DataStax Enterprise vs. MongoDB

DataStax has a good scalable option with multiple clusters and a good write rate. Cassandra also is improving and is an open-source technology that has good community support. The UI is also easy to understand and implement required functions.

Incentivized

MongoDB [is] great at storing JSON data grouped into "collections". In this format, you can store any JSON documents and conveniently categorize them by collections. The JSON document contained in MongoDB is called binary JSON or BSON and, like any other document in this format, is unstructured. Therefore, unlike traditional DBMS, any kind of data can be stored in collections, and this flexibility is combined with the horizontal scalability of the database. It should be noted that MongoDB does not have links between documents and “collections” (this is partially compensated by the Database Reference - links in the DBMS, but this does not completely solve the problem). As a result, a situation arises in which there is a certain set of data that is not related to other information in the database, and there is no way to combine data from different documents. In SQL systems, this would be an elementary task.

• Datastax Cassandra provides high availability and good performance for a database. It is built on top of open source Apache Cassandra so you can always somewhat understand the internal functioning and why.
• Datastax Cassandra is fairly simple to start using, you can install/setup your cluster and be productive in 1 day.
• Datastax Cassandra provides a lot of good detailed documentation, and when starting, the detailed free videos on the Datastax site and documentation are very helpful.
• Datastax Enterprise Edition of Cassandra provides more tools, good support, and quick response SLA for enterprise business support.

Incentivized

• Easy to learn. When I picked up MongoDB for the first time, I had little background in database management or modeling. If you have a background in javascript (and JSON)... then you can figure out how to use MongoDB pretty fast.
• Fast performance.
• It's relatively easy to set up in certain environments because there are lots of ready-made solutions out there.
• There's a lot of support in the existing ecosystem for it —, especially in the node.js realm.
• Query syntax is pretty simple to grasp and utilize.
• Aggregate functions are powerful.
• Scaling options.
• Documentation is quite good and versioned for each release.

Incentivized

• Cassandra is a bit difficult to learn and understand
• The costs are slightly higher for our company
• Hardware requirement is moderate to high at the beginning

Incentivized

• I love the idea of Map-Reduce native support in MongoDB. Admittedly I have not used it as much as I would like -- it always seems to trip me up.
• Recent additions to the aggregation queries have helped reduce (no pun intended) my need to better wield the weapon that is Map-Reduce.

Incentivized

We will continue to use it because it scales well with commodity hardware and we are satisfied with the documentation and support.

Incentivized

MongoDB is one of the most famous non-relational databases in the world, there are famous active projects that use this database. I think that the same company that develops the database gives you the online induction totally free is something that really is very positive. Accounts with a first-class support to be able to relate the correct implementation of the database, in addition to teaching you the best practices to optimize your projects, I believe that with this decision it is more than obvious which is the best decision at the time of seeing with which database to work.

Incentivized

There is a bit of a learning curve and tasks that are simple in traditional RDBMS systems can be complicated with DataStax Enterprise but once you get the hang of denormalizing data and getting the data model correct DataStax Enterprise is very usable. Usability from the developer's standpoint is very simple - the complication is on the architecture side with the data model.

It is one of the reasons why we prefer it to store documents in a JSON-style format, to access the desired document very quickly regardless of its size, to be readable by human eyes, and to be easily scalable and manageable.

Incentivized

We have had a few situations where we caused an outage or something has gone wrong and we are able to get a support person to offer live help within minutes. The escalation process is excellent - the best I've seen - and the support team is incredibly strong. Outside of emergencies, the team is very helpful with general questions and working through data model exercises and the subscription I believe still comes with some hours to help get the data model reviewed.

I have reached multiple times to the MongoDB community for the help and they have provided each and easy solution for every problem. Over the internet and on stack overflow many people responds over the challenges. Now this tool is very much used in every company and projects so internally many people are there to give a support.

Incentivized

While the setup and configuration of MongoDB is pretty straight forward, having a vendor that performs automatic backups and scales the cluster automatically is very convenient. If you do not have a system administrator or DBA familiar with MongoDB on hand, it's a very good idea to use a 3rd party vendor that specializes in MongoDB hosting. The value is very well worth it over hosting it yourself since the cost is often reasonable among providers.

Incentivized

I believe DataStax Enterprise is the best in class. There are some things that are different with the schema-less systems but I found DataStax Enterprise easiest to implement while evaluating. The replication is on par or better than others in practice. We are evaluating Astra in our test environment and that has additional benefits we are looking forward to using.

The environment I work in is somewhat unique in that we use both MySQL and MongoDB. However, each is used for specific purposes that the other is not well suited for. MongoDB is not a relational database like MySQL, so it serves as the perfect place to dump key bits of data for quick retrieval later. This is something we can't easily do with MySQL. On this smaller database, MongoDB also lets us retrieve data more quickly with its fast and efficient querying.

Incentivized

• Highly Scalable Database, Highly Available Services, and Platforms.
• High Performance, Low Latency and Highest throughput across varying workloads.
• Configured, Tuned and Monitored correctly works to provide the best user experience!
• Negative: Maintenance and Debugging Corner Cases

Incentivized

• We can make more open and flexible systems due to its easy adaptation to new evolutions in web applications.
• In the latest versions it offers support for different transactions and we could carry out real tests related to the concurrency of the application.
• MongoDB allows you to have distributed clusters, which improves the speed of the queries by reducing the latency that exists between the database cluster and the service that executes the query.

Incentivized