MongoDB Enterprise Advanced and Azure Database for PostgreSQL are strong contenders in the database market. MongoDB Enterprise Advanced tends to be more favorable in pricing and support, while Azure Database for PostgreSQL is noted for its robust feature set and high user satisfaction.
Features: MongoDB Enterprise Advanced offers scalability without a predefined schema, JSON capability, and robust security with role-based access controls. Azure Database for PostgreSQL integrates seamlessly with Azure services, providing powerful security features and robust AI integration.
Room for Improvement: MongoDB needs better enterprise integration, indexing improvements, and enhanced support for complex queries. Azure Database for PostgreSQL would benefit from easier scaling, enhanced mirroring, and improved cost management for logging metrics.
Ease of Deployment and Customer Service: MongoDB is deployed across various environments but relies on community support. Azure Database for PostgreSQL excels in technical support and cloud scalability within Azure, facilitating setup processes.
Pricing and ROI: MongoDB offers a free community edition but higher costs for enterprise features. Azure’s pay-as-you-go model reduces hardware costs and offers smaller initial investments. ROI potential depends on user needs and deployment scale.
It offers at least 25 percent cost savings compared to maintaining on-premises databases.
Now, we use embedded PostgreSQL vectors, which will undoubtedly reduce the TCO by using a much more cost-effective solution.
We've reduced our total ownership cost because we are not spending on expensive SQL server licenses.
Actually, with MongoDB, it's difficult to calculate the return on investment; it's too expensive for our use.
Once we open a support case, we have people engaged within about 20 minutes, especially for a Sev 1 issue.
The documentation and training we've received through Microsoft Learn on how to migrate, deploy, and manage the solution is exceptional.
We handle most implementations in-house, without extensive reliance on Microsoft's technical support.
However, we can see how well it scales after we deploy it for some large enterprise customers or big government organizations.
The scaling options with FlexServer provide us with the flexibility we need based on application complexity.
We can scale up compute and scale it down, but once storage is allocated, there is no way to scale it back down.
In CosmoDB, the scalability is much better than with the MongoDB ReplicaSet models.
MongoDB is highly scalable.
Overall, on a scale of one to ten, I would rate MongoDB an eight; it's mostly because we're still running a monolithic environment on old hardware, so there are some limitations with read-write access.
There is a stability issue where, if the database usage peaks quickly, it may crash and require intervention to restore functionality.
We have generative AI applications, and we have not noticed any latency.
Overall, I have not encountered any real latency issues or stability concerns.
It does not presently support knowledge graph functionalities as Neo4j does.
Azure Database for PostgreSQL can be improved by allowing quicker scaling without blips.
I believe there could be improvements in the mirroring part and Change Data Capture (CDC).
While solutions for other databases like SQL or PostgreSQL already exist, MongoDB requires additional integrations for developing AI solutions.
We have not contracted the security options in our contract because they're too expensive; thus, we implement just encrypted databases and not the security pack.
We've reduced costs by 60 percent compared to maintaining on-premises solutions.
The pay-as-you-go pricing model positively affects database-related costs by allowing us to start small and scale as needed.
The pay-as-you-go model works well for us.
We use the free version of MongoDB, so there are no licensing costs.
We have to pay approximately 2,000 euros per month for MongoDB.
My takeaway as a CTO is that they're comfortable with the security posture, the features, the observability, alerts, and now it integrates into the rest of the Azure landscape.
The query analyzers help me find out what's happening in each of the queries.
The most valuable features of Azure Database for PostgreSQL are its networking capabilities, which allow for integration with other Azure services.
It offers flexibility in schema adaptation, allowing us to change the schema and add new data points.
In ReplicaSet, it's acceptable, but if your workload needs more performance, and you must pass to a Sharding model, it becomes complicated in MongoDB; in Cosmos DB, however, it's simple.
MongoDB has definitely helped us improve our network monitoring and reporting dashboard.
Azure Database for PostgreSQL is a robust cloud solution designed to host scalable applications, manage large datasets, enable advanced analytics, and ensure data integrity through strong security features and automated backups.
Many utilize Azure Database for PostgreSQL due to its seamless integration with other Azure services and ease of setup. It supports advanced analytics and data warehousing with powerful querying capabilities. Users appreciate its high availability, automated backups, and strong security measures like advanced threat protection and encryption. Azure Database for PostgreSQL's compatibility with standard PostgreSQL ensures a smooth migration process and minimal disruption to existing applications. However, some areas needing improvement include scalability, performance under heavy loads, monitoring tools, integration with other services, documentation, support response times, and stability during peak times. Pricing is also considered high by smaller businesses.
What are the most important features of Azure Database for PostgreSQL?
What benefits and ROI should users look for?
In healthcare, Azure Database for PostgreSQL is often implemented to manage and analyze large patient datasets while ensuring data security and compliance with regulations. E-commerce companies utilize it to handle scalable transactions and customer data management, leveraging its integration with data analytics tools. Financial institutions employ it to securely store and process large volumes of financial data, relying on its robust security and automated backups.
MongoDB Enterprise Advanced is a comprehensive platform renowned for its scalability, user-friendliness, and high performance, underpinned by its flexible document-based storage and open-source model. JSON compatibility, clustering, and security elevate its standing among professionals.
The platform facilitates efficient data management through developer-friendly tools and a strong aggregation framework. MongoDB’s no-schema requirement, supported by community expertise, underlines its adaptability. While its sharding capabilities and affordably support large data volumes, there are aspects such as security enhancement and enterprise tool integration that need attention. Indexing and query optimization pose challenges, alongside high costs. Improvements in analytics and UI could advance its infrastructure further.
What are the key features of MongoDB Enterprise Advanced?Industries leverage MongoDB Enterprise Advanced for significant roles in data storage within IoT platforms, healthcare apps, public service monitoring, and big data analytics. Companies in logistics and telecommunications find it instrumental for business process management and video content management, benefiting from its seamless integration and unstructured data support.
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