Find out in this report how the two NoSQL Databases solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
Actually, with MongoDB, it's difficult to calculate the return on investment; it's too expensive for our use.
I would say we see value in money and return on investment with MongoDB Enterprise Advanced.
Technical support is very costly for me, accounting for twenty-five to thirty percent of the product cost.
We have received fairly good support whenever we reached out to the technical teams; they were prompt.
It is provided as a pre-configured box, and scaling is not an option.
In CosmoDB, the scalability is much better than with the MongoDB ReplicaSet models.
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.
MongoDB is highly scalable.
It's pretty much stable; we have not faced any major challenges or difficulties with MongoDB Enterprise Advanced.
The cloud version is only available in AWS, and in the Middle East, it is not well-developed in the Azure environment.
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.
From the AWS standpoint, if robust integration and data warehouse integration specific tools are added in the advanced suite, that would definitely be helpful.
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.
For a small company, the cost of MongoDB Enterprise Advanced is reasonable, but for heavy data usage, we see a little bit of cost pressure but it's acceptable.
It operates as a high-speed data warehouse, which is essential for handling big data.
When the database receives numerous requests, it has to perform. Those threshold limits we come to know, and then automatically these memory enhancement advanced features are configured so that during high demand periods, memory automatically increases to cater to the incoming advanced requests and volume of requests.
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.
| Company Size | Count |
|---|---|
| Small Business | 9 |
| Midsize Enterprise | 5 |
| Large Enterprise | 33 |
| Company Size | Count |
|---|---|
| Small Business | 35 |
| Midsize Enterprise | 13 |
| Large Enterprise | 38 |
IBM Netezza Performance Server offers high performance, scalability, and minimal maintenance. It seamlessly integrates SQL for efficient data processing, making it ideal for enterprise data warehousing needs.
IBM Netezza Performance Server is known for its outstanding data processing capabilities. Its integration of FPGA technology, compression techniques, and partitioning optimizes query execution and scalability. Users appreciate its appliance-like architecture for straightforward deployment, distributed querying, and high availability, significantly boosting operations and analytics capabilities. However, there are areas for improvement, particularly in handling high concurrency, real-time integration, and specific big data functionalities. Enhancements in database management tools, XML integration, and cloud options are commonly desired, along with better marketing and community engagement.
What are the key features of IBM Netezza Performance Server?Industries rely on IBM Netezza Performance Server for robust data warehousing solutions, particularly in sectors requiring intensive data analysis such as finance, retail, and telecommunications. Organizations use it to power business intelligence tools like Business Objects and MicroStrategy for customer analytics, establishing data marts and staging tables to efficiently manage and update enterprise data. With the capacity to handle large volumes of compressed and uncompressed data, it finds numerous applications in on-premises setups, powering data mining and reporting with high reliability and efficiency.
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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