How to Choose the Right Database for Your Needs

Article summary:
Discover how to select the perfect database for your project. Explore key factors, types of databases, and expert tips to make an informed decision.

Alex Kirpichny

Alexander Kirpichny

Product Owner, Ispirer Systems

Choosing a database can feel overwhelming. So many options, so many features, and every choice comes with long-term consequences. Pick the wrong one, and you might end up with performance issues, unexpected costs, or a system that doesn't scale the way you need.

So how do you decide? Instead of getting lost in technical jargon and marketing promises, let's focus on what really matters. Here's a clear, practical guide to finding the right database for your needs—without regrets down the line.

Understanding your project's requirements

Before picking a database, take a step back. The best choice isn't about features or costs in the first place. It's about what your project needs. A high-performance database sounds great, but do you really need a system built for millions of transactions per second? Let's break it down.

What type of data will be handled?

Structured or unstructured?

Relational databases (like PostgreSQL or MySQL) are best for structured data like customer records, financial transactions, and product inventories.

Unstructured data (videos, logs, sensor data) calls for NoSQL options like MongoDB or Elasticsearch.

Fun fact: 80-90% of enterprise data is unstructured, yet many still force it into relational databases, leading to headaches and database migrations.

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How much data are we talking about?

A small startup with a few gigabytes of data has different needs than an enterprise managing petabytes. Amazon stores over 100 petabytes of customer data, but most companies don't need that scale. If your data is expected to grow fast, databases like Cassandra or CockroachDB handle massive, distributed workloads better than a traditional SQL setup.

How will data be accessed?

Some applications are read-heavy (analytics dashboards, reporting tools), while others are write-heavy (real-time chat apps, financial transactions). A mismatch here can cause serious bottlenecks. Redis, for example, is built for lightning-fast reads, so it's perfect for caching. Meanwhile, PostgreSQL and MySQL balance reads and writes well for transactional applications.

Performance and scalability needs

How fast does your database need to be? A delay of even 100 milliseconds can drop conversion rates by 7%, so performance matters. If you expect millions of queries per second, databases like DynamoDB or ScyllaDB are optimized for high throughput. If your workload is steady but requires complex queries, PostgreSQL's indexing and optimization features can handle it.

Security and compliance

How much sensitive data are you handling? Regulations like GDPR, HIPAA, and SOC 2 might dictate your choice. Some databases offer built-in encryption, audit logs, and fine-grained access control. Healthcare and finance companies often turn to databases with strong security models, like Oracle or AWS Aurora, to meet compliance needs.

Budget constraints

Enterprise-grade databases can get expensive. Licensing for Oracle can run into hundreds of thousands of dollars per year. On the other hand, open-source options like PostgreSQL or MariaDB offer powerful features without the hefty price tag. Cloud-based databases add flexibility but watch out for hidden costs. AWS RDS, for example, charges for storage, backups, and I/O operations separately.

Types of databases: SQL vs. NoSQL databases

Not all databases are built the same. Some thrive on structure and order, others embrace flexibility and chaos. Understanding the difference between SQL and NoSQL databases is key to making the right choice.

SQL databases: the organized, rule-following choice

SQL (Structured Query Language) databases store data in a structured, tabular format with predefined schemas. They are like an automated Excel spreadsheet, where each row represents a record, each column a field, and strict rules ensure consistency.

Popular options:

MySQL is open–source and widely used for web applications, and it supports ACID transactions. It runs behind Facebook, X, and YouTube.
PostgreSQL is powerful and open-source and is known for advanced features like JSON support and extensibility. It's used by Instagram and Reddit.
Microsoft SQL Server is enterprise-focused, strong on security and database analytics, and often used in corporate environments.

Use cases & advantages:

Transactional applications – Banking systems, e-commerce sites, CRM software.
Data integrity – Enforces consistency, which is great for applications where accuracy matters (finance, healthcare).
Complex queries – SQL makes analyzing and manipulating large datasets easy with JOINs, indexing, and stored procedures.

SQL databases are best when structure, reliability, and consistency are top priorities. But they can struggle with high-velocity, unstructured data.

NoSQL databases: the flexible, scale-first choice

NoSQL databases ditch rigid schemas and table structures in favor of flexibility. They handle unstructured or semi-structured data, scale horizontally, and work well for real-time applications. Instead of tables, they use document, key-value, column-family, or graph models.

Popular options:

MongoDB is document–based and great for storing JSON-like data. It is used by eBay and Forbes.
Cassandra is highly scalable and is designed for massive, distributed workloads (used by Netflix and Apple).
Redis is a lightning-fast key-value store, often used for caching, session storage, and leaderboards.

Use cases & advantages:

Big data & high traffic – Ideal for applications with millions of users (social media, IoT, recommendation engines).
Flexibility – No fixed schema, making it easy to store and modify data on the fly.
High availability – Designed for distributed, fault-tolerant environments (Cassandra's architecture means no single point of failure).

NoSQL databases shine when speed, scalability, and flexibility matter more than strict structure. However, they often sacrifice consistency in favor of performance.

SQL vs. NoSQL: which one wins?

Need structured data with strong consistency? SQL is your best bet.
Handling massive, unstructured, high-speed data? NoSQL is built for that.

Some modern applications even use both—SQL for critical transactional data and NoSQL for real-time analytics or caching. The right choice depends on your project's needs, so weigh your options carefully.

Key factors in choosing a database

But picking a database doesn't mean deciding between SQL vs. NoSQL only. You need to find the right balance of performance, security, scalability, and cost. A perfect database for one project could be a disaster for another. Here's what to consider before making your choice.

Data structure compatibility

Your database should match the type of data you're dealing with. Again, a relational database (SQL) works best for structured, tabular data like customer records, inventory, or financial transactions. NoSQL databases handle unstructured data like logs, images, videos, and sensor data better.

Scalability

Scalability means how well your database handles growth. There are two main approaches:

Vertical scaling (scaling up): Adding more power (CPU, RAM, storage) to a single server. Easier but more expensive. Works for databases like MySQL and PostgreSQL.
Horizontal scaling (scaling out): Adding more servers to distribute the load. More complex but essential for massive traffic. NoSQL databases like MongoDB and Cassandra are designed for this.

For example, Amazon handles millions of requests per second during peak shopping events. Without horizontal scaling, their system would collapse.

Security features

Data breaches are expensive and damaging: Companies lost an average of $4.88 million per breach in 2024. Your database must offer encryption, access control, and compliance with industry regulations like GDPR, HIPAA, or SOC 2.

For example, healthcare applications require databases that support strong encryption (at rest and in transit) and audit logging to comply with patient data laws.

The best choices for this use case are Microsoft SQL Server, AWS Aurora, and PostgreSQL, which have built-in database security features for enterprise compliance.

Integration capabilities

Your database needs to connect with apps, analytics tools, and cloud services. Choosing a database with strong integration support can save time and the headaches of custom development.

For example, eCommerce platforms like Shopify integrate databases with payment gateways, inventory management systems, and AI-powered recommendation engines. PostgreSQL and MySQL have broad support, and NoSQL options like MongoDB offer native JSON storage for easy API interactions.

Community support and documentation

A database with a strong community and solid documentation means better troubleshooting, faster bug fixes, and a constant stream of improvements.

For instance, PostgreSQL has a massive developer community so it allows for faster feature updates and robust third-party tools. Oracle, on the other hand, is powerful but has expensive, proprietary support.

MySQL and PostgreSQL are two of the top five most popular databases globally, partly due to their open-source communities.

Upfront vs. long-term costs

Databases come in two main pricing models:

Open-source (free, but you handle maintenance) like PostgreSQL, MySQL, and MariaDB.
Commercial (paid, but includes support and enterprise features) like Oracle and Microsoft SQL Server.

Oracle's licensing can cost hundreds of thousands per year, but cloud-hosted databases (AWS RDS, Google Cloud SQL) offer pay-as-you-go pricing.

However, even free databases need maintenance, backups, and scaling, which can add up over time.

Availability & reliability

If your application goes down, so does your business. High-availability (HA) features help keep things running without crashes. They include:

Replication: Keeps copies of your database in sync across multiple locations. Essential for disaster recovery.
Failover: Automatically switches to a backup database if the primary one fails.
Backups: Regular backups prevent data loss from corruption or cyberattacks.

Banks rely on databases with failover and replication to make sure there's zero downtime because 'sorry, your account balance is unavailable' isn’t an option.

The best choices for this use case are AWS Aurora, PostgreSQL (with streaming replication), and Cassandra (for distributed uptime).

Performance expectations

Slow databases kill the user experience. Choosing a database that meets your speed and latency needs is critical for achieving very real monetary business goals. What to look for:

Read vs. write optimization: Some databases handle reads (queries) faster, and others optimize for writes (data entry). Redis is fast for reads, and PostgreSQL balances both well.
Latency & throughput: If your application handles millions of users, a low-latency, high-throughput database (like DynamoDB or ScyllaDB) will be the best option for it.

As we said, a 100-millisecond delay can cut conversion rates by 7%, and it has a very direct effect on revenue.