Best Database Management Options for 2025

Every time your customers check their account balance, track an order, or stream content, a database management system (DBMS) is working behind the scenes to store, sort, and serve data in real-time. In 2024, relational databases led the pack, making up 61.74% of total DBMS revenue worldwide. 

However, choosing thebest data base solution today involves more than just picking the most popular option. With various types and tools available, the decision can be overwhelming.

This guide walks you through the top database categories, tools, and selection tips so that you can pick the right fit for your app, your data, and your team.

• The best data base depends on your app’s structure, growth plans, and team skills, not just features.
• Relational, NoSQL, NewSQL, and Serverless DBMS options each serve different needs.
• Use case, scale, team familiarity, and non-negotiables should guide your decision.
• Codewave can help you choose, set up, and scale the right DBMS for your product from day one.

DBMS Categories and the Best Picks Under Each

You’re building an app and things are going well. Until you hit the part where data starts piling up. Users are signing up, orders are flying in, and suddenly your app feels slower. Maybe it’s crashing when traffic spikes. Or your team is spending hours writing workarounds just to get basic queries to run right. That’s usually a sign you didn’t pick the right database from the start.

At Codewave, we help you sort through the noise and pick the one that actually fits your app, so you can build faster, scale smarter, and stay focused on what matters.

Below is a clear breakdown of DBMS categories and the best data base options under each. 

Relational (RDBMS)

If your app depends on structured data, clear relationships, and reliable transactions, relational databases are still your safest bet. They’ve been around for decades for a reason. 

Here are some of the most trusted RDBMS tools you can count on:

1. PostgreSQL

PostgreSQL is a powerful, open-source RDBMS known for its reliability, extensibility, and standards compliance.  

• Ideal For: Developers who need SQL + NoSQL support, and teams building complex applications with strong data integrity
• ACID Compliance: Yes
• Query Language: SQL (with support for JSON, XML, full-text search, and custom functions)
• Cloud Support: Available via AWS RDS, Azure, Google Cloud SQL, Heroku, and more
• Pros and Cons 

Pros	Cons
Free and open-source	Can be slower than NoSQL for certain workloads
Highly extensible (PostGIS, TimescaleDB)	Manual tuning required at scale
Strong community and documentation	Slightly steeper learning curve for newcomers

2. MySQL / MariaDB

MySQL (and its community-driven fork MariaDB) is a fast, lightweight RDBMS that’s long been a go-to choice for web applications and CMS platforms.

• Ideal For: Web developers, small-to-medium apps, WordPress, and LAMP stack projects
• ACID Compliance: Yes (with InnoDB engine)
• Query Language: SQL
• Cloud Support: AWS RDS, Google Cloud SQL, Azure, DigitalOcean Managed DB, etc.
• Pros and Cons

Pros	Cons
Easy to set up and manage	Fewer features than PostgreSQL for complex workloads
Fast read performance	Not ideal for very large datasets or high write loads
Broad community and hosting support	Complex joins and subqueries can be less efficient

3. SQL Server

Microsoft SQL Server is a feature-rich, enterprise-grade RDBMS designed for transactional systems, reporting, and business intelligence, especially in Windows environments.

• Ideal For: Enterprise apps, BI and analytics-heavy platforms, Windows-based environments
• ACID Compliance: Yes
• Query Language: T-SQL (Transact-SQL)
• Cloud Support: Fully supported on Microsoft Azure SQL Database, also deployable on AWS and GCP
• Pros and Cons

Pros	Cons
Strong integration with Microsoft tools	Licensing can be expensive
Rich BI and reporting features	Heavier system requirements
Reliable performance and security	Limited flexibility outside Windows stack

4. Oracle DB

Oracle Database is a high-performance RDBMS built for large-scale enterprise applications that need speed, security, and advanced analytics.

• Ideal For: Mission-critical enterprise systems, large-scale transactional apps, high-security industries
• ACID Compliance: Yes
• Query Language: SQL with PL/SQL support
• Cloud Support: Oracle Cloud Infrastructure (OCI), plus support for AWS and Azure deployments
• Pros and Cons

Pros	Cons
Extremely stable at high volumes	Complex licensing and high cost
Strong in analytics, clustering, and replication	Steep learning curve
Advanced performance tuning capabilities	Overkill for smaller apps or teams

Can’t get clear answers from messy, scattered data? Check out Codewave’s Data Analytics Development services and let us build dashboards and reports that bring clarity.

NoSQL

When your data doesn’t fit neatly into rows and columns, or when speed and flexibility matter more than strict structure, that’s where NoSQL steps in. It’s built for scale, for change, and for handling messy, semi-structured, or even unstructured data without breaking a sweat. 

Below are the most widely used types, along with the best data base options under each.

1. MongoDB 

MongoDB is a document-based NoSQL database that stores data in flexible, JSON-like documents. It’s schema-less, which makes it ideal for apps that evolve quickly or deal with varied data formats.

• Ideal For: Developers building modern web/mobile apps, content-heavy platforms, or fast-changing data models
• ACID Compliance: Yes (at the document level)
• Query Language: MongoDB Query Language (MQL), also supports aggregation pipelines
• Cloud Support: Fully managed with MongoDB Atlas (AWS, Azure, GCP), or self-hosted
• Pros and Cons

Pros	Cons
Flexible schema—easy to update on the fly	Less efficient for highly relational data
Great for rapid prototyping	Write-heavy workloads may need tuning
Rich query and indexing support	Document size limit (16MB) can be restrictive

2. Couchbase 

Couchbase combines document storage with key-value performance and SQL-like querying through N1QL. It’s known for its offline sync and mobile database capabilities.

• Ideal For: Mobile-first apps, distributed systems, or projects needing both key-value speed and document flexibility
• ACID Compliance: Yes (on single-document operations)
• Query Language: N1QL (SQL-like syntax for JSON)
• Cloud Support: Couchbase Capella (managed DBaaS), self-hosted, edge deployment supported
• Pros and Cons

Pros	Cons
Blends document and key-value storage	Smaller developer ecosystem compared to MongoDB
Built-in mobile sync via Couchbase Lite	Steeper learning curve for N1QL
Fast, memory-first architecture	Costly at enterprise scale

3. Redis  

Redis is a lightning-fast, in-memory key-value store often used for caching, pub/sub systems, and ephemeral session data. It supports data structures like hashes, lists, and sorted sets.

• Ideal For: Caching layers, leaderboard systems, messaging queues, real-time analytics
• ACID Compliance: No
• Query Language: Redis Commands (not SQL or document queries)
• Cloud Support: Available via Redis Enterprise Cloud, AWS ElastiCache, Azure Cache for Redis
• Pros and Cons

Pros	Cons
Extremely fast in-memory performance	Not suitable for long-term storage
Simple and lightweight	Lacks complex query capabilities
Great support for pub/sub, TTLs	Limited data persistence without tweaks

4. DynamoDB  

DynamoDB is Amazon’s serverless NoSQL database that supports key-value and document data models. It’s built for massive scale with minimal ops overhead.

• Ideal For: Serverless apps, high-traffic workloads, globally distributed systems
• ACID Compliance: Yes (with transactional API)
• Query Language: PartiQL (SQL-compatible)
• Cloud Support: AWS-only (fully managed, no self-hosted option)
• Pros and Cons

Pros	Cons
Auto-scaling and serverless	Locked into AWS ecosystem
Built-in replication and backup	Can get expensive with high read/write units
Supports both key-value and document	Limited indexing and joins

NewSQL

You want the reliability of SQL but the scale and flexibility of NoSQL. That’s where NewSQL steps in. It’s built for teams that need strong consistency, familiar relational models, and the ability to grow without rewriting everything later. 

 If you’re building a fast-growing app that needs to stay consistent across regions, these are the best data base options under the NewSQL category worth exploring:

1. CockroachDB

CockroachDB is a distributed SQL database designed for high availability and global scalability. It automatically replicates, balances, and recovers data across nodes, making it ideal for apps that can’t afford downtime.

• Ideal For: Transaction-heavy apps, SaaS platforms, and global applications that need built-in failover
• ACID Compliance: Yes (full transactional support)
• Query Language: Standard SQL
• Cloud Support: CockroachDB Cloud (fully managed), self-hosted, Kubernetes-ready
• Pros and Cons:

Pros	Cons
High fault tolerance and self-healing	Can be complex to fine-tune for performance
Strong consistency across distributed nodes	Higher latency in multi-region writes
Compatible with PostgreSQL tools	Learning curve for ops teams

2. YugabyteDB

YugabyteDB offers a PostgreSQL-compatible SQL API on top of a distributed storage engine. It supports high-throughput transactional workloads and is built to handle both OLTP and distributed workloads efficiently.

• Ideal For: Apps needing horizontal scale with strong consistency—like fintech platforms or multi-tenant SaaS
• ACID Compliance: Yes
• Query Language: SQL (PostgreSQL-compatible), plus YCQL and YSQL APIs
• Cloud Support: Self-hosted, Kubernetes, and YugabyteDB Managed (cloud offering)
• Pros and Cons: 

Pros	Cons
Multi-API support for flexibility	Still maturing compared to PostgreSQL
Scales horizontally with no downtime	Limited third-party integrations
Built-in global distribution features	Needs careful config for write-heavy loads

3. Google Cloud Spanner

Cloud Spanner is Google’s fully managed, globally distributed SQL database. It combines the scalability of NoSQL with the transactional guarantees of traditional RDBMSs, offered as a service with minimal ops.

• Ideal For: Enterprises building globally distributed apps that require strong consistency and automatic scaling
• ACID Compliance: Yes (full transactional support with external consistency)
• Query Language: SQL (Spanner SQL dialect)
• Cloud Support: Available only on Google Cloud Platform
• Pros and Cons:

Pros	Cons
Fully managed with seamless global scale	GCP-only—no self-hosted version
Automatic sharding and scaling	Higher entry-level pricing
Excellent uptime and availability	Less flexibility for custom tuning

Cloud-Native / Serverless

If you don’t want to worry about provisioning, scaling, or patching your database, cloud-native and serverless options take that load off your shoulders. These databases are built to grow with your app, handle traffic spikes without breaking, and bill you only for what you use. 

 Here are some of the best data base tools in this category worth checking out:

1. Amazon Aurora Serverless

Aurora Serverless is a scalable, on-demand version of Amazon’s cloud-native relational database. It automatically adjusts capacity based on your app’s load, so you only pay for what you use.

• Ideal For: Apps with unpredictable or bursty traffic, startups that need cost-efficient scale
• ACID Compliance: Yes
• Query Language: SQL (MySQL- and PostgreSQL-compatible versions)
• Cloud Support: AWS-only (fully managed by Amazon RDS)
• Pros and Cons: 

Pros	Cons
Auto-scaling without downtime	Cold starts can affect performance
Compatible with MySQL and PostgreSQL tools	Locked into AWS ecosystem
Built-in replication, backups, and failover	Limited tuning compared to standard Aurora

2. PlanetScale

PlanetScale is a serverless MySQL-compatible database built on Vitess (used by YouTube). It’s designed for developers who want zero-downtime schema changes, instant branching, and massive scalability without managing shards.

• Ideal For: Developers building scalable web apps with CI/CD workflows, especially in fast-paced dev teams
• ACID Compliance: Yes
• Query Language: SQL (MySQL-compatible)
• Cloud Support: Fully managed, hosted by PlanetScale on cloud infrastructure (no self-hosted version)
• Pros and Cons: 

Pros	Cons
Schema changes without downtime	Lacks some advanced MySQL features (e.g., triggers)
Git-style branching for development	Locked into PlanetScale’s platform
Great for fast-moving teams and large scale	Not ideal for complex, deeply relational models

3. Google Firestore

Firestore is a fully managed, serverless NoSQL database from Google Cloud, designed for real-time apps. It stores data in flexible documents and collections, and syncs changes across devices in milliseconds.

• Ideal For: Real-time mobile/web apps, chat systems, offline-first apps
• ACID Compliance: Yes (document-level)
• Query Language: Firestore query syntax (NoSQL-style)
• Cloud Support: Google Cloud Platform only
• Pros and Cons: 

Pros	Cons
Real-time syncing and offline support	Complex queries and joins are limited
Easy integration with Firebase SDKs	Pricing can spike with heavy reads/writes
Great for mobile and multi-user apps	GCP-only; vendor lock-in

Manual processes slowing down your decision-making? Explore Codewave’s AI/ML Development services and let us build models that automate data handling, spot trends, and accelerate insights.

Once you’ve seen what each type offers, it’s time to figure out which one actually suits your project.  

Choosing the Right DBMS for Your Project

You’re running a fast-growing ecommerce site. At first, everything works fine. Then your catalog grows, customer activity spikes, and suddenly product searches slow down or break. Your team patches things with caching and quick fixes, but the core issue lingers, your database can’t keep up with the way your data behaves. 

Choosing the best data base starts with knowing what your app actually demands, not just now, but six months from now. 

Here’s how to make that call with clarity.

1. Start with Your Use Case

Don’t start by comparing features. Start with your app. A chat app with high-speed messaging needs a very different setup than a slow-growing marketplace. Thinking this through early saves you from major rewrites down the road.

Focus on:

• What kind of app is it?
• How much traffic are you expecting?
• Is your data structured, semi-structured, or all over the place?
• Do you care more about fast reads, safe writes, or real-time sync?

Here’s a quick cheat sheet:

App Type	Best Fit DBMS
E-commerce backend	PostgreSQL, MySQL, CockroachDB
IoT sensor data	InfluxDB, TimescaleDB
AI/ML app backend	MongoDB, Fauna, Pinecone
Real-time chat	Redis, DynamoDB
Complex relationships	Neo4j, Amazon Neptune

2. Define Your Non-Negotiables

It’s easy to get distracted by benchmarks and shiny features. But what really matters is what you can’t compromise on. Maybe you need SQL support because your team already knows it. Maybe cost is a blocker. Maybe you can’t risk being stuck in a single cloud ecosystem.  

Checklist:

• SQL support: yes or no?
• Is global availability a must?
• Open-source or fine with managed?
• Is vendor lock-in a dealbreaker?
• What’s your hard budget?

3. Consider the Team’s Skillset

You don’t want your devs learning a new query language in the middle of a product sprint. The best database is the one your team can actually use well. If you don’t have a DevOps team, a fully-managed option might be the only way to go. 

If your backend engineers live in SQL, don’t throw them into a NoSQL setup unless you really need to.

Think about:

• What languages and tools does your team already know
• Whether you can afford to manage infrastructure
• How much support you’ll need during rollout

4. Think About Scale 

Your app might not need to scale on day one. But what happens if it takes off next quarter? Some databases crumble when hit with unexpected traffic, others handle scale effortlessly.  

Ask yourself:

• How fast is your data growing?
• Do you need to support multi-region traffic?
• Do you need horizontal scaling from the start?

5. Evaluate Ecosystem and Support

No tool works in isolation. Your database should work well with the stack you’ve already chosen, and it should have a support system you can rely on when things break.  

Look for:

• Good docs and active developer forums
• Compatibility with your current tools
• Third-party tools or libraries you may need later

6. Security & Compliance Requirements

Some projects can’t afford loose ends. If you’re dealing with personal data, financial records, or anything sensitive, your DBMS needs to meet a higher standard.  

Check for:

• ACID support for safe transactions
• Built-in encryption (at rest and in transit)
• Compliance options for HIPAA, GDPR, PCI-DSS, or whatever your industry requires

Start small. Test with sample data. Try the free tiers. Benchmark a few options before you lock one in. The best data base is the one your app and team can work with, now and six months from now.

Off-the-shelf tools not fitting your workflow? Check out Codewave’s Custom Software Development services, we build solutions tailored to how your business actually runs.

Once you’ve got a handle on the basics, you can look into query tuning, caching layers, or using vector databases for AI use cases.

Why Choose Codewave for Database Management Solutions?

Choosing the best data base isn’t just about picking the right tool, it’s about setting up the right foundation for your product to grow.  At Codewave, we design end-to-end data architectures that match your app’s pace, scale, and complexity.

From relational systems to NoSQL, serverless, and distributed setups, we help you choose wisely, set it up right, and keep it running smoothly. 

Want to see how we’ve helped teams like yours solve real data challenges? Check out our portfolio and see what’s possible when you build with clarity from day one.

What you get with Codewave’s Database Management Services: 

• 60% faster setup of the right database architecture for your app
• 3x quicker deployment, so your product moves from dev to production without delays
• Save up to 3 weeks a month by automating backups, scaling, and performance tuning
• 25% lower development costs through smart database design and AI-assisted query optimization

Our services include:

• Data Strategy Consultation: We help you assess your current data flow, understand your app’s needs, and design a roadmap that scales with your growth, without overcomplicating things.
• Database Architecture & Development: From choosing the best data base to setting up schema, replication, and backups, we handle the full setup, tuned for performance and reliability.
• AI-Powered Data Solutions: We bring AI into the mix to optimize query performance, enable real-time analytics, and help your systems learn from usage patterns.
• Dashboards & Reporting Tools: We build clean, interactive dashboards that turn your raw data into insights your team can act on, fast. No SQL skills needed.

Curious to see what your data is really capable of? Book a free demo with Codewave’s experts and discover how we can turn your data into real results.

FAQs 

Q. What’s the difference between relational and NoSQL databases? 

A. Relational databases (like MySQL and PostgreSQL) store data in tables with predefined schemas, making them ideal for structured data and transactions. 

NoSQL databases (like MongoDB and Cassandra) store unstructured data in flexible formats like key-value pairs, documents, or graphs, offering scalability and high performance for big data applications and real-time analytics.

Q. How do cloud-based databases compare to traditional on-premises databases?

A. Cloud-based databases like Amazon RDS and Google Cloud Firestore offer benefits such as easy scalability, reduced hardware costs, and automatic updates. They allow businesses to focus on development rather than infrastructure management. Traditional on-premises databases provide more control but come with higher setup and maintenance costs, making them less flexible for rapidly growing businesses.

Q. How important is database security in 2025?

A. Database security is critical as businesses handle more sensitive data. With increased cyber threats, ensuring data privacy and integrity is a top priority. Look for databases with built-in encryption, role-based access control, and audit logging. Cloud databases often provide enhanced security features, but you still need to ensure compliance with regulations like GDPR or CCPA. 

Q. Can AI and machine learning improve database management?

A. Yes, AI and machine learning can significantly enhance database management by automating routine tasks, detecting anomalies, and optimizing queries. For example, predictive analytics can forecast data growth, while machine learning models can automatically optimize database indexing and query performance, reducing manual intervention and improving overall efficiency.

Q. How can emerging database technologies impact business operations in 2025?

A. Emerging database technologies, such as distributed databases and in-memory databases, can significantly boost business operations in 2025 by offering faster data access, enhanced real-time processing, and better handling of large datasets. These technologies reduce latency and improve decision-making speeds, particularly for businesses dealing with high-volume transactions or complex data processing.