Awsome, ease to learn

the data embankment is secure and manageable

currently no cons for me, overall good software features

Using MongoDB for years, mostly as local storage in docker containers. Latest project is based on cloud solution.

I like the Atlas Cloud solution. It enables various tricks and cross-integrations of single database between multiple apps (containers) with simple user management - for a reasonable price.

I cannot get over this Atlas Compass UX issue - I want to open at least two "tabs" with collections at the same time. This is not possible and even worse, when you switch between collections, thee state is completely reset. So I'm compiling the search query carefully with all the commas, brackets and identifiers, I often need to search for ID from another collection or maybe from the same collection. When i change collection scope -> BAM! The whole query is gone :( The input should retain its actual value... Second "issue": When it comes to schema changes in living DB, sometimes it behaves unexpectedly (no error, no data). Maybe I'm doing something wrong, but I would like to know it...

Built several multi-node clusters for data warehousing for ETL tools. Had a lot of issues early on to get it working. Documentation 2 years ago + was spotty but now its a lot better. Not to mention the software has evolved to include features sorely missing awhile back. We moved away from thsi product as it doesnt allow backups as robust as MSSQL and oracle but its great for certain types of data needs.

Can use a free version of it. And does allow clustering of it inclusive.

Compass client tool is useless versus other DB software tools. Pretty much all cli based access ot the collections within

MongoDB is for me the top database for microservices applications. It scales great and is easy to use. It has many integrations with popular programming languages. It is easy to configure it for heavy loads.

It is a NoSQL database by which it can be a certain entry threshold for beginners. Correctly selecting and setting up indexes can be a problem.

We used Mongo to host some incoming user data on one of our portals. We needed a solution that could host variable length data where the format could change semi-irregularly. Mongo was both quick to implement and reliable in use and has caused us no problems once we finished all the configurations.

MongoDB was our go-to solution for a NoSQL database due to the ease of installation and simplicity. We had used Mongo before for smaller projects but decided to use the MongoDB Atlas service to have a reliable instance online. Using the hosted version alleviated a lot of the configuration issues we had experienced also, although it isn't required to use the hosted version depending on the use case.

The visualization aspect of Mongo constantly was an issue for our team. We wanted to display dashboards of our data through a variety of methods but most were difficult to implement using Mongo, or were simple/slow at best. There was also a lot of trouble trying to connect MongoDB Atlas with our AWS VPC, specifically so that our cloud functions could access it.

Very positive, allowed me to run a website with many many thousands of data entries, and all the usual features like relations and performance management that you would come to expect from a tool like MongoDB

Object oriented data storage is quickly becoming more popular and MongoDB makes it very easy to dip your toes into, and its blazing fast.

Adoption while fast, is still not there globally which means often resources on the web can be a bit scarce when compared to other database types.

I use mongo with our applications. For any set of data, this is the best database.

Really speedy and requires less memory. We can create collections without a definite number of rows and columns to come. We can add data without restrictions It gives a JSON output which is really easy to work with Easy to add , edit and remove data If a certain section has more than one data, we can insert them as sub arrays, and it makes the developments so flexible.

The free version allows limited memory so that we have to upgrade it to insert unlimited data. Other than that this is the best.

It is very much fast and agile in nature.

It is very very much easy to use. It also manages multi server clusters. It has all the feature for documentations. I will definitely recommend everyone to use.

It is difficult to work with complex data.

Pretty decent and easy to use. Quality has improved.

Data storage, organization and maintenance.

Due to memory issues, I’ve seen duplicate data being created most times.

Useful for modelling social graph data and catalog data

Easy setup with various SDKs Good replication strategy Aggregation modelling is very straight forward

Collection indexing is very hard to update Increased memory usage

I needed a free but industry standard database for my Node.JS based library management application. MongoDB was just what I needed, I did consider MySQL but I wanted to try nosql / document orientated. It has been fantastic - it's hard to believe productions like these are free for all.

I love how MongoDB integrates with code in such a way that queries look just like any other code. I also love how the documents from MongoDB are returned natively to Node.JS as objects without any mapping at all. MongoDB is an intuitive and very performant database, I recommend trying it if you haven't.

The big thing with MongoDB you have to get your head around is the whole redundant data thing - it is usually recommended that you store a lot of redundant data in your documents instead of having data normalised with relationships as you would in a relational DB. Have redundant data can make your query code really simple, but it can make your update code very complex / cumbersome. However, you can opt out of this approach and use MongoDB with relationships - I would recommend using Mongoose (an npm package) that will handle a lot of the work with relationships for you.

Generally positive, but if I run into issues, I just fall back to the command line.

It's a very pretty interface, and it's easy to connect to the Database instances that are available on my workstation. There are lots of wizards and buttons that make it easy to structure queries and get to the data I'm looking for. There's a lot of community docs around the tool.

It's difficult to execute a console script, or import data if it came from another install. There are some things you'd expect to be able to do, that don't work they advertise them. Deep data objects often cause problems while importing them, and I usu. just get frustrated and get Robo3T and move on.I don't like how they're kind of obfuscating what's going on underneath the query. I wish there were more verbose output on what's happening during execution.

The first decision about whether to use MongoDB or not is whether you need a relational or non-relational DB. Once you decide a non-relational is best for you project, then MongoDB is a solid choice. It has the ongoing support of a professional team and is widely used in the market, especially for projects utilizing the MEAN stack. This makes it easier to deploy than other solutions.

MongoDB is incredibly easy to set up and use. The fact that non-relational DBs are better for more unstructured data, makes it so that you don't necessarily have to know exactly what the end state is going to be before building your schema. Its data throughput is also a key differentiator, so anything with Big Data is going to be a good fit for a non-relational DB and MongoDB, in particular. Finally, the team behind MongoDB is constantly improving the product and releasing updates, and there are several good data viewers in the market, including one from Mongo, for viewing the data and creating queries.

Creating metrics dashboards can be challenging due to the potential need for JOIN queries in your data. Anything that contains these will be harder to aggregate. Their aggregation framework can be hard to use and limiting for certain requirements.

According to my experience with MongoDB it is good NoSQL database. It has good query capacity, Also when we going to solve some business problems some requirements are change when we build the system. At that time we have to change same database collections. MongoDB support it.

Mongo DB is non relational database (NoSQL). It is a document database. it has good query capacity. Very easy to install and setup Mongo DB. Also Mongo DB is schema-free, there fore our software (code) defines the schema. It support BSON data format, there fore no complex to write code with mongo db (most of programming languages support BSON data format. We can index the mongo collections and increase the query performance

When we need to query using two mongo collections (join two collection), we can use lookup feature. but the problem is Mongo DB lookup feature is slow. Also we have no option to run query among two or three collections like as Elastic Search. When we using Elastic Search we can run query among two or three indexes(collections). There for in Elastic Search we can move historical data to separate collections and improve query performance. if we need query with historical, Elastic Search support it. But using Mongo DB we have to handle it in programming side.

Because the data storage is in JSON-like documents, it is very easy to understand and visualize. Used in class, and currently implementing an app for a physical therapist that tracks clients exercises and progress. I like the fact that the storage is encrypted.

I was introduced to MongoDB in a bootcamp. It is free and opensource. I wondered how good free could be. In my opinion it beat firebase. Objects can be defined graphically or via your code. It is easy to interface with Postman. Commercial upgrades are available. Currently using Mongo for an app. Supports Node.js. there is a lot of documentation available for beginners.

Not suitable for complex transactions. An occasional backup is recommended. After a crash, you need to check your logs. In fact, you may not know it crashed until you check your logs. With a crash or reset documents may fall into an "unconfirmed" category and are lost. This may have bee corrected in more current versions. I don't know.

It's straightforward to set up and offers multiple SDKs for use in application projects. The documentation is also readily updated and quickly followed.

Document size and nesting is limited in MongoDB, so if you have a requirement of document size more than 16MB or needing to have document with more than 100 levels, then this may not be a good choice.

MongoDB is a NoSql DB which helped us to store the non relational data. Searching and storing capacity of this tool is really good as compared to other databases

If you are moving on it from RDBMS than you may face some issues in the starting else it is really good to go.

MongoDB is at the core of our programme, which needs pace and accuracy. It's mainly used by the tech runtime team, with other teams using aspects of the software using it implicitly. For all online and mobile applications created for our customers, I use MongoDB as a database. It satisfies all of our requirements. The service is of high quality, being dependable, convenient, and durable. It's the most up-to-date and successful platform for software development.

Change streams and graph queries are two common database principles that MongoDB has quickly embraced. For popular programming languages, there is excellent library support. Developers will find it easy to set up and configure. It's also simple to write a query. Good scalability so data can be efficiently shared and is available quickly . There are a lot of tools and help online. Since there are so many ready-made options available, it's pretty simple to set up in some conditions. If you know JSON then it going to be easy for you to use.

The user interface is a bit difficult to understand at first. They could make it easier to integrate with other NoSQL databases. When opposed to a SQL-based database, extensive aggregation queries in MongoDB can be challenging.

Certain common operations are very very easy to spin up a one-off query for and utilize the data right then and there. In that sense, there is a very lightweight feel to developing with MongoDB at the start of most projects.

The design paradigms around MongoDB make it so that you will eventually want to add some sort of schema-type check somewhere in your stack. This will make development much simpler over time as the data begins to evolve. In general, there's a lot of other scaffolding you will want to do in order to sane-ly use a NoSQL as most people seem to want to (a quick start data store for a project that would otherwise benefit from a relational database). Not saying this is the "correct way" to use MongoDB, but just our experience within a system/framework like Meteor for example.

I use it to store files to search text.

it is good to store document and unstructured data and it support transactions.

It is hard to use drivers because it serve async features because many developers have trouble understanding async functions.

MongoDB has made it very easy to rapidly develop and deploy applications that require a document-store based NoSQL database solution.

MongoDB is the most widespread solution available in terms of a NoSQL database that manages data as a document store. It provides all the best features a NoSQL database should have to offer: clean and easy setup, simple syntax, as well as advanced features such as map-reduce and scaling capabilities. The BSON document type maps beautifully with the JavaScript stack, making it simple to wire up an application with a nodejs backend and any popular frontend JavaScript framework.

MongoDB can be hard to set up to support auto-scaling environments, and the best provisioned hosting options are quite expensive.

Comparing Google to Blackboard and Canvas, the whole experience is less than ideal. I have never used an application with so little support.

It is very dependable, quick, and suitable for many different database systems. Additionally, querying is so quick that it practically matches SQL databases in speed. You may also aggregate data from the tables using its queries.

To make something helpful for certain individuals, you occasionally have to eliminate functionality. In order to do that, functionalities might be disabled.

MongoDB provides most of the features of SQL databases, if not more, like aggregation, clustering, replication, compound indexes etc. Once configured properly, it lets you quickly start saving and fetching data very easily and efficiently.

Mongo comes as an alternative to SQL databases and provides vast number of features and flexibilty as compared to SQL databases. It can be used for storing huge amounts of data as JSONs and it provides seamless integration with tech stack using Node.js.

Even though Mongo allows you to build your application without a rigid schema, it still needs to be configured and architectured properly to make sure the performance is up to the mark when data size starts increasing.

I am a javascript developer and I always prefer to use MongoDB because it is very compatible with javascript world , I have setup many relations among the collections and it is very easy for overall web and mobile development.

1. MongoDB is very compatible with javascript based technologies such as Node.js , Angularjs and Angular2+. 2. Import and Export Database is very easy. 3. MongoDB also IDE to manage Database collections. 4. Executing Database Schema is really easy. 5. Setting up relations in MongoDB is very easy as compared to traditional SQL based Databases.

You need to have a little bit knowledge of SQL and Database queries to understand the MongoDB concepts for managing DB and running its functions.

Overall experience is good, Pricing for the product is fair enough.

MongoDB provides flexibility to use any kind of JSON data. It is highly reliable and offers unique scalability.

Even though MongoDB is a power tool, It is back-breaking to setup and the learning curve could be sharp.