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 Vertica both compete in the database management sector, with MongoDB excelling in scalability and developer-friendly features while Vertica holds an advantage in complex query performance due to its columnar storage optimized for analytics.
Features: MongoDB is highly valued for its dynamic schema design, making it easy to support agile development and scalability. It is favored for handling unstructured data, essential in fast-paced environments. Vertica's features include exceptional analytical processing capabilities, leveraging its columnar storage for optimizing complex queries and data aggregation. It supports large-scale data analysis with superior performance for business intelligence and reporting requirements.
Room for Improvement: MongoDB requires enhancements in transactional support and better integration with BI tools. There is a need for improved security and indexing capabilities to broaden its enterprise appeal. Vertica could benefit from advancements in transactional capabilities and improved workload management. Enhanced integration with diverse storage options, including cloud environments, would broaden its deployment flexibility and scalability.
Ease of Deployment and Customer Service: MongoDB offers flexible deployment options across cloud, hybrid, and on-premise environments with varied support experiences, from community-driven to dedicated enterprise support. Vertica's deployment can be complex due to its architecture, requiring configurations tailored to user needs, though it offers reliable customer service to manage performance issues effectively.
Pricing and ROI: MongoDB provides an economical entry with its free community edition, though its enterprise edition incurs higher costs for advanced features and support. Vertica's pricing reflects its analytical strengths and is more costly, yet it offers savings in large-scale deployments due to its efficiency and competitive pricing models, yielding strong ROI when used for intensive analytics.
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 is a flexible and scalable NoSQL database solution that efficiently handles both structured and unstructured data. It is known for its ease of use, JSON capabilities, and efficient data handling, making it ideal for developer-friendly environments.
MongoDB stands out with features like sharding, geospatial indexing, and its open-source nature. Users find it fast and appreciate its seamless integration into different applications. Despite being powerful, it remains straightforward to set up and use, bolstered by a clustering and aggregation framework that enhances its functionality. While generally appreciated for its cost-effectiveness and broad application compatibility, there is room for improvement in security, enterprise integration, and the creation of more robust training and documentation resources. Enhancing performance and adding support for traditional relational database concepts could also prove beneficial.
What are MongoDB's most important features?MongoDB finds widespread application across industries like healthcare, logistics, and telecommunications. It efficiently powers IoT platforms, data analytics, and document management systems. Its schema flexibility and JSON storage capabilities make it ideal for real-time processing and multi-server databases, essential for handling large-scale data volumes in modern applications.
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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