SQL vs. NoSQL: Will the Wrong Data Store Destroy your Transaction Records?

Storing customer information is a fundamental part of running a business. For companies like fintechs, this goes beyond just names and emails. They often need to store user profiles, authentication details, bank account information, transaction records, API logs, and more.

But figuring out the best way to store all this data isn’t always simple. There are industry standards and regulations that must be followed to ensure the business operates safely and remains compliant.

Fintechs also generate a massive amount of transaction data. This data isn’t just for record-keeping. It needs to be accessible for tasks such as processing claims, handling chargebacks, and generating account statements. Due to the volume, it’s important to use the appropriate database to keep customer experiences smooth and operations running efficiently.

In this guide, you’ll learn about SQL and NoSQL databases and how each one handles the unique challenges that come with payment processing.

SQL vs. NoSQL Databases: The Basics

When working with databases, one of the first decisions you’ll run into is choosing between SQL and NoSQL. Each has its strengths, depending on what you’re building and how your data is structured.

SQL Databases

An SQL database is a type of relational database management system that uses SQL (Structured Query Language) to store, retrieve, and manage data. It organizes data into tables, which are made up of rows and columns, similar to a spreadsheet.

Some core concepts to understand in SQL databases:

• Relational: Data is stored in related tables that can reference each other.
• Primary Key: A unique identifier for each role, like an id.
• Foreign Key: A field that links one table to another. For example, an orders table might include user_id to connect each order to a user.
• Schema: The blueprint of the database. It defines tables, columns, data types, and how everything connects.

Popular SQL databases include PostgreSQL, MySQL, Microsoft SQL Server, and Oracle DB.

NoSQL Databases

NoSQL databases take a different approach. They don’t follow the traditional table-based structure. Instead, they store data in formats like key-value pairs, documents, graphs, or wide-column stores, depending on the type of database.

Some core concepts to understand in NoSQL databases:

• Data Model: Defines how data is stored and organized, which can be in the form of documents, key-value pairs, or graphs.
• Collections: These work similarly to tables in SQL databases and are used to group related data.
• Keys/IDs: Unique identifier for each entry.
• Indexes: Help speed up data retrieval by targeting specific fields or attributes.

Common NoSQL database systems include MongoDB, Redis, Cassandra, and Neo4j.

Key Differences at a Glance
Here’s a quick comparison of SQL and NoSQL databases:

With the basics of these databases covered, let’s explore the points you need to consider when selecting a database for your transaction records.

Structure vs. Flexibility

Financial transaction records are usually structured in a consistent format. You’ll typically find fields like transaction IDs, amounts, timestamps, and other predictable data points. This structure makes it easy to design your database tables. You can map each column to a specific data type and rely on that structure to stay consistent.

If this were always the case, picking a database for a fintech product would be simple. But transaction records aren’t always predictable. You also need to account for metadata that changes based on the payment method, currency, or regional requirements.

Take our Direct Charge API for example. When you charge and validate a card, the response includes standard parameters that can be handled easily by a structured database. At the same time, each card may have its own authorization method, which is indicated in the meta field. One card might use a PIN, another might require an OTP, and others may carry flags from the issuer. This dynamic metadata doesn’t fit neatly into a fixed schema, so your database needs to be flexible enough to store it without creating chaos.

{
    "status": "success",
    "message": "Charge initiated",
    "data": {
        "id": 1254647,
        "tx_ref": "UNIQUE_TRANSACTION_REFERENCE",
        "flw_ref": "IUSE9942171639769110812191",
        "device_fingerprint": "N/A",
        "amount": 7500,
        "charged_amount": 7500,
        "app_fee": 105,
        "merchant_fee": 0,
        "processor_response": "Pending redirect to issuer's 3DS authentication page",
        "auth_model": "VBVSECURECODE",
        "currency": "NGN",
        "ip": "N/A",
        ...............
        "card": {
            "first_6digits": "543889",
            "last_4digits": "0229",
            "issuer": "MASTERCARD MASHREQ BANK CREDITSTANDARD",
            "country": "EG",
            "type": "MASTERCARD",
            "expiry": "10/31"
        }
    },
    "meta": {
        "authorization": {
            "mode": "redirect",
            "redirect": "https://auth.coreflutterwaveprod.com/transaction?reference=IUSE9942171639769110812191"
        }
    }
}

SQL databases like PostgreSQL or MySQL are great at handling structured data. You can create normalized tables that keep related information consistent and easy to manage. For example, your users table can link directly to payment transactions, payment methods, and settlement records. This structure makes reconciliation easier to implement, especially because of the consistency you get from ACID properties and the predictability of SQL joins.

On the other hand, NoSQL databases like MongoDB or DynamoDB give you flexibility when dealing with unstructured data that is less predictable, such as the meta object in a payment response. You can store the entire transaction, including any custom fields, inside a single document. This means you don’t have to constantly update your schema just to support a new payment method or metadata. That kind of flexibility is useful, but it comes with trade-offs. Queries can become more complex, and consistency across documents isn’t always guaranteed.

Reconciliation

This is one of the most critical areas where consistency truly matters. When you’re matching thousands of transactions each day against internal ledgers, bank statements, or settlement records, you can’t afford to get things wrong. Accuracy in how your data is stored becomes a big deal.

SQL databases work well in this area. They give you relational structure and strong consistency, which makes it easier to identify mismatches and investigate them with confidence. You can write precise queries that compare payment and settlement data side by side. For example:

SELECT
    pt.transaction_id,
    pt.amount,
    pt.settlement_date,
    st.settled_amount,
    ABS(pt.amount - st.settled_amount) as variance
FROM payment_transactions pt
LEFT JOIN settlement_records st ON pt.transaction_id = st.transaction_reference
WHERE pt.status = 'completed'
AND pt.settlement_date = '2024-01-15'
AND (st.transaction_reference IS NULL OR ABS(pt.amount - st.settled_amount) > 0.01);

With NoSQL, reconciliation tends to be more complex. Related data may be scattered across different documents or collections. You might need to run multiple queries to piece things together. Additionally, because updates don’t always occur simultaneously, temporary mismatches may appear. For instance, a transaction and its settlement record might not be written at the same time, leading to a mismatch that resolves itself later. That kind of uncertainty makes it harder to generate accurate reports in real-time.

Performance and Scale

Big traffic days like Black Friday or sudden success from a social media campaign can cause traffic to surge 10 times higher than usual. That’s when the performance of your payment system is really put to the test.

Modern SQL databases are surprisingly capable of handling high write volumes. With good indexing and a well-optimized schema, they can process thousands of writes per second. But SQL databases tend to scale vertically, which means you’re limited by the capacity of a single machine. Eventually, you may hit hardware limits.

NoSQL databases scale horizontally. You can distribute the load across multiple machines, which makes them better suited to handle unpredictable spikes in traffic. If your product needs to respond quickly to changing demand, this can be a major advantage.

Security and Compliance Considerations

Fintech products are subject to strict regulatory standards. PCI DSS, GDPR, and other frameworks shape how you store, transmit, and access customer data.

SQL databases offer mature features that make it easier to stay compliant. These include:

• Row-level security for multi-tenant systems
• Encryption at rest and in transit
• Built-in audit logging for accountability
• Role-based access controls for managing user permissions
  

NoSQL databases have undergone significant security improvements in recent years. MongoDB, for instance, supports field-level encryption. AWS DynamoDB uses IAM policies for fine-grained access control. Still, the same flexibility that makes NoSQL attractive can also introduce risks. Sensitive data can get buried inside deeply nested fields, and the lack of strict schemas may lead to uneven security coverage across different types of records. If you’re not careful, this could result in exposed data or inconsistent policy enforcement.

With the key points for selecting database systems in mind, there are a few important questions you should ask yourself to help streamline your decision. Let’s explore those next.

Making Your Decision: Questions to Ask

Below are some questions to ask to help you align your choice with your technical and business needs:

• Volume and Growth Patterns: Are you handling thousands or millions of transactions per day? How fast is your volume increasing, and can you predict spikes or traffic patterns?
• Reconciliation Complexity: How often do you need to match transactions with external systems? Are your reconciliation processes simple, or do they require complex, multi-way matching?
• Compliance Requirements: What regulations apply to your payment data? Do you need audit trails, data residency controls, or encryption at rest and in transit?
• Development Team Expertise: Does your team have more experience with SQL or NoSQL databases? Often, the best database is the one your team can use confidently and maintain reliably.
• Integration Ecosystem: What external systems will interact with your payment data? SQL databases typically offer stronger compatibility with analytics tools, financial software, and reporting platforms.

In some cases, a hybrid approach might give you the best of both worlds. You can store core transaction data in SQL and use NoSQL to store metadata like device fingerprints, authorization info, or fraud signals.

Wrapping Up

Choosing the wrong type of database won’t make your transactions disappear, but it can make them hard to use. Poor performance, inconsistent data, or a painful reconciliation process can all result from using a tool that doesn’t fit your actual needs.

Start by understanding your data patterns, compliance obligations, and growth trajectory. Let your requirements guide the choice, not just trends or what feels flexible.

At the end of the day, the right database isn’t SQL or NoSQL. It’s the one that works for what you’re actually building.