MongoDB Reviews & Product Details

Ease of installation, clarity of documentation, tutorials and tech notes.

all has been pleasing so far, typically patching and new features are difficult, but not here.

Distribution of massive data sets to knowledge workers efficiently and securely.

Powerful aggregation tools, great documentation. GridFs is one of my favorite features. I'm amazed at it's speed and ease of use.

Some non-obvious syntax with variables in pipelines. Operations on arrays of sub-documents can be confusing.

Fast data retrieval and flexible data models are quite important to us. Our software is used on assembly lines and those two details are key to our customer's success. MongoDB has been a perfect fit in those respects. Uptime is also quite important to us, so the ability to replicate and fail over in Mongo is also exactly what we need.

Extremely performant and easy to use. You don't need to be a developer to view and understand the data structure.

Need more around best practices in structuring data.

Segregation and performance of large data sets (data marts and data warehouses). With MongoDB, it is really easy to integrate with front end visualizations for performance. The reactivity is great!

Ease of implementation, deployment and fantastic performance at price point.

Waiting for MongoDB 4.0 and ACID transactions!

We were looking for light weight schema-less data store for docker container deployments. We have seen the performance / low latency we desired at an excellent price point.

JSON like usage. Just storing/retrieving JSONs is huge for our SPA, as it just stores the objects from the front end straight to the database and back.

Lack of relationships. Comming from a RDBMS world is hard to envision everything as isolated objects.

Faster response times

easy administration of the database as well.

I don't have any complain of the product

sensor data

The ease of operations, the ease of development and how well it fit into our agile IT org and our co timeous delivery model.

Non-ACID compliant which has limited MongoDB use cases that might have otherwise fit.

Data from multiple sources that is slightly different, in a relational DB, the data model would become unwieldy quiet fast, but MongoDB was not o my more performant, but also allowed us to realize storage savings through compression.

Unlike SQL databases, MongoDB's document model makes it very easy to develop new software ideas in Java and then making them persistent. Using the Morphia open-source library, we can easily map our Java classes and persist them in MongoDB in the same 'shape' making it very intuitive to understand and optimize the data design.

Coming from an SQL world, there is necessarily a bunch of new terminology and technology to understand. All of my previous experience about optimizing SQL databases no longer applies.

We have been developing translation memory systems for internationalizing e-commerce websites in real time. The enormous amount of textual data was overloading MySQL. Changing the design of the system - adding a fields or changing an index - was literally taking days of 100% CPU and made changing the production system a nightmare. With MongoDB we can quickly iterate, add new fields - change the 'shape' of the documents in the live system, test from our development environments - all without affecting the behaviour or performance of the live system.

the show is amazing. Im very excited to stay here

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

social projects

non traditional way to maintain and process data, which is faster if designed well schematically

new format means we need to dedicate some time to understand to maintain data in document formats.

Migrate to fetch data faster.

The speaker lounge and interacting with fellow mongonese.

I dislike that fact that the conference is not longer.

Scaling and performance issues.

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.

Mainframe Offloading Data Lake Cloud Strategy Micro Services Innovation

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.

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

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

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 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.

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.