July 25, 2025
by Sudipto Paul / July 25, 2025
Developing an app is exciting until your relational database starts breaking things you didn’t know could break.
At first, it’s the basics: race-condition overwrites, missing foreign keys, and rollback scripts after a bad deployment. You fix things, your app gains traction, and then the real trouble starts.
Simple queries slow to a crawl, migrations lock tables for minutes, and regional replicas start lagging.
Even if you muscle through that, there’s another wave of problem waiting for you. Your cloud bill spikes, compliance demands audit logs, and leadership wants multi-region availability without vendor lock-in. This is when you start searching for the best relational database tools for app development.
Using G2 reviews and feedback from developers who've lived through the chaos, this article spotlights the relational databases worth your trust.
*These are the top-rated relational databases for app development, according to G2 Grid Reports.
Amazon Relational Database Service (RDS) is AWS’s fully managed platform for MySQL, PostgreSQL, MariaDB, Oracle, SQL Server, and Aurora. It lifts the grind of provisioning, patching, backups, restoring with point-in-time recovery, and scaling so developers can ship features instead of babysitting servers.
More than 1,000 reviewers rate Amazon RDS an average of 4.5 out of 5 on G2. App developers love it for its scalability, ease of use, security, and backup services. G2 reviewers rate Amazon RDS’ backup capabilities 9.1 out 10, meaning you can focus on writing code instead of managing databases.
Amazon RDS users also appreciate its ability to scale compute and storage with a few clicks or a simple API call. Plus, multi‑AZ configurations promise automatic failover in case of disruptions, and read replicas meet read scalability needs. G2 reviewers rate it 9.1 and 9.2 out of 10 for scalability and availability, respectively.
That said, Amazon RDS can limit control over low‑level configurations and restricted operating system access. It offers pay‑as‑you‑go pricing that can be cost‑effective for smaller workloads. However, G2 reviewers warn that it may become expensive for high‑volume or continuously running environments.
Plus, some engines may have more extensions or replication options available, meaning developers must adapt their approach depending on the engine they use. Despite these drawbacks, G2 reviewers rate Amazon RDS 8.8 out of 10 across areas like data replication, application performance, and network security.
Amazon RDS remains a top relational database choice among developers. You’ll love its ease of use, reliability, and support as long as its managed approach aligns with your application's requirements.
App developers choose Amazon RDS because it automates database management tasks like backups, patching, and scaling, so they can focus on building features. It supports popular engines and simplifies high availability with multi-AZ and read replicas. G2 reviewers rate it for storage (9/10), encryption (8.8/10), and disaster recovery (8.7/10).
| Key strength | How Amazon RDS delivers it |
| Automated administration | Amazon RDS takes care of time-consuming tasks like provisioning, patching, backups, and restoring with point-in-time recovery. This automation reduces operational overhead and accelerates development cycles. |
| Scalability and high availability | Amazon RDS makes it easy to scale storage and compute resources with a few clicks or API calls. It also offers Multi-AZ deployments for automatic failover and read replicas for horizontal scaling. G2 reviewers also frequently cite stable uptime and performance under growing workloads. |
| Security and backups | Amazon Relational Database Service includes built-in encryption, isolated virtual private cloud deployments, automated snapshots, and configurable backup retention policies. These features help teams meet compliance needs without investing in separate tooling. G2 reviewers rate it 8.8 out of 10 for its atomicity, consistency, isolation, and durability (ACID) compliance. |
| Reliable data replication |
Whether using MySQL, PostgreSQL, or another supported engine, Amazon RDS offers native support for read replicas and cross-region replication. Developers use this for failover, geo-redundancy, and analytics offloading. |
1. How do you migrate an existing database to Amazon Relational Database Service?
To migrate an existing database to Amazon RDS, start by choosing your target engine (MySQL, PostgreSQL, etc.) and setting up a compatible RDS instance. AWS Database Migration Service is the preferred tool. It supports minimal downtime and works for homogeneous and heterogeneous migrations. Export your source database, upload the data to Amazon S3, or use DMS to stream it directly. Configure your target schema in RDS, initiate the migration task, and monitor it via the AWS Console. Once complete, update your app to point to the new RDS endpoint.
2. Is Amazon RDS better than managing your own database?
Amazon RDS is often better than managing your own database if you want to reduce operational overhead. It automates backups, patching, scaling, and failover — tasks that require manual setup and monitoring in self-managed environments. RDS also offers built-in security, high availability, and consistent performance. While it limits some low-level control, many developers prefer Amazon RDS for its reliability, time savings, and ease of use.
3. How do you set up Amazon Relational Database Service?
To set up Amazon RDS, log into the AWS Console and choose your preferred database engine. Define instance size, storage, and network settings, then enable features like Multi-AZ for high availability and automatic backups. Once launched, RDS provides an endpoint to connect your app. You can also manage setup via AWS CLI or infrastructure-as-code tools.
Amazon Aurora is a fully managed, MySQL-, DSQL-, and PostgreSQL-compatible relational database engine designed for performance-hungry, cloud-native applications. It’s ideal for development teams, DevOps engineers, and architects who want the scalability and reliability of commercial-grade databases without the overhead of provisioning or maintaining the underlying infrastructure.
Amazon Aurora is built for speed and high availability. It can deliver up to five times the throughput of MySQL and three times that of PostgreSQL. The database improves fault tolerance by automatically replicating six copies of your data across three Availability Zones. Aurora can scale storage automatically up to 128TB per database instance, with no downtime.
G2 reviewers rate Amazon Aurora’s scalability 9 out of 10. This relational database also earns high marks for backup (9.1 out of 10), application performance (8.9 out of 10), and provisioning (8.7 out of 10).
Amazon Aurora also seamlessly integrates with the AWS ecosystem. Developers benefit from tight integrations with services like Lambda for serverless operations, S3 for data lake connectivity, and CloudWatch for monitoring. The platform also supports read replicas and multi-master configurations, enabling high-read throughput and better write availability for globally distributed applications. G2 reviewers rate Amazon Aurora 8.9 out 10 for both data replication and disaster recovery.
Some reviewers on G2 point to cost complexity and limited control over some configurations as drawbacks. Others call out limited database customization. While Amazon Aurora removes much of the operational burden, that abstraction comes at the cost of fine-grained tuning.
Despite these caveats, Amazon Aurora strikes an appealing balance between performance, scalability, and simplicity, especially for teams already committed to AWS. Its near-instantaneous scalability and high fault tolerance make it ideal for cloud-native app development workloads that need reliability without constant monitoring.
App developers choose Amazon Aurora for its high performance, automated scaling, and built-in high availability. It’s MySQL and PostgreSQL compatibility simplifies migration, while features like serverless deployment, point-in-time recovery, and low-latency read replicas streamline testing, iteration, and scaling.
| Key strength | How Amazon Aurora delivers it |
| Blazing performance at scale | Amazon Aurora offers up to 5x the throughput of MySQL and 3x of PostgreSQL, while remaining fully compatible with both. It’s a distributed, purpose-built storage layer that scales automatically up to 128 TB, and compute is decoupled from storage for seamless scaling. G2 users frequently praise its speed under heavy workloads. |
| High availability and resilience | Amazon Aurora separates compute from storage and stores data redundantly across six copies in three Availability Zones. Automatic failover and quick recovery ensure minimal interruption. G2 reviewers rate it 9.2 out of 10 for its high availability. |
| Scaling and cloning for agile development | Amazon Aurora supports up to 15 low-latency read replicas and offers serverless and DSQL options for dynamic scaling. Fast database cloning and Aurora Backtrack let developers experiment and rollback in minutes, accelerating test cycles. |
| Backup and disaster recovery | Continuous, incremental backups to Amazon S3 run without impacting live operations, and point-in-time recovery makes restoring data simple and reliable. |
1. What are the limitations of Amazon Aurora?
Amazon Aurora delivers strong performance and scalability. However, reviewers on G2 note that costs can rise sharply with increased usage, and its deep integration with AWS services may limit flexibility. Some also report that tuning and configuration can be complex without prior AWS experience. That said, Amazon Aurora’s managed infrastructure and high availability remain valuable for teams prioritizing reliability at scale.
2. What is the difference between Amazon Aurora and Amazon RDS?
While both Amazon Aurora and Amazon RDS are managed services, Aurora offers higher availability and scalability, including automatic replication across multiple Availability Zones. In contrast, Amazon RDS supports a broader range of database engines but doesn't match Aurora's performance or recovery features.
3. Is Amazon Aurora good for high-traffic applications?
Yes, Amazon Aurora is highly suited for high-traffic applications. It automatically scales storage up to 128 TB and supports up to 15 low-latency read replicas. G2 reviewers frequently cite its strong performance under load, reliable failover, and seamless scaling as ideal for enterprise workloads and traffic-heavy environments, including gaming and financial services.
Microsoft SQL Server is a relational database platform aimed at enterprises, mid‑market firms, and seasoned developers looking for a scalable, secure, and feature-rich system for critical data workloads.
This database handles everything from analytics to data warehousing and heavy-duty business intelligence. It integrates deeply with tools like Power BI, Excel, SSRS, and Power Automate, making it a strong fit for organizations already using the Microsoft ecosystem. G2 reviewers rate it 8.7 out of 10 for scalability and 8.8 out of 10 for security, making it ideal for BI-heavy use cases.
G2 users also rate Microsoft SQL Server exceptionally high across fundamental database dimensions: Query language earns 9.3 out of 10, backup and recovery 9 out of 10, and stability 8.9 out of 10. Its performance analysis tools earn 8.9 out of 10, proving the database’s efficiency with monitoring and analyzing query performance, user sessions, and system errors.
Some reviewers, however, called out licensing costs as hefty, especially as you scale. However, many consider Microsoft SQL Server as a dependable choice with strong metrics across security, concurrency, access control, encryption, and disaster recovery. Others mentioned that smaller servers strain under the resource requirements, especially with more advanced workloads. A few users also flagged limitations in Linux support, and certain features like online indexing and big‑data clusters being locked behind Enterprise tiers.
If you're running at enterprise scale and need advanced security, high availability, and deep tooling, Microsoft SQL Server is a compelling, battle-tested platform. For leaner setups or tight budgets, the trade-offs around licensing and resource demands may nudge you toward lighter alternatives.
App developers choose Microsoft SQL Server for its strong performance, built-in development tools, and deep integration with the Microsoft ecosystem. It supports JSON, REST APIs, and works seamlessly with Visual Studio and .NET.
| Key strength | How Microsoft SQL Server delivers it |
| Performance and scalability | Microsoft SQL Server scales confidently across workloads, from small business databases to massive enterprise deployments. Features like in-memory OLTP and intelligent query processing ensure applications can handle high-concurrency environments without sacrificing speed or reliability. |
| Integration with the Microsoft ecosystem | Built to work natively with tools like Power BI, Excel, Azure Synapse, and Power Apps, Microsoft SQL Server helps teams simplify reporting, automation, and data visualization. G2 users rate it 7.9 out of 10 for Hadoop integration and 8.3 out of 10 for Spark integration. |
| Data security and access control | Microsoft SQL Server earns high marks for its encryption (8.9 out of 10), role-based access controls (9.1 out of 10), and robust authentication protocols (9 out of 10). Microsoft’s Always Encrypted technology, transparent data encryption, and dynamic data masking protect sensitive information at rest and in motion. |
| Powerful tools for developers and app builders | Microsoft SQL Server includes a rich suite of developer tools, from T-SQL enhancements to built-in support for JSON and REST APIs. Combined with SQL Server Management Studio (SSMS) and Visual Studio integration, developers can quickly model databases, write stored procedures, and optimize queries to build fast, stable applications with minimal friction. |
1. How does Microsoft SQL Server compare to MySQL?
Microsoft SQL Server is best for high-volume enterprise applications that demand scalability and robust reporting, while MySQL is better for web apps, startups, and projects where simplicity and cost-efficiency matter most.
Microsoft SQL Server offers more advanced features for enterprise-level applications, like built-in business intelligence tools, strong security protocols, and deeper integration with the Microsoft ecosystem. MySQL, while lightweight and open-source, can fall short in complex analytics and transaction-heavy workloads.
2. What are common Microsoft SQL Server errors, and how to fix them?
Frequent Microsoft SQL Server errors include:
Google Cloud SQL is a fully managed relational database service that supports MySQL, PostgreSQL, and SQL Server engines. It’s built for developers and organizations looking to offload routine database administration to the cloud.
Users find it super easy to configure Google Cloud SQL servers via the GCP console and praise features like IP whitelisting and security. settings. Users also highlight integration as a major advantage, syncing naturally with BigQuery, GKE, Firebase, and Compute Engine, making application development smoother. Google Cloud SQL also scores particularly high on core capabilities. G2 users rate it 9.2 out of 10 for backup, 9.1 out of 10 for data encryption, and 9.2 out of 10 for data replication.
The platform features automated maintenance, high availability SLAs up to 99.99%, optional data caching for read-heavy workloads, and instant storage scaling. The Enterprise Plus edition offers up to 4x faster read performance, 2x improved write latency, engine autotuning, and near-zero downtime during scaling or maintenance.
Some reviewers, however, mention that it can be expensive, especially when you scale and bring in additional integrated services. That said, Google Cloud SQL remains a top-tier choice if you’re looking for a managed relational database with best-in-class backup, security, and scalability.
App developers choose Google Cloud SQL for its fully managed setup, seamless integration with Google Cloud services, and support for popular relational databases like MySQL, PostgreSQL, and SQL Server. It simplifies operations with automated backups, scaling, and security, letting teams focus on building features instead of managing infrastructure. With high G2 scores for ease of use, scalability, and reliability, it’s a trusted choice for production-grade apps.
| Key strength | How Google Cloud SQL Server delivers it |
| Effortless setup and developer velocity | Google Cloud SQL is straightforward to spin up a MySQL, PostgreSQL, or SQL Server instance and hook it straight into your application stack. Developers rave about how the GCP console makes provisioning “super easy” and note that the UI feels seamless, especially when their app already uses Compute Engine, Cloud Run, GKE, or BigQuery. |
| Backup, recovery, and high availability | G2 users mention that backups are reliable and easy to use, and appreciate automated point-in-time recovery and multi-region failover. Google Cloud SQL further strengthens this by offering 99.99% SLA support and features like cascading replicas in the Enterprise Plus edition, ensuring low downtime and strong disaster recovery. |
| Rich querying power and data integrity | Cloud SQL provides powerful SQL capabilities, especially when used alongside Cloud SQL Insights and tools like BigQuery. The platform supports ACID-compliant transactions, complex indexing, stored procedures, PL/pgSQL, full-text search, and advanced analytics. G2 reviewers confirm it’s excellent at handling app-level queries and relational workloads. |
| Unified, fully managed service | Google Cloud SQL takes care of routine database operations — patching, backups, replication, container-level monitoring, and automated maintenance windows. Developers report meaningful time saved on routine ops with a clear dashboard for monitoring stats. |
1. What are the best practices for Google Cloud SQL security?
The best practices for Google Cloud SQL security include enabling SSL/TLS connections, using private IP for internal connectivity, and enforcing identity and access management (IAM) based access controls. It’s also recommended to enable automated backups, set up failover replicas, and regularly rotate database credentials. Encrypting data at rest and in transit is enabled by default, aligning with Google’s broader security architecture.
2. Can you migrate your existing database to Google Cloud SQL?
Yes, Google Cloud SQL supports database migration from on-prem or other cloud environments using tools like Database Migration Service (DMS), mysqldump, or pg_dump. These tools help transfer schema, data, and users while minimizing downtime. Cloud SQL supports MySQL, PostgreSQL, and SQL Server, making it flexible for most legacy migrations.
3. What are the best practices for using Google Cloud SQL?
Best practices for using Google Cloud SQL include sizing instances based on workload, enabling automatic storage increases, and using read replicas for scalability. Developers should also monitor performance using Cloud SQL Insights and schedule maintenance during low-traffic windows. Keeping database versions up to date ensures stability and access to the latest features.
4. Is Google Cloud SQL better than AWS RDS?
Google Cloud SQL is best for teams already using Google Cloud services and looking for tight integration with BigQuery, GKE, or Firebase. AWS RDS offers broader engine support and may suit users deeply embedded in the AWS ecosystem. Both platforms are reliable and fully managed. The better choice depends on which cloud platform your infrastructure is built on.
Oracle Database is designed to support massive transaction volumes, high concurrency, and complex data structures, making it ideal for sectors like finance, government, telecom, and others managing large-scale or regulated data workloads.
At its core, Oracle Database delivers unmatched reliability, scalability, and security. On G2, it holds a strong average rating of 4.35 from 950+ reviewers. Users frequently highlight its ability to handle high‑volume workloads and its built-in encryption, auditing, and fine‑grained access control. Oracle Database also earns high marks for stability (8.8/10), security (9/10), query language (8.9/10), and data manipulation (8.7/10). These numbers reflect real strengths in enterprise‑grade data integrity, governance, and performance.
Reviewers on G2 also love the flexibility. Users value the extensive tool kit, from RMAN backups to Data Guard and RAC, for disaster recovery and high availability. But no platform is flawless. Many reviewers point out that Oracle Database is one of the most expensive databases on the market. Another concern is that it can be resource-heavy, and finding some functions can be tricky.
Despite these disadvantages, there’s no doubt that Oracle Database offers top-tier scalability, security, governance, and high‑availability features, with strong rated scores in ACID compliance, replication, and query performance.
App developers choose Oracle Database because it offers unmatched scalability, robust security, and reliable performance for enterprise-grade applications. Its support for SQL, PL/SQL, and JSON makes it flexible for modern app development, while features like Real Application Clusters and Data Guard ensure high availability. Developers also appreciate its comprehensive toolset for managing backups, monitoring, and replication across cloud and on-prem environments.
| Key strength | How Oracle Database delivers it |
| Scalability for high‑volume workloads |
Oracle Database is built to shine under pressure, handling massive transaction volumes with consistent performance. Oracle’s Real Application Clusters (RAC), Data Guard, and Exadata technologies enable clusters of servers and seamless workload sharing. |
| Query engine and development support | When it comes to building apps, developers need a database that's responsive, flexible, and expressive. Oracle Database’s support for SQL, PL/SQL, JSON, and AI Vector Search gives devs a powerful palette. PL/SQL makes complex queries approachable, which means faster development cycles and cleaner, more maintainable app logic. |
| Comprehensive manageability across environments | Oracle promotes unified management, whether on-prem or in the cloud. Centralized management simplifies deployment, backup, and performance tuning, which is especially valuable for development teams managing multiple environments. |
| High availability and fault tolerance |
Downtime isn’t an option for many enterprise apps, and Oracle responds with a full suite: RAC, Data Guard, Active Data Guard, and globally distributed RAFT-based replication. |
1. How do you recover a corrupted Oracle Database?
To recover a corrupted Oracle Database, administrators typically use Oracle Recovery Manager (RMAN) to restore data from backups. If redo logs are intact, RMAN can perform point-in-time recovery to restore the database to a consistent state. Flashback Database can also be used for less severe corruption scenarios. Oracle’s Data Guard ensures high availability and can help switch to a standby database in critical cases. Recovery steps depend on the corruption type — physical or logical.
2. What are the best practices for Oracle Database security?
Best practices for Oracle Database security include enabling encryption for data at rest and in transit, implementing fine-grained access controls, and regularly auditing user activity. Oracle recommends using features like Database Vault to enforce separation of duties and Advanced Security for transparent data encryption. Keeping systems patched and using least-privilege access models are also essential. Multi-factor authentication and secure password policies further reduce risk. Regular security assessments help maintain compliance.
3. What are the system requirements for Oracle Database?
The system requirements for Oracle Database depend on the edition and deployment environment (on-premise or cloud). For Oracle Database 19c on Linux, minimum requirements include 2 GB RAM (ideally 8 GB or more), 10 GB of disk space for installation, and supported operating systems like Oracle Linux or Red Hat. CPU and memory need to scale with workload size. Oracle also provides hardware and OS certification matrices to ensure compatibility with enterprise infrastructure.
Other popular relational database tools for app development include:
Got more questions? We got you covered.
Choosing the right relational database for app development depends on your application’s scale, compliance needs, team expertise, and cloud environment.
Ultimately, the best choice balances ease of use with flexibility and long-term scalability.
Tech startups often choose Amazon Aurora or Google Cloud SQL due to their ease of setup, automatic scaling, and cloud-native performance. These tools allow developers to focus on building features instead of managing infrastructure.
Startups with smaller teams benefit from the automation of backups, monitoring, and patching. Amazon Aurora is particularly popular for performance-intensive apps, while Google Cloud SQL appeals to those already within the Google Cloud ecosystem. Both offer generous scalability without demanding deep database admin expertise upfront.
Amazon RDS is widely regarded as one of the most reliable relational database tools for new businesses. It automates critical tasks like patching, backups, and failover while supporting popular engines like MySQL and PostgreSQL. Its built-in security, multi-AZ deployment, and high G2 ratings for reliability and availability make it a dependable starting point.
Amazon Aurora, Oracle Database, and Microsoft SQL Server are top choices for handling large datasets in tech. Aurora offers up to 128 TB of auto-scaling storage and high throughput, while Oracle excels in transactional integrity and enterprise-grade performance. Microsoft SQL Server supports advanced analytics and business intelligence with robust indexing and query optimization tools.
Google Cloud SQL and Amazon Aurora are popular relational database tools for mobile app development due to their seamless integration with backend services and ability to scale. These tools provide high availability, point-in-time recovery, and strong performance under variable loads. Developers can use them alongside Firebase, Lambda, or Cloud Functions for reactive architectures. Google Cloud SQL’s simplicity and tight coupling with GCP make it a favorite among Android-first teams, while Aurora's low-latency replicas serve iOS and cross-platform needs well.
The best cloud-based relational database tools for app development include Amazon RDS, Google Cloud SQL, and Microsoft Azure SQL Database. These services offer fully managed hosting, automated backups, horizontal scaling, and high availability out of the box. They’re designed to integrate seamlessly with cloud-native development stacks, supporting fast deployment and continuous delivery pipelines.
Oracle Database is the leading choice for enterprise companies that require high performance, fault tolerance, and compliance at scale. It supports massive transactional workloads and offers advanced security features like fine-grained access control, transparent data encryption, and audit logging.
Relational database tools directly affect app performance by managing query speed, transaction consistency, replication lag, and scaling behavior. High-performing databases like Amazon Aurora or Microsoft SQL Server optimize memory, indexing, and query execution to reduce latency. Poorly tuned or self-hosted databases often become bottlenecks during peak usage or schema changes. Managed tools also ensure backups, failover, and monitoring don’t interfere with live traffic.
SQL databases use structured schemas and are ideal for complex queries, transactions, and relational integrity, making them perfect for finance, SaaS, and regulated apps. NoSQL databases, such as MongoDB or DynamoDB, prioritize flexibility and horizontal scalability, often favored for real-time analytics, IoT, or content-heavy platforms. Relational databases ensure strong ACID compliance, whereas NoSQL leans toward eventual consistency and faster writes. Developers choose SQL for precision and business rules, and NoSQL for rapid prototyping and high-volume unstructured data.
Amazon Aurora, Google Cloud Spanner, and Oracle Database are among the most scalable relational database tools for app development. Aurora supports auto-scaling storage and up to 15 low-latency read replicas, while Spanner offers global consistency with horizontal scaling. Oracle supports RAC and high-throughput clustering for mission-critical apps.
Relational databases can be your app’s secret weapon or its biggest liability.
As your app gains users, every overlooked detail, from replication lag to missing audit logs, can surface with costly consequences. That’s why choosing the right relational database tool remains the most strategic decision for developers.
Platforms like Amazon RDS, Aurora, Microsoft SQL Server, Google Cloud SQL, and Oracle Database exist to help you scale beyond the basics without losing sleep over rollbacks or replication errors. Backed by G2 reviews and real developer feedback, the tools spotlighted in this guide are built for high-performance, high-stakes environments.
Your app deserves a database that keeps up with your ambition, not one that holds you back when it matters most. Pick wisely, build confidently, and don’t wait for things to break.
Explore the best object-oriented databases you can use to store data models created by object-oriented programming languages.
Sudipto Paul is an SEO content manager at G2. He’s been in SaaS content marketing for over five years, focusing on growing organic traffic through smart, data-driven SEO strategies. He holds an MBA from Liverpool John Moores University. You can find him on LinkedIn and say hi!
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