Serial Number Database Software

I think Supabase could improve a bit on the visualization of metrics, especially in how computing consumption is presented. It would also be necessary to have easier options to quickly clear cache and RAM during operation. These improvements would make the tool even more efficient in managing resources. Review collected by and hosted on G2.com.

Supabase offers me enormous flexibility to operate my backend and database easily. It facilitates the integration of my team, and its easy implementation and scaling are ideal for my software as a service needs. Review collected by and hosted on G2.com.

I find the learning curve to be challenging, especially around data modeling, partitioning, and cluster tuning. For new teams, it can be tough to understand data modeling and partitioning best practices and how to design tables around application access patterns. I think more practical examples, guided tutorials, and real-world production scenarios would make this easier. Review collected by and hosted on G2.com.

We use ScyllaDB to handle high-volume data ingestion and low-latency access with horizontal scalability. It's great for real-time events and time-series data, and it manages high throughput while maintaining consistent low latency, efficiently using CPU, memory, and storage. Review collected by and hosted on G2.com.

I find some limitations in support and integration with tools such as Flink. OceanBase introduces several data types and features that are not natively supported by MySQL. As a result, existing CDC tools and pipelines originally built around MySQL can be difficult to migrate and may require significant modifications. Also, the initial setup was somewhat complex. Review collected by and hosted on G2.com.

OceanBase provides better query performance for our large-scale data, handles transactional workloads effectively, and simplifies our architecture with its built-in vector capabilities, eliminating the need for a separate vector database. Review collected by and hosted on G2.com.

One area that could be improved is the learning curve for some of the more advanced AI and enterprise features. The platform is powerful, but new users may need additional documentation, onboarding, or guidance to take full advantage of its capabilities. Pricing can also be a consideration for smaller organizations or teams with limited budgets. Overall, I would like to see even more streamlined integrations, simpler configuration, and clearer pricing to make adoption easier. Review collected by and hosted on G2.com.

EDB Postgres AI Database helps solve the complexity of managing data, AI workloads, integrations, and enterprise database requirements across different platforms. By bringing PostgreSQL and AI capabilities together, it makes it easier to build and manage AI-powered applications while keeping data secure and accessible. This benefits me by reducing integration complexity, improving development and operational efficiency, and helping teams get faster value from their data without having to manage multiple disconnected systems. Review collected by and hosted on G2.com.

The user interface looks and feels like software from fifteen years ago; it is desperately crying out for a modern redesign. Performance also degrades quickly once the database file size grows, and the lack of native, seamless web or cloud integration is a massive pain point. If you want to share an Access database across a remote team today, you have to jump through hoops with SharePoint or virtual desktops, which feels incredibly outdated. Review collected by and hosted on G2.com.

It bridges the gap for departmental projects that have outgrown Excel but do not justify the massive cost and IT overhead of a full enterprise SQL Server infrastructure. It allows us to track inventories, internal workflows, and relational client data efficiently. Having automated reporting and strict data validation rules built right into the local office desktop saves us dozens of hours of manual cross-referencing every week. Review collected by and hosted on G2.com.

The Enterprise edition, in particular is very expensive per core, and the costs increase quickly as you scale. SQL Server also requires more RAM and CPU, and when parallel jobs are running it can lead to resource constraints. On top of that, the licensing model is complex and difficult to estimate upfront for a real project. Review collected by and hosted on G2.com.

Problem:

Downtime or data loss can severely disrupt business operations.

How SQL Server helps:

It offers Always On Availability Groups, along with backup, disaster recovery options, and failover mechanisms.

Benefit to me:

I can build systems that remain available and resilient, reducing downtime and supporting business continuity. Review collected by and hosted on G2.com.

One area that can require additional effort with MySQL is query optimization when working with larger datasets or queries involving multiple tables. In my development work with Spring Boot and Hibernate/JPA, I have occasionally needed to review generated SQL queries, check joins and indexes, and fine-tune queries when database operations affected API response time.

Another area is troubleshooting database-related issues across the application and database layers. When a query or transaction does not behave as expected, identifying whether the issue is in the application logic, Hibernate/JPA mapping, SQL query, or database configuration can take some investigation.

Overall, these are manageable with proper indexing, query optimization, monitoring, and database design, but having better built-in guidance or tooling for identifying inefficient queries and explaining performance bottlenecks could make the development experience easier. Review collected by and hosted on G2.com.

In my backend development work, MySQL provides the relational data layer for applications built with Java and Spring Boot. Before using MySQL as part of the application stack, managing persistent application data and maintaining relationships between different entities required more manual handling. MySQL gives us a structured way to store, retrieve, update, and maintain application data reliably.

I use MySQL along with Spring Boot and Hibernate/JPA, which allows the backend application to interact with the database through well-defined entities, repositories, queries, and mappings. This integration makes it easier to develop and maintain REST APIs while keeping database operations organized.

MySQL also helps with transaction handling and maintaining data consistency when multiple related database operations are performed. From a development and troubleshooting perspective, being able to inspect the underlying tables and SQL queries is particularly useful when debugging API or data-related issues.

Overall, MySQL simplifies the persistence layer of our backend applications and provides a dependable foundation for day-to-day Java/Spring development. Review collected by and hosted on G2.com.

Kintone gives us enough visual flexibility for internal applications, but it starts showing its limits when the experience needs to feel highly tailored. Fields, groups, tables, and spacing are easy enough to arrange, but precise control over labels, responsive behavior, layouts, or interactive components usually means adding JavaScript, CSS, or a plug-in. We ran into that while building an app for field technicians. The team wanted every piece of information visible on a single screen so they would not have to move through tabs or extra steps, and we tried to accommodate it. The form technically worked, but it became dense, slower to scan, and harder to use on smaller screens. We spent some time trying to improve it with CSS before accepting that the problem was not the styling. The structure itself was overloaded. Simplifying the form and showing only the information technicians needed at each stage produced a much better result than continuing to decorate a layout that had already become too complicated. Review collected by and hosted on G2.com.

A client integration required sandbox credentials, endpoints, technical contacts, API versions, test results, and several approvals before it could move into production. The issue was that all of this information was scattered across emails, support tickets, and a shared spreadsheet that two different people had already organized in completely different ways. We built an integrations app in Kintone and used Process Management to move each record through Discovery, Development, Client Testing, and Production Ready. Lookup pulled client data from our main app, and Related Records surfaced the incidents and deliverables tied to each integration. Our first version was too strict. We made so many fields mandatory that the team started entering placeholder values just to move a record forward. That technically satisfied the workflow, but it made the data less reliable. We adjusted the validation rules by status and added a JavaScript API customization that checked only the information required for each transition. Later, we connected the REST API to our deployment service so the release identifier and deployment result were written back to Kintone automatically. The platform didn’t remove the technical work, and it didn’t prevent us from designing a bad first version. What it changed was where that effort went. Instead of building and maintaining another internal application from scratch, we focused on the process, the validation logic, and the integrations that actually mattered. Engineering, QA, and operations ended up working from the same record, blockers became easier to spot, and we stopped asking the client for information that someone else on the team had already collected. Review collected by and hosted on G2.com.

It's a bit annoying and very time-consuming compared to the previous ERP systems. It's okay, but just very time-consuming. Review collected by and hosted on G2.com.

I use SAP HANA Cloud for supplier management, completing end-to-end transactions more efficiently. It reduces my transaction time from over an hour to just five to ten minutes per transaction. Review collected by and hosted on G2.com.

Nothing much but postgres do not have an ability to store the data entirely in ram for fast access which Mysql like database provides Review collected by and hosted on G2.com.

Postgres is a database that can support NoSQL, PG vector, PostGIS, and transactional data all together in one place. It helps me to go, search and write other connection code in my project. Review collected by and hosted on G2.com.

I find the cost and licensing complex. It's challenging to learn and run, Oracle Database is resource-heavy, and the developer experience feels outdated. The vendor lock-in is a concern as well. I think pricing could be simpler, and I would like to see a better cloud-native story with friendlier defaults. Review collected by and hosted on G2.com.

I use Oracle Database to run critical business apps, manage huge data securely, and ensure stability. It scales to terabytes, has built-in backup and disaster recovery, and supports real application clusters. Review collected by and hosted on G2.com.

Manual backups are locked to a higher tier. If you want that feature, you’ll need the plan that’s €22 + VAT per license/month (billed annually) instead of the starter at €11 + VAT per license/month (billed annually). It’s not a dealbreaker for me, but it’s worth knowing up front. Review collected by and hosted on G2.com.

Ninox is a time saver. After implementing my new management and accounting system, I am saving 2-3 hours each day on tasks that I used to run manually and are now automated thanks to databases created in Ninox. Review collected by and hosted on G2.com.

The biggest downside of Redis Cloud is the cost as you scale up. The managed service is convenient, but pricing can rise quickly as memory usage increases. I’d also like to see more flexible configuration options, along with stronger monitoring capabilities. Review collected by and hosted on G2.com.

Redis Cloud helps address performance bottlenecks in applications that need fast access to frequently requested data. By caching responses and session data in Redis, we can reduce database calls and improve response times, making the application faster and more scalable overall. Review collected by and hosted on G2.com.

Schema and partition key design require careful planning. Query flexibility is limited compared to relational databases. Review collected by and hosted on G2.com.

Amazon DynamoDB handles high traffic efficiently, offering scalable high-performance data storage for real-time apps, without database management. It features automatic scaling, high availability, low latency, minimal operational overhead, and seamless AWS integration, providing fast performance for distributed and event-driven applications. Review collected by and hosted on G2.com.

One area that could be improved is the pricing, especially as storage and resources scale. Some database configuration and monitoring options can also be a bit complex for new users. More cost optimization recommendations and a simpler management interface would make Google Cloud SQL even easier to use. Review collected by and hosted on G2.com.

I use Google Cloud SQL to manage data with ease, offering reliable performance, automated backups, and high availability. It simplifies administration by handling infrastructure, letting us focus on development. Scaling is seamless, saving us time and effort. Review collected by and hosted on G2.com.

The observability story still feels incomplete out of the box. Getting meaningful metrics without plugging in external tools like Prometheus takes more effort than it should. Also, the error messages during misconfigured cluster setups can be cryptic - a bit more guidance there would save a lot of debugging time. Documentation has improved, but advanced topics like PITR and xCluster replication still feel like they assume too much prior knowledge. Review collected by and hosted on G2.com.

YugabyteDB is solving the problem of needing a database that can handle large amounts of data and many users without slowing down. It gives the power of traditional SQL databases with the scalability of NoSQL systems. For me, as someone new to the software field, it helped me understand how modern applications manage data across different regions and servers while staying fast and reliable. It’s also helping me learn how real-world systems are built — like how companies make sure their apps don’t go down even if one part of the system fails. It’s been a great learning tool and has given me confidence to explore more advanced database concepts. Review collected by and hosted on G2.com.

VAS can increase as workload scales, especially for high-performance instances and storage. I've also found out that certain low-level database optimization and customization are limited compared to self-managed databases. Review collected by and hosted on G2.com.

Amazon RDS removes the operational burden of managing databases by handling backups, scaling, and high availability. It automates tasks like patching and failover, improving reliability and allowing us to focus on application and data work instead of management. Review collected by and hosted on G2.com.

Proper cache invalidation and eviction strategies require careful planning. Applications also need to be designed to gracefully handle cache misses or node failovers. Review collected by and hosted on G2.com.

ElastiCache reduced database bottlenecks by serving frequently accessed data directly from memory.

Example: Product catalog data, user sessions, authentication tokens, and API responses were cached in Redis, reducing database queries by more than half while lowering application response times from several hundred milliseconds to well under 100 milliseconds during peak traffic. Review collected by and hosted on G2.com.

It can get expensive if we don't monitor usage closely. Review collected by and hosted on G2.com.

I use Azure SQL Database to store and query application data without infrastructure worries. It saves me time by handling backups, servers, and patching automatically, allowing easy scaling and integration with Microsoft tools. Review collected by and hosted on G2.com.

Costs can increase significantly for high scale production workloads. It is quite expensive for high scale production workloads, especially with replicas and storage growth. Troubleshooting performance bottlenecks in complex environments can also require deeper AWS expertise. Review collected by and hosted on G2.com.

Amazon Aurora solves the challenge of running high-level and scalable relational databases without manually managing backups, failover, and infrastructure maintenance. Review collected by and hosted on G2.com.