MongoDB

The best for document databases

A lot of community support for free

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.

Support gave clear and timely answers and recommendations.

This is a good tool in its category for reading real-time unstructured data. As my team works on understanding the sentiments of users over some products, initially we used SAS Text Miner but we were not getting those desired results as it was unable to process the real-time data.

The only major problem is that it is incapable of performing statistical analysis. Now as I am using it for streaming data for storage it working very much fine but some times if the logic is lengthy for defining data types then it will became tricky and hence then it becomes time consuming. Overall for unstructured data this tool will suits your requirement for sure.

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.

MongoDB is an excellent tool to start development fast on a smaller POC, or, to serve as a backend for storing raw json-based data as well. It can be used for emulating a relational database but its core strength is storing the redundant, non-BCNF data, and querying it. So if we have any of those, MongoDB can serve as the DB with a really fast initialization in the development process - but just as well as in production.

If your use case does not require relation heavy data models or transactions that need to be ACID compliant, MongoDB can be a good option, especially if you are starting with a new project and need to prototype and validate your ideas quickly. Its schemaless design allows you to change your data models on the fly, which can often be the case for new projects. However, the key thing is making sure your developers can get their heads around how MongoDB works and the lack of SQL.

MongoDB provides excellent support, both via a paid plan, as well as from the community, which is huge. You can usually get your questions answered by the community.

We read data produced by a device in the network with a web API and take it on MongoDB. We also encrypt and compress text-weighted large data collected on MongoDB and extract it daily on a filing system. MongoDB preferred that for such applications because NoSQL structure gives more speed.

There are enough resources and communities on the internet. If you research the points where you get stuck, you will not have any difficulties.

It's a rather obvious choice when a decision has been make to start a new project and ending up not wanting to implement it with a relational database solution. MongoDB is well suited in storing all kinds of data an application might need, all you need to do is evaluate whether the application would benefit from a relational database or not.

Have not needed any official support. Plenty of resources available online, both official and community.

If you are looking for a no sql db then MongoDB is one of the best open source solution with a great community who can help you to solve any problems. It has a high availability and indexing is pretty fast as well. You may have to research a bit on your use-case before going for a nosql db but if it fits your use-case then it is very developer friendly. Integrates well with nodejs, python , java etc.

Community backed MongoDB support is pretty great, you can always ask any questions or specific use cases on the MongoDB forum and you'll get a reply at the latest by 1-2 days.

As a developer, the main reason for using MongoDB is its speed and its ability to add records with different fields to the same collection in a much more flexible way than a database based on a relational model. For example, a document can be used to represent a blog and the associated comments can be placed as an array in the main document. This makes data easier to manage, eliminating the "JOIN" operation that affects performance and horizontal scalability in traditional relational databases.

As for less favorable scenarios, they could be those applications that need to perform frequent updates with many transactions, for example bank transactions.

In general, the documentation is good and the support team usually responds to questions fairly quickly, as well as a strong community that often shares a lot of knowledge in the form of articles and webinars.

So if you need a highly available database, which you can rely on since it has inbuilt replication and JSON format message, then MongoDB is the best way to go for it. It follows BASE if the databases are inconsistent if you are scaling over a large system. What it means is that it is not suitable for storing passwords. For that, make sure that you use ACID databases which are relational.

Scenarios where MongoDB is well suited:
- When working with small/medium-sized dataset where speed and flexibility are priorities.
- When working with schema-less or polymorphic models that would be much harder to represent in a traditional RDBMS.
- More generally MongoDB makes sense as a place you'd store your business logic/frequently accessed data, not as storage for infrequently accessed long-term storage.

Scenarios where MongoDB is less appropriate:
- I wouldn't recommend using MongoDB as a caching service. It's more expensive than many databases that could be used where performance isn't a critical issue or long-term persistence is desired (e.g., compared with Datastore/Firestore/Dynamo/etc.), while it falls short of Redis when performance is critical or data need not be stored for long.

Modern web and mobile applications developed entirely in Javascript ( Node.js, Angular, or Vuejs or Emberjs.) are particularly interested in using MongoDB as a database. It is very easy to develop an API to manage the database accesses in an optimal, fast and secure way.
Loopback.js is a good starting point to get an idea of the potential of this environment.

While we don't and wouldn't use it for transactional data, it's an excellent tool for collecting big unstructured data.

I believe any startup or any business with a rapidly changing data schema would benefit from using a no-SQL database in general. Analytical and traditional reporting can be difficult to do out of MongoDB or any no-SQL database because the SQL capacity is pretty limited.

The tool is very easy to set up and start developing right away. I found it extremely simple to start utilizing in a short period of time. Mongo is more suited for people not concerned with back end logic as there is no joining like in a typical relational database scenario. However that could cause disruption to the people used to seeing relational databases.

MongoDB is very flexible, and high performing. I have evaluated a number of NoSQL databases and have found MongoDB to be the best overall. Aggregation pipelines took a little bit of practice to learn but once I got the hang of it, I realized how they could be used to solve problems easily.

MongoDB supports JSON Schema data with the most complete NoSQL query framework among all NoSQL databases. The shard cluster is well designed for large applications with multiple concurrent users. It is best suited for applications that store data mostly for reading and reporting. But it will be improved towards applications with more write operations as well in future when transaction are supported on shard cluster (expected in version 4.2).

MongoDB using JSON like documents in a database system. Also, it's an open source software. Its very flexible compared with other database related software. It helps to store a very large amount of data perfectly. It provides high performance, availability, and scalability. It's a NoSql database.

But the main disadvantage of this product is we can't use it for transaction functionalities.

This product is well suited if your need is to use a fast distributed DB with semi structured data and your semantics are not well predefined. It's also useful for building apps requiring real-time responses and fast deployment with ease of maintenance.
I wouldn't recommend you use it for any scenarios where it's beneficial to normalize the data.

I think that MongoDB is the easiest and fastest database solution when starting any new project. Unless the project has a clear need for a relational setup from the beginning, it just feels a lot easier and faster to work with MongoDB. Scenarios where it's less appropriate would mostly be those that need the features of a relational (ex: SQL) database. Even then, we like to use MongoDB as a primary database and use SQL only for the aspects of the application that are better suited to it.

MongoDB would be ok if you're starting from scratch with a very small team and want to gradually build your product (specification is in flux) along with continually learning, optimizing and monitoring your database (something one should probably be doing anyway). It also might be good if your system has little need for consistency and you can afford nesting documents and data duplication. For any other use case, like a big team with defined complex specifications or a high need for consistency, you will probably end up with a mess.

If you have a large amount of unstructured data, (like NoSQL), to be read or written in a short amount of time, MongoDB is a great choice for this. Its structure well secures the data from being lost. It has good scalability to handle an increasing amount of data. It has a well-supported team to help you set up and maintain the cluster. Overall, it is a good choice to use for a NoSQL database.

In case if you have less budget, go for this tool as it's free. Also, if you want to work with JSON Schema or file system, I will recommend this tool. In case budget is not concern, I will recommend SQL server or OracleDB.

MongoDB is very much well suited if you are storing raw data. Also, it can be easily integrated into the Hadoop big data ecosystem, so it is useful if you have a large amount of data. Scalability is another amazing feature of this database system. But MongoDB will not perform well if you have a use case where you have to update your data very frequently. In case of frequent updates, Cassandra will be a better option.

Amongst situations where the data being modeled is not well structured, using a NoSQL database — and using MongoDB in particular — may be a great choice. While Mongo *does* let you get away with less structure, you must be aware that less structure is not always the correct development avenue to take. Not having to manage a database schema does not necessarily make your development speed any faster.