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 technical evaluation is very good."
"The most valuable features of Cassandra are the NoSQL database, high performance, and zero-copy streaming."
"I am satisfied with the performance."
"The use of Cassandra in real-time data analytics has been pivotal for our e-commerce platform. As our platform operates 24/7, providing services to sellers and customers alike, the need for real-time updates is paramount."
"A consistent solution."
"Some of the valued features of this solution are it has good performance and failover."
"I am getting much better performance than relational databases."
"We can add almost one million columns to the solution."
"MongoDB is flexible and it allows other applications to be added."
"Scalability seems good. I've never been even close to finding the limits. I've run a couple of notes of redundancy but I've never had any problems with scalability."
"The most valuable feature is the geometric information done with GeoJSON."
"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."
"I like that MongoDB has a free version. You can also buy the enterprise edition, which is cheaper than Oracle."
"The most valuable feature is that you can store unstructured data, which is helpful when you don't know what the best structure should be and you cannot use a relational database because of that."
"One of the most valuable features of MongoDB is it is Its open source."
"Migrating to MongoDB upgrades the IT environment and puts users in the NoSQL environment, which is faster."
"The solution is not easy to use because it is a big database and you have to learn the interface. This is the case though in most of these solutions."
"Depending upon our schema, we can't make ORDER BY or GROUP BY clauses in the product."
"Cassandra can improve by adding more built-in tools. For example, if you want to do some maintenance activities in the cluster, we have to depend on third-party tools. Having these tools build-in would be e benefit."
"Doesn't support a solution that can give aggregation."
"Maybe they can improve their performance in data fetching from a high volume of data sets."
"It can be difficult to analyze what's going on inside of the database relative to other databases. It can also be difficult to troubleshoot sometimes."
"The initial setup of Cassandra can be difficult in the configuration. There might be a need to have assistance. The implementation process can six months for connecting to certain databases."
"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."
"There are some problems with bugs appearing in sharding when the data is too high."
"The performance of the solution could be improved."
"The transaction could use improvement. From MySQL, for example, you cannot create a transaction if you are reading and writing a document at the same time."
"MongoDB should incorporate more features, particularly search functionality, and real-time communication capabilities, to improve the database and provide data listening services. Currently, we rely on the Atlas offering, but it would be fantastic if MongoDB could develop a new solution or updated version that includes these features within its internal database and driver. However, I am uncertain if this would be a viable or profitable move for them, and I am speaking from a mobile-centric viewpoint."
"MongoDB should better support small and medium companies. There are a lot of clients out there that are interested, however, they need something lighter and less complex and something not so expensive upfront."
"The solution could have more integration."
"I have found the solution difficult to operate as an administrator."
"We'd like information about client onboarding experience and success stories. It would help to have something to show to internal stakeholders."
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 DataStax, whereas MongoDB is most compared with InfluxDB, Couchbase, ScyllaDB, Oracle NoSQL and Oracle Berkeley DB. See our Cassandra vs. MongoDB 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.