MongoDB vs. Valentina

If asked by a colleague I would highly recommend MongoDB. MongoDB provides incredible flexibility and is quick and easy to set up. It also provides extensive documentation which is very useful for someone new to the tool. Though I've used it for years and still referenced the docs often. From my experience and the use cases I've worked on, I'd suggest using it anywhere that needs a fast, efficient storage space for non-relational data. If a relational database is needed then another tool would be more apt.

Incentivized

If you have to manage a lot of different databases from different vendors, you could do well by standardizing on this product. It checks all the boxes for a proper database management studio. You would only have to learn one product to manage them all. However, if you are looking to find a good product for a single database vendor, you might be better off finding a tool that was designed specifically for that vendor product.

Incentivized

• Being a JSON language optimizes the response time of a query, you can directly build a query logic from the same service
• You can install a local, database-based environment rather than the non-relational real-time bases such a firebase does not allow, the local environment is paramount since you can work without relying on the internet.
• Forming collections in Mango is relatively simple, you do not need to know of query to work with it, since it has a simple graphic environment that allows you to manage databases for those who are not experts in console management.

Incentivized

• Foreign Key Constraint data viewer! This was a tremendously helpful feature that not many other products have. Just pick a constraint and see the data in the child tables! No setup required!
• Supports any and every database imaginable.

Incentivized

• An aggregate pipeline can be a bit overwhelming as a newcomer.
• There's still no real concept of joins with references/foreign keys, although the aggregate framework has a feature that is close.
• Database management/dev ops can still be time-consuming if rolling your own deployments. (Thankfully there are plenty of providers like Compose or even MongoDB's own Atlas that helps take care of the nitty-gritty.

Incentivized

• Somewhat laggy performance. Boot up speed was on the slow side and connecting to our database servers was also a little slower than other products.
• User interface can be a little clunky. Instead of the usual tree view of servers, databases, and schemas, you are presented with lists that you click on and get new windows to pick the new list of data from. Not organized efficiently.

Incentivized

I am looking forward to increasing our SaaS subscriptions such that I get to experience global replica sets, working in reads from secondaries, and what not. Can't wait to be able to exploit some of the power that the "Big Boys" use MongoDB for.

Incentivized

NoSQL database systems such as MongoDB lack graphical interfaces by default and therefore to improve usability it is necessary to install third-party applications to see more visually the schemas and stored documents. In addition, these tools also allow us to visualize the commands to be executed for each operation.

Incentivized

Finding support from local companies can be difficult. There were times when the local company could not find a solution and we reached a solution by getting support globally. If a good local company is found, it will overcome all your problems with its global 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

We have [measured] the speed in reading/write operations in high load and finally select the winner = MongoDBWe have [not] too much data but in case there will be 10 [times] more we need Cassandra. Cassandra's storage engine provides constant-time writes no matter how big your data set grows. For analytics, MongoDB provides a custom map/reduce implementation; Cassandra provides native Hadoop support.

Compared to Microsoft's SQL Management Studio, Valentina studio was comparable, just harder to get used to in the UI department. It ran slightly slower than other products but did save some time with neat features they baked into the product. However, in using DBeaver Community and DBForge Studio for PostgreSQL from Devart, we found different products that the team ultimately decided to use. DBeaver has all of the features we needed most (minus the constraint data viewer) and a more intuitive UI that we were used to. DBForge Studio for PostgreSQL has a very standard Windows look and feel and lacks some features, like database/table designers, but makes up for those shortcomings in the much easier filtering and sorting options right in the data grids. You can even write a query and edit the data returned, which is something we don't see in many of these tools. Our team ultimately settled on the developers using DBeaver and the support team that needs data viewing/editing capabilities using DBForge Studio.

Incentivized

• Open Source w/ reasonable support costs have a direct, positive impact on the ROI (we moved away from large, monolithic, locked in licensing models)
• You do have to balance the necessary level of HA & DR with the number of servers required to scale up and scale out. Servers cost money - so DR & HR doesn't come for free (even though it's built into the architecture of MongoDB

• It saved time in some areas of finding child data by not having to write queries to find it.
• The user interface caused some extra training for some users and ultimately caused them to not like using it.

Incentivized