Database Software Resources

Articles, Glossary Terms, and Discussions to expand your knowledge on Database Software

Resource pages are designed to give you a cross-section of information we have on specific categories. You'll find articles from our experts, feature definitions, and discussions from users like you.

Contents

Database Software Articles

What Is Software-defined Storage? Benefits and Best Solutions

Enterprise IT teams using hardware-bound IT infrastructure connect data storage operations to specific devices.

by Sudipto Paul

The Case for Multicloud Infrastructure Adoption

Putting all your eggs in one basket doesn’t always work out well. We’ve all done it, and when done in a business context (e.g., Cloudflare’s July outage that briefly tanked numerous web-based businesses), the consequences can be far-reaching and dire.

by Zack Busch

Database Software Glossary Terms

Hyperconverged Storage

Hyperconverged storage is an element of hyperconverged infrastructure. Discover how hyperconvergence works and helps organizations scale.

by Alyssa Towns

Desktop Database

A desktop database stores and manages data on a personal computer. Learn more about its key features, benefits, and when to choose one for your business.

by Sagar Joshi

eDiscovery

Electronic discovery, or eDiscovery, is collecting, reviewing, and producing data as a part of a legal investigation. Learn about its types and benefits.

by Sagar Joshi

Data Virtualization

Data virtualization gives users access to disparate data systems. Learn the use cases,best practices for success, and how it differs from data federation.

by Alyssa Towns

Database Software Discussions

What's the best relational database for software and IT companies based on user reviews?

Looking for input from G2 reviewers at software companies, SaaS teams, and IT services organisations in the Relational Databases category, specifically from those who have selected a relational database as the foundation for a production application and can speak to which platform held up as the codebase grew.

The platforms with the strongest software and IT company evidence:

PostgreSQL: The combination of SQL standards compliance, performance under complex workloads, and extensibility that allows teams to add capabilities through extensions rather than changing databases is described as the architecture choice that ages well as a software product grows.
MySQL: Software teams building standard CRUD applications, content management systems, and e-commerce backends credit the predictability and tooling ecosystem for keeping development velocity high without specialised database expertise.
Microsoft SQL Server: The SQL Server Management Studio tooling, the reporting and analytics integration with the Microsoft BI stack, and the compatibility with .NET application frameworks are credited for making development and database administration accessible without requiring specialised DBA expertise.
Amazon RDS: The ability to run MySQL, PostgreSQL, or SQL Server on RDS while AWS handles the operational overhead is credited for allowing small software teams to operate production databases without a dedicated database engineer.
SQLite: Software teams building mobile applications, desktop applications, and lightweight backend services credit SQLite for the development simplicity that eliminates the local database server setup step before coding can begin.

For software engineers who have chosen a relational database for a project that has now been in production for more than two years: what was the database-level behaviour that surprised you most as the data volume grew beyond what the initial design anticipated, and was the response a schema change, an indexing strategy, or a decision to evaluate a different database?

I’d pay close attention to query patterns that only become painful at scale. Growing data volume often exposes indexing, join, and schema decisions long before the database itself becomes the real bottleneck.

Answered: Preksha Buttan on September 17, 2026

Your answer

Which Database as a Service (DBaaS) platforms handle ultra-low latency for high-throughput workloads?

Latency requirements that look fine in a demo often fall apart under real production throughput. Here's what reviewers in the Database as a Service (DBaaS) category say holds up at scale:

Amazon DynamoDB: Reviewers cite consistent single-digit-millisecond performance even under heavy read and write volume, the most frequently cited option for this specific need.
Couchbase: Reviewers in high-throughput applications cite in-memory caching layered with persistent storage as the reason latency stays low under load.
Google Cloud BigTable: Reviewers cite strong throughput for very large datasets, though the learning curve is steeper than DynamoDB's according to several reviews.
Arango: A smaller review base, but reviewers cite multi-model flexibility without the latency tradeoff some multi-model databases carry.

At genuinely high throughput, has anyone hit a latency ceiling with any of these that only showed up once traffic passed a specific volume?

Oh Amazon DynamoDB is built explicitly for this, it's marketed and reviewed around single-digit millisecond latency at virtually unlimited scale, serverless, and it's the classic pick for high-throughput applications like gaming leaderboards, ad-tech bidding, or session storage. Amazon RDS is the other AWS-native option, but it's a traditional relational database as a service, so it's excellent for consistency and complex queries, not really optimized for the same ultra-low-latency, massive-throughput profile as DynamoDB. Couchbase is worth a look too, well-rated and known for combining low-latency key-value access with more flexible querying than DynamoDB offers. To be real, "ultra-low latency" for DynamoDB assumes simple key-based access patterns, if your workload needs complex joins or transactions across many tables, that changes the calculus toward a different engine. What's the access pattern, mostly simple key lookups, or complex relational queries at high volume?

Answered: Hasitha Varre on September 18, 2026

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What are the best Database as a Service (DBaaS) platforms for CEOs eliminating database maintenance?

What does "eliminating database maintenance" actually mean at the level a CEO cares about, not server patching, but not thinking about the database as a line item requiring headcount at all? That's the angle behind this look at the Database as a Service (DBaaS) category.

What we're hoping to find:

Fully managed provisioning, patching, and backups with no infrastructure team required
Predictable, usage-based pricing a CEO can forecast without engineering translation
Uptime and reliability track record strong enough that database management stops being a topic in leadership meetings
A migration path that doesn't require rebuilding the application layer
DigitalOcean: Reviewers consistently cite simplicity and predictable pricing as reasons smaller and mid-sized companies choose it over larger cloud providers.
Amazon Relational Database Service (RDS): Reviewers cite deep automation of maintenance tasks, though several note the pricing model takes more effort to forecast than DigitalOcean's.
SAP HANA Cloud: Reviewers in larger enterprises cite strong managed-service depth, though it's positioned toward SAP-centric organizations rather than a general-purpose pick.

For a company with no dedicated database administrator on staff, has one of these actually made that role unnecessary, or does someone still need to own the vendor relationship?

I don’t think managed infrastructure completely eliminates database ownership; it changes what someone owns. Patching and backups can disappear, while cost control, schema decisions, performance issues, and vendor escalation remain. The useful CEO-level test might be whether those responsibilities stay small enough to live with an existing engineering owner rather than eventually forcing the company to hire dedicated database expertise.

Answered: Shubham Gupta on September 16, 2026

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