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 solution is user-friendly with a good object retrieval feature."
"It is a stable solution. Stability-wise, I rate the solution a nine out of ten...Overall, MongoDB has helped manage and analyze attachment data."
"The installation is very easy to do and understand."
"It is really a pretty easy product to use. It's very reliable, it's proven."
"MongoDB has a simple data-loading interface."
"The solution does not hold data in tabular format like SQL does but rather clusters data so that it can link on a large scale."
"It is convenient to use because we can do manipulations with the JSON data that we get. There are also a lot of joins and associations with MongoDB, which makes it easy to use for us."
"Its flexibility, and cost. It is reasonably priced."
"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."
"Integrated R and geospatial functions are helping us improve efficiency and explore new revenue streams. "
"Vertica gives knowledgeable users and DBAs excellent tools for tuning."
"It maximizes cloud economics with Eon Mode by scaling cluster size to meet variable workload demands."
"I like the projection feature, which increases query performance."
"Vertica's most outstanding features are the compression rates achieved and the speed of access of high volume data."
"Any novice user can tune vertical queries with minimal training (or no training at all)."
"I have found the solution to be scalable."
"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."
"People coming from RDBMS should have the flexibility to write queries in SQL that can be converted into JSON queries."
"Enhancing the documentation to make it more beginner-friendly is crucial."
"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."
"The stability could be improved."
"The analytics needs improvement."
"The solution could include more integrations with other platforms."
"We'd like information about client onboarding experience and success stories. It would help to have something to show to internal stakeholders."
"I have found that coding support could be simplified."
"Vertica can improve automation and documentation. Additionally, the solution can be simplified."
"Documentation has become much better, but can always use some improvement."
"The integration of this solution with ODI could be improved."
"One feature, which has really benefited us, is the scalability offered by Vertica as it has enabled Pythian's clients to manage data with agility."
"The integration with AI has room for improvement."
"If you do not utilize the tuning tools like projections, encoding, partitions, and statistics, then performance and scalability will suffer."
"Limitations in group by projections is where I would like to see an improvement."
MongoDB is ranked 1st in NoSQL Databases with 70 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, Cassandra and Neo4j Graph Database, whereas Vertica is most compared with Snowflake, SQL Server, Amazon Redshift, Teradata and Microsoft Azure Synapse Analytics. 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.