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 Enterprise Advanced and OpenText Analytics Database (Vertica) compete in the database solutions category. User reviews suggest MongoDB has the upper hand in affordability and support, while Vertica stands out in advanced analytics and performance.
Features: MongoDB offers fast searches, scalability, and a developer-friendly environment, primarily supporting JSON document storage. Vertica is known for its quick response times on complex queries due to columnar storage, efficient projections, and parallel processing capabilities.
Room for Improvement: MongoDB users suggest enhanced security, indexing stability, and better hybrid environment integration. Vertica could improve workload management, documentation, and developer tool integration, as well as manage resource demands during peak loads.
Ease of Deployment and Customer Service: MongoDB is versatile in hybrid cloud deployment and has a strong community, although enterprise support could be faster. Vertica is versatile across platforms but lacks intuitive management tools and robust technical support. Both could benefit from enhanced customer engagement.
Pricing and ROI: MongoDB is valued for its open-source model and cost-effectiveness, appealing to small to medium businesses with low TCO. Vertica’s pricing is competitive, based on data size rather than node count, but can be pricey for large implementations. While MongoDB shows performance improvement for ROI, Vertica’s pricing aligns with its analytics capabilities, yet both need clearer ROI visibility.
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.
I saved a lot of money because the storage was on a cheaper alternative and was not directly on OpenText Analytics Database (Vertica), but on S3.
The time we used to take with our earlier databases has reduced to one-tenth of what was there earlier, which is a positive outcome that can be converted to financial metrics in terms of return on investment.
We have received fairly good support whenever we reached out to the technical teams; they were prompt.
Throughout this process, customer support was outstanding, and we had a person actively supporting us from the OpenText Analytics Database (Vertica) team for our use case.
Overall, our experience with OpenText Analytics Database (Vertica) customer support has been good and reliable.
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.
We have experienced easy horizontal scaling, consistent query performance as data grew, and the ability to handle large analytic workloads.
OpenText Analytics Database (Vertica) has very good scalability.
The scalability of OpenText Analytics Database (Vertica) is very strong.
It's pretty much stable; we have not faced any major challenges or difficulties with MongoDB Enterprise Advanced.
OpenText Analytics Database (Vertica) is very stable.
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.
Smarter automatic projection management is needed with more intelligence, auto projection creation, automatic optimization, and reduced manual testing with better workload management.
Projections could be made more dynamic, and if they could find a faster way to update, insert, and delete data, that would also be helpful.
OpenText Analytics Database (Vertica) does not have a cloud-based UI that Snowflake has, which features a very good comprehensive GUI for querying and analyzing data.
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.
The pricing for OpenText Analytics Database (Vertica) is somewhat on the higher side for the license.
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.
I can use it in Eon Mode in which I can store the data in cheaper storage such as Amazon S3 and have different compute nodes.
Projection and columnar storage are the most valuable features because they dramatically improve query performance and reduce the need for index management.
The best features that OpenText Analytics Database (Vertica) offers are mainly the parallel processing, ETL capabilities, and the multi-cloud features which are very handy to use.
| Company Size | Count |
|---|---|
| Small Business | 35 |
| Midsize Enterprise | 13 |
| Large Enterprise | 38 |
| Company Size | Count |
|---|---|
| Small Business | 29 |
| Midsize Enterprise | 23 |
| Large Enterprise | 42 |
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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