprise-level systems.

MariaDB offers great concurrency control and is widely adopted for its high availability, while Oracle SQL is known for handling mission-critical workloads with reliability and efficiency. On the NoSQL side, Cassandra is another powerful option, designed for large, distributed systems where fault tolerance and horizontal scaling are paramount. Companies like Netflix and eBay use Cassandra for its ability to handle massive amounts of data across geographically distributed clusters.

In addition to PostgreSQL, MongoDB, and other large-scale databases, tools like Cassandra, Couchbase, and MariaDB offer tailored solutions depending on the needs of your application. Each database has strengths that make it a good fit for high-traffic, data-intensive applications.

As your application continues to grow and evolve, the decision between SQL and NoSQL will depend on the type of data you're managing and your specific scalability needs.

In the next step, we’ll dive into a different use case: applications that are event-driven and require real-time data processing.

Event-Driven Applications

So far, we’ve talked about small-scale and prototype applications, as well as those requiring data integrity. But another major category that’s increasingly popular in today’s tech landscape is event-driven applications.

Whether it’s real-time notifications, WebSockets, or IoT systems, event-driven architectures are everywhere. These types of applications rely on responding to events as they happen, often in real-time, which is crucial for things like stock trading platforms, real-time analytics, and messaging services.

For event-driven systems, the challenge is not just in processing the data but in doing so at scale, with high-speed delivery and reliability. To handle these demands, you need a combination of tools designed for fast data access, real-time streaming, and reliable storage.

Personal Recommendations

For real-time data handling, Redis is an ideal tool. Redis is an in-memory data store, meaning it’s incredibly fast, making it perfect for caching and managing high-speed data flows.

In an event-driven application, Redis can serve as a temporary, fast-access storage for real-time data that needs to be processed and delivered quickly to multiple clients. For example, in a stock trading platform, Redis can store the latest stock prices, which need to be retrieved in milliseconds to update dashboards or send alerts to users.

When it comes to managing data streams, Kafka or RabbitMQ are excellent choices. Both are messaging systems that handle real-time data pipelines, allowing you to distribute data to multiple listeners as soon as events occur.

For instance, in a trading system, data on market transactions can be streamed through Kafka, ensuring that updates are distributed to various services, such as those that handle client notifications or analytics processing.

But in an event-driven architecture, fast data access and streaming alone aren't enough—you also need to persist the data for long-term storage. This is where databases like MongoDB come in.

MongoDB, with its schema-less design and scalability, can store event data over time, making it available for future queries or analysis. In combination with Redis and Kafka, MongoDB ensures that your system can handle both real-time data and long-term persistence without sacrificing performance.

Example Use Case

Let’s consider a trading system as a use case to see how these tools can work together. In such a system, market data needs to be ingested, processed, and delivered to users as quickly as possible. Here’s how it might work:

1. Redis stores the real-time price updates for stocks, allowing the application to retrieve this data in milliseconds and send it to clients via WebSockets.

2. Kafka manages the flow of data from external sources, streaming market data into the system and distributing it to different services, such as pricing engines, notification systems, and analytics modules.

3. MongoDB stores all the historical transaction data, allowing traders to access past information for analysis or reporting.

By combining these tools, you can build an event-driven system that handles large volumes of real-time data while ensuring fast access and reliable storage.

This approach allows you to scale effortlessly, maintaining high performance even as the number of users and the amount of data grow.

My Thoughts

At this point, I think we’ve covered some of the most common tools that can be used for a wide range of applications you might encounter in your journey as a developer. Naturally, different systems and applications will call for different solutions, and the databases we've discussed are just a few examples.

So, I encourage you to continue your research, dig deeper, and explore other tools that may be a perfect fit for your specific needs.

However, it's important to remember that none of this is set in stone. These recommendations aren’t meant to be absolute truths or universal solutions. The key is choosing the right tools for your project.

For example, if you're working on a small-scale application and you feel confident about using something like PostgreSQL to validate your idea, go for it! Just keep in mind the potential technical debt that might come with overengineering early on. Similarly, if you’re building an event-driven system and decide to rely on a traditional database for real-time data storage, that can be a viable choice—just be aware of the trade-offs that come with that decision.

At the end of the day, it's all about finding the right balance for your system. Always research thoroughly, talk to people who have used these tools in the field, and seek advice from others in your industry. Understand how these technologies fit into your project and what works best for your particular needs.

Conclusion

Thank you for reading! In this article, we’ve explored different databases suited for various types of applications, from small-scale prototypes to data-heavy, event-driven systems.

If you have experiences to share or any thoughts on the article, feel free to drop them in the comments below.

I’m always open to feedback and happy to engage in discussions that can help us all learn and grow.

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