MongoDB Reviews & Product Details

Fantastic key notes. Real world use cases. Great Technology Great arrangement and awesome food.

Expected more technical details but I understand time does not permit everything.

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the show is amazing. Im very excited to stay here

the experience is really good, it don't have any complaint.

social projects

It has reduced my calls in the middle of the night. The rep server redundancy makes things low stress.

We would have liked an on-site consultant to verify our production deployment prior to go-live.

We are currently implementing a document store which will allow searches across multiple types of docements.

I like the flexible schema. This allows developers to keep moving without all the riggor to get a new column added in production.

Flexibility has gotten us into trouble also. Developers are now just adding fields without modeling which in 10 years, this might become a problem when we try to do impact analysis

Document storage for admin system

- The flexible schema - db features like: aggregate, lookup, graph

To find something in the documentation takes me time. I also find it difficult to find in-depth articles on indexing, sharding etc.

Building modern day micro-services based, API centric solutions for our clients. We build an ERP system on nodejs and mongo and now we are building a travel and booking platform.

networking and otheruseful opportunities

would be easier to limit session availability by having pre-registration :)

launching websites, realize that javascript and mongodb go together perfectly

Being schema-less provides lot of advantage in the agile atmosphere

Lack of open source IDE available, need to expose more of the paid tools to be available with minimal options in the community edition too.

Log Analytics, Geo-information systems, and health data metrics analysis. Geo-location capabilities of mongodb are great out-of-box.

It is very Flexible and easy for Java users.

learning to query without sql was difficult

Using it for mass data storage and analytics. Faster performance than oracle

The flexibility of document focused databases makes it easy to change or update schemas, hold data with varying sample rates or different, non-subset fields.

High performance drivers to native data structures in other programming languages. Specifically, if I want to store time series data in Mongo, then retrieve in Python, the list of queries has to be iterated through to pull out the individual data fields. Some third party solutions provide a better solution.

Data storage for IoT applications. SQL is certainly popular with business insight applications, but rolling out new and developing products in a start up meant we could not future proof our data collection up front when working with SQL databases.

MongoDB is really very good NoSQL database. It is simple but powerful. It is quick on large datasets and simple to retrieve data. We use it mainly for logs and statistics data. Where our SQL-based database it uneffective Mongo comes and helps us.

If you newer worked with NoSQL DBs it would be a little bit unusually to use it but perfect documentation and simple (JS-based) query language helps you to start quick.

MongoDB is used for statistics and logs data. Store that in SQL DB is very resourse expensive and uneffective.

MongoDB is very easy to learn and the BSON format make is super clear to read and interact with. The most important for us was the sharding feature and the fact we could take advantage of atomic operations.

It's not a general purpose DB and you need to plan heavily before creating new db schema.

We were have a lot of performance problems with our write-heavy application and MongoDB helped us save all those problems. The installation and maintenance was also very straight-forward and we were able to pick it up with ease from the online documentation.

Easiest document database with real querying capabilities with map and reduce built in.

Update queries get really slow and database performance starts falling linearly with the size of database

Workflow management with jobs and task tracking. Product catalog with the evolving schema of products.

MongoDB is a versatile NoSQL database, that requires limited training to get started. Querying the database doesn't involve writing complex code, but simply sending a JSON filter.

Setting up a production system can be rather expensive for small systems. Setup and maintenance can also be rather complicated without paid tools.

MongoDB has a dynamic schema. This give flexibility to the application, making it possible to easily create backward- and forward compatible versions. The schema does allow the creation of indexes, to increase performance.

We've used mongo in three different projects: in to of them for data serialization and for schema analysis for the other one. The query API is powerful and expressive and object serialization (with Morphia, I am sure there are other great frameworks) is seamless and with minimal annotation overhead. MongoDB Compass tool that comes with it is fantastic for statistically analyzing the schema of arbitrarily hierarchical data.

I would probably add a bit more flexible wildcard-style query capability (i.e. when you don't know a specific name for a field, but know a little about the structure it should satisfy), but I'm being picky.

Natura language understanding. Mongo's support for heterogeneous documents has been very handy.

The ease of use. The ability to scale very easily. The great datastructure and javascript query language,

Greater speed compared to Postgree would be awesome.

Storing large different types of data, not always structured-