We compared MongoDB and Vertica based on our user's reviews in 4 parameters. After reading all of the collected data, you can find our conclusion below.
MongoDB is praised for its flexibility, scalability, advanced query language, and reliable customer service. Users suggest improving the query language, documentation, and performance optimization. MongoDB offers flexible pricing and provides a strong return on investment. Vertica highlights exceptional performance, scalability, ease of use, and advanced analytics capabilities. Users suggest improving the user interface, documentation, compatibility, and performance. Vertica offers reasonable pricing and receives positive ROI feedback.
Features: MongoDB's valuable features include flexibility in working with dynamic data structures, scalability for efficient data management, a powerful query language, and reliable replication. Vertica stands out for exceptional performance, ease of use, advanced analytics capabilities, and seamless integration with various data sources and tools.
Pricing and ROI: MongoDB offers a user-friendly and seamless setup cost, with flexible pricing options to cater to different budgets and needs. Vertica stands out with its relatively low setup cost compared to similar products, and its licensing is praised for its flexibility in customization. MongoDB's ROI is praised for its positive outcomes and benefits according to user feedback, while Vertica's ROI is highlighted in user reviews.
Room for Improvement: MongoDB users have emphasized the need for a more intuitive query language, improved error handling, better documentation, and faster query execution. Enhanced integration capabilities with popular programming languages and third-party tools are recommended. Vertica users have suggested improvements in the user interface, better documentation, increased compatibility and integration with other data management systems, and optimized performance and speed.
Deployment and customer support: MongoDB's customer support receives positive feedback and offers responsive and helpful technical teams, although limited to the enterprise version. Support is highly rated during data validation and migration events. Open-source users rely on community support. The initial setup for MongoDB varies. Some find it easy, especially on-premises or in private clouds, while others note complexity, particularly in feature-rich or clustered deployments. Vertica's customer support is praised for its knowledge and responsiveness, although some users report challenges with issue escalation and lengthy fixes. Users find Vertica's initial setup and deployment straightforward, typically taking a few days. Internal teams manage deployment easily with assistance from Vertica and vendor support.
The summary above is based on 150 interviews we conducted recently with MongoDB and Vertica users. To access the review's full transcripts, download our report.
"The aggregation framework is very powerful when elaborating on data."
"The solution is user-friendly with a good object retrieval feature."
"My impression is that the initial setup is straightforward."
"I find the integration with other tools very easy."
"We've found the product to be scalable."
"It has visible benefits, actually, in terms of price of ownership if you compare it to, for example, Oracle."
"It is easy to set up."
"The most valuable features of MongoDB are we have a lot of documentation and SQL-based applications that run on it."
"Vertica is a columnar database where the query performance is extremely fast and it can be used for real-time integrations for API and other applications. The solution requires zero maintenance which is helpful."
"It maximizes cloud economics with Eon Mode by scaling cluster size to meet variable workload demands."
"It's the fastest database I have ever tested. That's the most important feature of Vertica."
"We are also opening new areas of business and potential new revenue streams using Vertica's analytic functions, most notably geospatial, where we are able to run billions of comparisons of lat/long point locations against polygon and point/radius locations in seconds. "
"The feature of the product that is most important is the speed. I needed a columnar database, and its speed is what it's built to do, and so that's what really does differentiate Vertica from its competitors."
"Vertica is a columnar database, this support our developments in analytics, advanced analytics, and ETL process with large sets of data."
"For me, It's performance, scalability, low cost, and it's integrated into enterprise and big data environments."
"Bulk loads, batch loads, and micro-batch loads have made it possible for our organization to process near real-time ingestions and faster analytics."
"Lacks sufficient scalability and elasticity."
"The scalability of the solution has room for improvement."
"More stable indexes would be helpful in a future release. That's been an issue for some time. I don't know if it's been fixed now but we transitioned to it because we needed a search index to be able to search for things and if that goes or starts disappearing, we have to move away from that solution. I don't know if they fixed it, last time I had this issue was three years ago so they might have solved it."
"It would be much more useful if I have an admin user and a password."
"The solution can be a bit tough to set up if you don't have knowledge about how the database works."
"The improvements could be made to intelligence to detect disk storage and prevent MongoDB from crashing."
"It isn't easy to recognize entities with MongoDB."
"There are some problems with bugs appearing in sharding when the data is too high."
"Promotion/marketing must be improved, even though it is a very useful product at very good price, it is not as "popular" as it should be."
"We are looking for a cheaper deployment for the solution. Although we did a lot of benchmarks, like Redshift. We tried Redshift, it didn't work. It didn't work out for us as well."
"Vertica's native cloud support could be improved, and its installation could be made easier."
"It would be great if this were a managed service in AWS."
"Fact-to-fact joins on multi-billion record tables perform poorly."
"Metadata for database files scale okay, but metadata related to tables/columns/sequences must be stored on all nodes."
"The geospatial functionality could be designed better."
"They could improve the integration and some of the features in the cloud version."
MongoDB is ranked 1st in NoSQL Databases with 68 reviews while Vertica is ranked 4th in Data Warehouse with 83 reviews. MongoDB is rated 8.2, while Vertica is rated 8.2. The top reviewer of MongoDB writes "Lightweight with good flexibility and very fast performance for searching data". On the other hand, the top reviewer of Vertica writes " A user-friendly tool that needs to improve its documentation part". MongoDB is most compared with InfluxDB, Couchbase, ScyllaDB, Oracle NoSQL and Aerospike Database 7, whereas Vertica is most compared with Snowflake, SQL Server, Amazon Redshift, Teradata and Apache Hadoop. See our MongoDB vs. Vertica report.
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SQreamDB is a GPU DB. It is not suitable for real-time oltp of course.
Cassandra is best suited for OLTP database use cases, when you need a scalable database (instead of SQL server, Postgres)
SQream is a GPU database suited for OLAP purposes. It's the best suite for a very large data warehouse, very large queries needed mass parallel activity since GPU is great in massive parallel workload.
Also, SQream is quite cheap since we need only one server with a GPU card, the best GPU card the better since we will have more CPU activity. It's only for a very big data warehouse, not for small ones.
Your best DB for 40+ TB is Apache Spark, Drill and the Hadoop stack, in the cloud.
Use the public cloud provider's elastic store (S3, Azure BLOB, google drive) and then stand up Apache Spark on a cluster sized to run your queries within 20 minutes. Based on my experience (Azure BLOB store, Databricks, PySpark) you may need around 500 32GB nodes for reading 40 TB of data.
Costs can be contained by running your own clusters but Databricks manage clusters for you.
I would recommend optimizing your 40TB data store into the Databricks delta format after an initial parse.
Morten, the most popular comparisons of SQream can be found here: www.itcentralstation.com
The top ones include Cassandra, MemSQL, MongoDB, and Vertica.