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.
"One of the biggest benefits is the speed and flexibility of the documents, especially when it comes to modifications."
"MongoDB is easy to use."
"Its flexibility, and cost. It is reasonably priced."
"One of the most valuable features is the ability to Text Search can be used anywhere and anytime."
"It's easy to add and remove things in MongoDB. You can alter the tables. MongoDB is faster at reading, slower at writings."
"I like that MongoDB has a free version. You can also buy the enterprise edition, which is cheaper than Oracle."
"The installation is very easy to do and understand."
"It can handle a lot of files quickly."
"Partition and join back to node are easy and simple for DBAs."
"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."
"Vertica has a few features that I like. From an architecture standpoint, they have separated compute and storage. So you have low-cost object storage for primary storage and the ability to have several sub-clusters working off the same ObjectStore. So it provides workload isolation."
"Eighty percent of the ETL operations have improved since implementing this solution."
"Bulk loads, batch loads, and micro-batch loads have made it possible for our organization to process near real-time ingestions and faster analytics."
"Speed and resiliency are probably the best parts of this product."
"The feature I like best is performance. We use Red Tool and Red Job for the data warehouse and reporting. It's perfect. Performance is good, and it can return ad hoc queries very quickly. Of course, it's a cluster, so it's easy to scale."
"The Vertica architecture means it can process/ingest data in parallel to reporting and analyzing because of its in-memory Write-Optimized Storage sitting alongside the analytics optimized Read-Optimized Storage."
"You need integration with other tools to run the query in MongoDB."
"It would be good to have scalability for clusters. For example, if we have three clusters, we should be able to increase to five clusters if required. I am not sure if such a feature is currently there. I hope there is good documentation for this."
"The scalability of the solution has room for improvement."
"MongoDB is a very useful and convenient choice, but sometimes for more complex projects, there are certain niche requirements that appear, so using a different tool could be beneficial. It raises the complexity of the architecture, but it could be beneficial to the world, the features, the ease of the features which are being implemented."
"MongoDB should be more stable, and support should be more efficient."
"It could be much more flexible like SequoiaDB. I would like to see more flexibility in the next release, especially when working with Microsoft Windows. A lot of people struggle with MongoDB because of their Windows versions. But Linux is faultless and mostly runs nicely."
"The MongoDB documentation can be a little complicated sometimes."
"It has certain limitations when it comes to handling hierarchical data, enforcing relationships, and performing complex joins, which should be taken into account when designing databases for applications with intricate data requirements."
"It would be great if this were a managed service in AWS."
"Vertica seems to scale well, except for one use case where you are on a multi-node cluster. For example, if you had a nine-node cluster, one node goes down, then the eight nodes don't scale, because the absence of the node is very apparent, which is a problem. If you have nine nodes or multiple nodes, the whole idea is that if one of those nodes goes down, then you should not see an impact on the system if you have enough capacity. Even though we have enough capacity, you can still see the impact of the one node going down."
"Very bad support, I would rate it two out of 10."
"I believe the installation process could be streamlined."
"I think they need an easy client so that you can write queries easily, but it's not necessarily a weak point. I think some users would need them."
"I have found that coding support could be simplified."
"Vertica's native cloud support could be improved, and its installation could be made easier."
"They could improve on customer service."
MongoDB is ranked 1st in NoSQL Databases with 69 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.