Apr 19, 2020
I haven't used SQream personally. However, if you are only considering GPU based rdbms's please check the following
https://hackernoon.com/which-gpu-database-is-right-for-me-6ceef6a17505
MongoDB and OpenText Analytics Database Vertica compete in the database category. MongoDB is often preferred for pricing and support, while Vertica is viewed as better in terms of advanced features.
Features: MongoDB is lauded for its scalability, JSON document storage, and fast search queries, making it ideal for semi-structured data. Its open-source framework is a key benefit for cost-effective application. Vertica excels in advanced analytics, parallel processing, and superior query performance, crucial for handling complex analytical tasks and integrating with varied data sources.
Room for Improvement: MongoDB could enhance integration and security features and refine its documentation. There's a call for improved indexing stability and capabilities for managing large datasets, particularly around security configurations. Vertica could work on making its documentation more intuitive and expand its data management system integrations, focusing on workload management and dynamic storage options.
Ease of Deployment and Customer Service: MongoDB is praised for its simple deployment across hybrid, public, and private clouds, though support can vary. Vertica supports hybrid cloud and on-premises deployments, with professional support being helpful but documentation needing more clarity and examples.
Pricing and ROI: MongoDB’s open-source availability lowers initial costs but enterprises seeking support packages might encounter higher expenses. Its scalable nature promises high ROI in specific projects. Vertica’s pricing is deemed fair for its advanced features, despite potentially being costly for smaller organizations. It offers a predictable cost model through storage size-based licensing, aiding scalability planning.
Actually, with MongoDB, it's difficult to calculate the return on investment; it's too expensive for our use.
CosmoDB is better for auto-scale.
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.
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.
While solutions for other databases like SQL or PostgreSQL already exist, MongoDB requires additional integrations for developing AI solutions.
We have to pay approximately 2,000 euros per month for MongoDB.
We use the free version of MongoDB, so there are no licensing costs.
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.
It offers flexibility in schema adaptation, allowing us to change the schema and add new data points.
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.
OpenText Analytics Database Vertica is known for its fast data loading and efficient query processing, providing scalability and user-friendliness with a low cost per TB. It supports large data volumes with OLAP, clustering, and parallel ingestion capabilities.
OpenText Analytics Database Vertica is designed to handle substantial data volumes with a focus on speed and efficient storage through its columnar architecture. It offers advanced performance features like workload isolation and compression, ensuring flexibility and high availability. The database is optimized for scalable data management, supporting data scientists and analysts with real-time reporting and analytics. Its architecture is built to facilitate hybrid deployments on-premises or within cloud environments, integrating seamlessly with business intelligence tools like Tableau. However, challenges such as improved transactional capabilities, optimized delete processes, and better real-time loading need addressing.
What features define OpenText Analytics Database Vertica?OpenText Analytics Database Vertica's implementation spans industries such as finance, healthcare, and telecommunications. It serves as a central data warehouse offering scalable management, high-speed processing, and geospatial functions. Companies benefit from its capacity to integrate machine learning and operational reporting, enhancing analytical capabilities.
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