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.
"It is very fast - faster than an SQL or MySQL Server."
"The most valuable feature is the geometric information done with GeoJSON."
"I value the API integrations."
"The community is great if you have problem."
"My impression is that the initial setup is straightforward."
"The installation is very stable."
"It facilitates the generation of heatmaps for graphical data analysis."
"It's super easy to develop a couple of solutions for clients with MongoDB, like a quick web page with no clear data structure that they need to spin up quickly to validate some sort of MDTP."
"The fast columnar store database structure allows our query times to be at least 10x faster than on any other database."
"Vertica is a columnar database, this support our developments in analytics, advanced analytics, and ETL process with large sets of data."
"I don't need any special hardware. I can use commodity hardware, which is nice to have in a commercial solution."
"The performance is very good and the aggregate records are fast."
"Any novice user can tune vertical queries with minimal training (or no training at all)."
"The most valuable feature of Vertica is the ability to receive large aggregations at a very quick pace. The use case of subclusters is very good."
"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."
"Vertica enabled us to close large deals. Customers with large data sets had to be migrated from PostgreSQL to Vertica due to performance."
"I think it would be good to have more search options such as an index resource. This will provide more options and resources to do advance searches."
"The dashboard is an area of concern in the solution where improvements are required."
"I think that MongoDB's search engine should be improved."
"The MongoDB documentation can be a little complicated sometimes."
"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 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."
"I'd like to see an ID generator. It's very technical but I don't think it has one, so we have to go to great lengths to work around that."
"When it is about to reach the maximum storage capacity, it becomes slow."
"Monitoring tools need to be lightweight. They should not take up heavy resources of the main server."
"It should provide a GUI interface for data management and tuning."
"I would personally like to see extended developer tooling suited to Vertica – think published PowerDesigner SQL dialect support."
"Vertica offers a platform-as-a-service version, but their software-as-a-service solution is only available on AWS. They need to get a SaaS version on Azure and GCP as fast as possible."
"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."
"Vertica can improve automation and documentation. Additionally, the solution can be simplified."
"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."
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.