We compared Cassandra and MongoDB based on our users' reviews across five parameters. After reading all of the collected data, you can find our conclusion below.
Cassandra offers scalability, high availability, fault tolerance, and distributed architecture, with praised customer service. MongoDB highlights flexibility, scalability, advanced query language, and replication, with a need for improved query language, documentation, performance, and integration. MongoDB also offers flexible pricing and strong ROI, with exceptional customer service and support.
Features: Cassandra's valuable features include scalability, high availability, fault tolerance, and distributed architecture. Users praise its ability to handle large data volumes and seamless performance across multiple nodes. MongoDB offers flexibility, dynamic data handling, scalability, advanced query language, and reliable replication.
Pricing and ROI: The setup cost for Cassandra is considered straightforward and easy to manage, with flexible and accommodating licensing options. MongoDB offers a seamless experience with a transparent pricing structure and hassle-free setup. Users appreciate MongoDB's various pricing plans and the accessibility of its open-source community edition. Both Cassandra and MongoDB offer a positive ROI. However, Cassandra focuses on improved efficiency, scalability, and performance, while MongoDB is praised for its flexibility, ease of use, and ability to handle large amounts of data efficiently.
Room for Improvement: Cassandra can benefit from improvements in scalability, performance, support for large datasets, handling of write-heavy workloads, ease of use, management of clusters, documentation, and monitoring tools. MongoDB needs enhancements in terms of its interface, navigation system, performance, scalability, documentation, customer support, and data replication capabilities.
Deployment: Some users find the setup process straightforward and easy. They mention that there is documentation and assistance available, which makes the installation process easier. However, other users mention that the initial setup can be difficult and may require assistance. The duration required for the initial setup, deployment, or implementation phases of MongoDB also varies. Some users found the initial setup to be quite easy and straightforward, taking only a couple of hours or even less than an hour. However, there were also users who found it to be a bit complex, especially for specific use cases or cluster deployments, which took a couple of days.
Customer support: Users highly praise the customer service for both Cassandra and MongoDB. They appreciate the promptness, reliability, and knowledge displayed by the support teams. Additionally, the staff's helpfulness, responsiveness, and expertise is appreciated.
The summary above is based on interviews we conducted recently with Cassandra and MongoDB users. To access the review's full transcripts, download our report.
"The time series data was one of the best features along with auto publishing."
"Can achieve continuous data without a single downtime because of node to node ring architecture."
"Cassandra is good. It's better than CouchDB, and we are using it in parallel with CouchDB. Cassandra looks better and is more user-friendly."
"The most valuable feature of Cassandra is its fast retrieval. Additionally, the solution can handle large amounts of data. It is the quickest application we use."
"The technical evaluation is very good."
"A consistent solution."
"Some of the valued features of this solution are it has good performance and failover."
"The most valuable features of Cassandra are the NoSQL database, high performance, and zero-copy streaming."
"I like the schemaless architecture that it follows. I also like the sharding that it provides."
"MongoDB is scalable and stable. The initial setup is very easy, and deployment and maintenance can be done by one person."
"The solution has good flexibility and very fast performance for searching data."
"We haven't had any issues with stability."
"The most valuable features of MongoDB are the variety of translations available and the ability to deploy it on the cloud is useful. The cloud users can access the data, work on the data, and if they want to import or manipulate some data they can."
"I like the document storage feature. It's pretty simple."
"Migrating to MongoDB upgrades the IT environment and puts users in the NoSQL environment, which is faster."
"We decided to work with MongoDB as its interface is easier to understand and more universal."
"There were challenges with the query language and the development interface. The query language, in particular, could be improved for better optimization. These challenges were encountered while using the Java SDK."
"Interface is not user friendly."
"There could be more integration, and it could be more user-friendly."
"Fine-tuning was a bit of a challenge."
"The secondary index in Cassandra was a bit problematic and could be improved."
"The disc space is lacking. You need to free it up as you are working."
"Maybe they can improve their performance in data fetching from a high volume of data sets."
"The solution is limited to a linear performance."
"I don't see a lot of areas that need improvement."
"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."
"Enhancing the documentation to make it more beginner-friendly is crucial."
"We find it difficult to incorporate MongoDB in some projects."
"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."
"MongoDB could improve by not having so many updates and different versions."
"The on-premises version of the solution is still pretty expensive, especially compared to the cloud version."
"The stability could be improved."
Cassandra is ranked 4th in NoSQL Databases with 19 reviews while MongoDB is ranked 1st in NoSQL Databases with 69 reviews. Cassandra is rated 8.0, while MongoDB is rated 8.2. The top reviewer of Cassandra writes "Well-equipped to handle a massive influx of data and billions of requests". On the other hand, the top reviewer of MongoDB writes "Lightweight with good flexibility and very fast performance for searching data". Cassandra is most compared with Couchbase, InfluxDB, ScyllaDB, Oracle NoSQL and Accumulo, whereas MongoDB is most compared with InfluxDB, Couchbase, ScyllaDB, Oracle NoSQL and Oracle Berkeley DB. See our Cassandra vs. MongoDB report.
See our list of best NoSQL Databases vendors.
We monitor all NoSQL Databases reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
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.