10 examples of databases that will support the growth of your IS

Too often, databases are seen as a purely technical subject, reserved for IT experts. They are seen as a basic infrastructure that has to work, full stop.

This is a mistake, because the volumes of data held by businesses are growing exponentially: customer data, product data, transactions, logs, connected objects, social networks, etc. The ability to exploit this data effectively will become a key competitive advantage, and even a condition of survival for SMEs and SMIs.

Of course, not every SME is destined to become a 'data company'. But every company can and should make progress in exploiting its data, at its own pace and according to its own resources.

To help you achieve this, here are 10 examples of the most effective databases in 2025.

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What is a database?

In the broadest sense, a database is a structured collection of information that is managed by specific software called a database management system, or DBMS.

It is an absolutely central element in the information system of any company, whatever its size.

In practical terms, databases make it possible to store, organise and make effective use of all the company's vital data: customers, products, orders, employees, suppliers, stocks, production data, financial and accounting data, etc. They are at the heart of the applications and processes used by the company. They are at the heart of business applications and processes.

Without a database, it's impossible to manage your business electronically! Here are a few examples of uses

• CRM (customer relationship management) is based on a customer database;
• ERP (enterprise resource planning) uses product, stock, purchasing and HR databases, etc. ;
• the e-commerce web shop uses a product catalogue and customer accounts;
• decision-making tools and dashboards draw on the company's data.

In addition to these 'traditional' uses, companies are now looking to leverage their data to make better decisions, personalise their offerings and optimise their operations. Each on its own scale. They are also faced with issues of regulatory compliance, particularly with the RGPD. A data leak can be very costly in terms of image and penalties!

In a nutshell: managing your databases well is both an operational imperative and a strategic issue (development and transformation). That's why the subject is so high on organisations' IT agendas.

What are the 4 types of database?

1. Relational databases (SQL)

Databases are said to be " relational " when data is stored in tables made up of rows (records) and columns (attributes).

Each table has a unique primary key. Foreign keys are used to link tables together in order to model relationships.

☝️ The SQL language has become the standard for relational databases.

Today, relational databases are the most mature. Oracle is the leader on the professional market with its advanced functions, followed by Microsoft's SQL Server. They are at the heart of critical business applications such as :

• management (ERP packages, CRM, finance, HR, purchasing, logistics and production apps) ;
• payment and reservation systems (banking, e-commerce, travel); and
• medical records, insurance, administration, specific business applications.

Despite their maturity, relational databases are showing their limitations when it comes to certain modern requirements: modelling complex data, distributed processing of large volumes, real-time queries, etc.

2. Document-oriented databases

Here, the focus is no longer on data, but on collections of documents. The databases are therefore tailored to manage semi-structured and heterogeneous data, following dynamic patterns, with greater flexibility and performance.

A document encapsulates data in key-value format, often in JSON, XML or BSON (binary JSON). It may contain fields of different types, lists or nested documents. Each document then has a unique identifier.

Concrete use cases for these document-oriented databases :

• articles in a content management system (CMS) ;
• product catalogues
• user profiles
• games in the form of web and mobile applications.

3. Column-oriented databases

These share common features with the relational model (tables, rows, columns), but have been adapted for Big Data and queries on immense volumes. The key: storage by columns rather than rows. Columns from the same family are stored contiguously together.

Column-based storage, coupled with compression, partitioning and data/query distribution mechanisms, enables a high level of scalability.

Google, with its Bigtable solution (and its free version HBase), and Facebook, with Cassandra, are the benchmarks in this field.

Here are the most important use cases:

• data warehouses ;
• distributed processing (MapReduce);
• the Internet of Things (IoT);
• log analysis.

4. Graph databases

These use graph theory to model entities (nodes) and the relationships (edges) between them. Typically, banks use graph databases to identify suspicious transaction patterns. For example, if a customer suddenly makes several transactions to newly created accounts, which in turn quickly transfer these funds to an account abroad, the relationships between the nodes trigger an alert.

With graph databases, you can efficiently explore complex, multi-level relationships that are difficult to model or query in traditional databases.

They have become central to :

• social networks (relationships between users)
• recommendation systems
• fraud detection;
• network analysis, with identity and access management.

10 examples of effective databases in 2025

Here's a clear overview of the 10 must-have solutions in 2025, with their strengths and weaknesses, to help you make an informed choice based on your real needs.

Oracle Database

This is one of the benchmark solutions for enterprise databases. Oracle Database offers reliability and security, making it the ideal choice for mission-critical applications. However, this excellence comes at a cost, which is not justified for businesses with less demanding needs.

👍 The benefits :

• Data always consistent: your sensitive information is protected by proven mechanisms, even in the most complex environments.
• Holds up under pressure: even during peak loads, Oracle maintains highly competitive response times. This is made possible by intelligent optimisations that you configure on demand.
• Never down (or almost never down): even when the hardware fails, the automatic replication and failover mechanisms ensure that your applications continue to work.
• A safe for your data: highly sophisticated encryption, auditing and access controls protect your sensitive data. In turn, this level of security simplifies your regulatory compliance.
• Uncompromising analysis: everything is executed directly in the database, from simple SQL queries to machine learning. No need for additional infrastructure.

👎 Disadvantages:

• Significant investment : it's no secret that Oracle licences and infrastructure represent a significant budget. It won't be justified for medium-sized organisations with no critical requirements.
• Requires experts: without qualified people to maintain it, you will only be able to exploit a fraction of the database's capabilities... at a high price.

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Microsoft SQL Server

SQL Server is the Swiss army knife of the Microsoft ecosystem.

If your company already uses Windows, Office and Azure, SQL Server will integrate naturally into your environment, with a minimum of friction.

👍 Benefits:

• Integrates seamlessly into the Microsoft ecosystem: communication is natural - authentication, reporting, cloud. No complex development required.
• Fast when you need it to be: innovative technologies speed up both rapid transactions and high-volume analyses, without separate systems.
• Always available: partitioning mechanisms ensure continuity, even during updates or incidents, limiting the impact on your business.
• In-depth protection: sophisticated encryption and access controls secure your data, without compromising performance.
• Intuitive even for beginners: Microsoft sets the standard for clear graphical interfaces and intelligent wizards to guide you. Even if you're not a database expert, you'll still have plenty to work with.

👎 Disadvantages:

• Pricing to be decoded: with Microsoft, take the time to fully understand what you're buying. There's a whole range of editions with limited functionality in the affordable versions.
• Long-term marriage: once integrated into your Microsoft infrastructure, a change of direction will become costly and technically complex.

Salesforce Data Cloud

Salesforce is the specialist in customer data.

If you're all about personalised customer experience and already use Salesforce, the CDP (Customer Data Platform) Data Cloud software will give you a significant competitive edge.

👍 Benefits:

• Centralisation of multi-source data: Salesforce Data Cloud collects and aggregates data from CRM, ERP, e-commerce, social networks and other sources.
• Real-time analysis: immediate processing and analysis of data flows, for instant decision-making and greater responsiveness.
• Advanced segmentation and personalisation: using AI, the solution creates dynamic profiles and enables ultra-targeted campaigns based on customer history and behaviour.
• Omnichannel campaign management: synchronising and coordinating marketing messages across all channels, for seamless communication.
• Friction-free integration with the digital ecosystem: Salesforce offers native connectors for tools such as Tableau, Slack, Google BigQuery or Snowflake, ensuring optimal interoperability.

👎 Disadvantages:

• Not suitable for small organisations: the functional coverage and pricing will be better suited to large companies and SMEs that are mature in the subject of databases.
• Gradual learning curve: mastering all the modules takes time and training. So plan this investment from the outset.

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MariaDB

This is the free, scalable alternative to MySQL.

For those who want to move away from proprietary systems, without sacrificing performance, MariaDB offers a smooth transition with a bonus of innovation.

👍 Benefits :

• Painless migration: your MySQL applications will work with MariaDB, without rewriting.
• Faster by default: optimisations naturally reduce response times on large volumes, without any complex configuration on your part.
• Security at no extra cost: solid protection is built into the standard version. No need to pay for additional modules.
• Rich functionality: advanced capabilities are included free of charge. This saves you the hassle of tinkering with expensive alternative solutions.
• Total transparency: development is open. So you understand where the product is going and can influence its evolution, unlike proprietary solutions.

👎 Disadvantages:

• Compatibility to be checked: some specific functions of recent MySQL may not work. Test your critical applications before migration.
• Growing community: there are fewer tools and experts available than for MySQL, although the gap is closing fast.

MongoDB

MongoDB has rapidly gained market share in the web and mobile worlds. It is currently the benchmark for document-oriented databases. Ideal for fast-moving applications that handle heterogeneous data!

👍 Benefits :

• Maximum flexibility: you store data with varied and evolving structures without restructuring the database. Perfect for projects that pivot frequently.
• Faster development: natural mapping to code objects eliminates complex translation layers and reduces time to market.
• Native speed: the document-centric approach avoids costly joins and drastically speeds up operations, even on millions of records.
• Frictionless growth: simply add servers as your needs grow. MongoDB automatically distributes the load without manual intervention.
• Robust ecosystem: professional training, support and tools mean you don't have to navigate blindly, especially for mission-critical deployments.

👎 Drawbacks:

• Not made for banking: support for multi-document transactions remains limited. Avoid for mission-critical financial applications.
• Thinking differently: designing effective data models requires a different approach to SQL databases. A period of adaptation is therefore required.

MySQL

For years, MySQL has been the Swiss army knife of the web.

Simple, proven and universally supported, this database is often the default choice for small to medium-sized websites and applications.

👍 The benefits :

• Simplicity first: installation in just a few minutes, highly intuitive administration. You'll be up and running straight away, even without advanced expertise.
• Fast to read: MySQL is particularly efficient for serving web content, which explains why most CMS have adopted it.
• Stable as a rock: decades of intensive use have eliminated most bugs. Your database won't let you down in production.
• Everywhere you are: it is natively supported by all web languages and frameworks, with abundant documentation and millions of trained developers.
• Low budget: it's free for most uses!

👎 Disadvantages:

• Glass ceiling: performance drops on very large volumes or complex queries. Plan for a possible migration in the event of strong growth.
• Not for intensive use: lock management is less sophisticated and can slow down applications with lots of simultaneous writes.

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PostgreSQL

This is a premium open source alternative when you need advanced functionality but can't invest in Oracle. PostgreSQL promises a comparable level of sophistication... without the prohibitive costs.

👍 The benefits :

• Power to spare: built-in enterprise features mean you don't have to buy expensive add-ons or develop workarounds.
• Future-proof code: strict compliance with SQL standards protects your development investments over the long term.
• Infinitely adaptable: its modular architecture means you can add exactly the capabilities your business needs. No overload.
• Native multi-functionality: built-in support for structured data, documents, text search and geolocation simplifies your architecture.
• Institutional security: the protection and integrity mechanisms are already adopted by the most demanding banks and administrations.

👎 Disadvantages:

• More technical: PostgreSQL requires more expertise than MySQL to be perfectly optimised.
• Priority to reliability: it is slightly slower on certain simple operations, prioritising data consistency over raw performance.

Redis

Redis is the champion of speed.

For projects that pay attention to every millisecond of processing time saved, this database offers first-rate performance. Example: for datas requiring instant access, such as caches and counters.

👍 Benefits:

• Ultra-fast: microsecond responses transform the user experience of your most time-sensitive applications.
• Tailored structures: specialised data types simplify the creation of functionalities such as rankings, queues or counters.
• Integrates anywhere: a simple API can be easily plugged into your existing system, without the need for a major overhaul.
• More than just a cache: advanced capabilities such as scripting, clustering and replication make this a much more versatile tool than it looks.
• Industrialised: professional support has an excellent reputation. Solutions hosted by all the major cloud providers make it easy to use in production.

👎 Disadvantages:

• Volatile by design: storage is mainly in memory, which can lead to data loss in the event of an unexpected reboot.
• Limited by RAM: you'll need to have enough memory to hold all the data. The cost can rise rapidly for large volumes.

SAP HANA

SAP HANA is an analytical gas factory designed for large companies that need to analyse huge volumes of data in real time.

The solution stands out for its speed and depth of analysis.

👍 Key benefits :

• All in memory: a revolutionary architecture eliminates the disk bottleneck and drastically speeds up all processing, even the most complex.
• Instant analyses: reports and dashboards run in real time on fresh data, without waiting for night-time extractions.
• Complete toolbox: take advantage of integrated machine learning and text mining capabilities. No need for additional solutions!
• Integrates with everything: extensive connectivity makes it easy to integrate with your existing ecosystem, whether SAP or not.
• Designed for extremes: the SAP architecture is capable of managing terabytes of data with constant responsiveness.

👎 Disadvantages:

• Major investment: the cost is very high in hardware, licences and expertise. It is therefore only justifiable for large organisations.
• Inherent complexity: a significant learning curve, even for experienced professionals. Serious support is required.

SQLite

It's a pocket database, a minimalist package.

If you need to store structured data locally, without a server or configuration, SQLite offers a surprisingly robust solution.

👍 Benefits:

• Extremely lightweight: it's a simple file that you can embed in any application without installation or server.
• Perfect for the edge: works directly on connected devices and objects, allowing data to be processed as close to its source as possible.
• Surprisingly robust: internal mechanisms protect your data, even in the event of a power failure or system crash.
• Fast locally: amazing performance for moderately-sized local data, often superior to more complex client-server solutions.
• Universally supported: the tool is natively integrated into most languages and systems, with a simple API.

👎 Disadvantages:

• Not for multi-users: SQLite is designed for sequential or limited access, not for hundreds of simultaneous connections.
• Limited by its simplicity: it is unsuitable for large databases (>10 GB) or distributed architectures that require more sophisticated solutions.

How do you choose your database? 5 criteria to consider

#1 The nature and volume of the data to be managed

• What is your data model? Structured, semi-structured, documents, graphs...
  👉 Example: for data on e-commerce product profiles, a document database such as MongoDB will be more suitable than a relational database.
• What kind of growth and variability? Linear, in stages, seasonal peaks in activity? MB, GB, TB?
  👉 Example: to handle peaks like Black Friday, Redis will be very effective as a cache in front of a main database.

#2 Processing

• What are the use cases? Heavy transactional, analytical, decision-making, real-time?
  👉 Example: for complex BI queries, a data warehouse will be more suitable than a MySQL database.

#3 Environment and security

• What is your application ecosystem? Languages, frameworks, ETL/BI tools, virtualisation, cloud?
  👉 Example: in a context with Microsoft applications, SQL Server will be easier to integrate than an open source database.
• What are the legal and security constraints? RGPD, regulated sector, encryption, auditability?
  👉 Example: in healthcare, a database like Oracle, which is HIPAA/RGPD certified, is essential.

#4 Maturity and longevity

• How old is the solution? Has it been adopted by the market? How dynamic is it?
  👉 Example: MongoDB has gone beyond its hype status to become a sure thing.
• How rich is the ecosystem? Libraries, integrations, tools, hosting providers?
  👉 Example: the MySQL/PostgreSQL ecosystem is currently the largest and most dynamic.

#5 Budget and skills

• What is the licence/subscription model? Its initial cost and TCO over 3 years? 👉 Example: SQL Server is free with the Express edition, but the Enterprise edition costs several thousand euros.
• What skills are available in-house? On the market? How much do they cost? What training is available?
  👉 Example: it's easier and cheaper to find PostgreSQL developers than SAP HANA experts.

FAQ: frequently asked questions about databases

How do you choose between relational and NoSQL?

The choice doesn't have to be made in these terms. It's not a question of pitting SQL against NoSQL. Most applications combine SQL for the transactional core and NoSQL for more specific needs. The key is to have the APIs and tools to manage this complementarity.

SQL remains essential for structured data and processing where consistency and integrity are paramount. Relational databases such as PostgreSQL are excellent for this.

NoSQL, on the other hand, provides flexibility and performance for large volumes of varied data (documents, graphs), often with less consistency. MongoDB is a benchmark.

Is it better to migrate to the cloud or stay on premise?

That depends on your context and your IT strategy. The cloud offers simplicity of administration and an attractive pay-as-you-go model, but raises questions about long-term costs, security and compliance.

For a start-up or a new project, a native cloud approach is often the best way to focus on the business.

For an industrial SME, a hybrid approach is generally preferable, keeping sensitive data in-house and using the cloud for occasional processing.

Should I opt for open source or vendor solutions?

Open source offers independence, a low initial cost and a rich ecosystem. Databases such as MySQL, PostgreSQL and MongoDB are de facto standards that most companies consider to be sufficiently robust. Open source is no longer synonymous with immaturity. If you have a motivated team of experts, open source will give you greater control and agility.

Vendor solutions offer additional guarantees, advanced features (security, encryption, availability), responsive support and sector-specific certifications.

Oracle and SQL Server are still the safe bet for business-critical applications.

What disruptions can we expect to see in databases over the next 3-5 years?

Over the next 3 to 5 years, several major developments will transform the database landscape:

• Artificial intelligence (AI) will become native to databases. Instead of exporting your data to separate AI tools, you will be able to query your data in natural language and obtain relevant answers.
  
  
• LLM-based interfaces will enable complex queries to be generated automatically, data to be explored intuitively and models to be programmed. All without the need for in-depth technical expertise.
  
  
• Serverless" databases will become widespread, so you won't have to scale your resources. The database will automatically adapt to your needs, going into standby mode when not in use and ramping up instantly during peaks in activity. This model will radically simplify administration and significantly reduce costs.

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Example databases: what to remember?

One final word. Priority should always be given to usage and business value. In other words, what really counts is how the database supports your business activity, over and above technical considerations (performance, scalability, modernity). A database should help solve real-life problems.

So avoid sterile technological debate. Rather than choosing a technology for its technical features or popularity, choose it for its suitability to your specific needs!