MongoDB is an open source document-oriented database system. It is part of the NoSQL family of database systems. Instead of storing data in tables as is done in a "classical" relational database, MongoDB stores structured data as JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster.
$0
per month
MarkLogic Server
Score 9.0 out of 10
N/A
MarkLogic Server is a multi-model database that has both NoSQL and trusted enterprise data management capabilities. The vendor states it is the most secure multi-model database, and it’s deployable in any environment. They state it is an ideal database to power a data hub.
$0.01
per MCU/per hour + 0.10 per GB/per month
Pricing
MongoDB
MarkLogic Server
Editions & Modules
Shared
$0
per month
Serverless
$0.10million reads
million reads
Dedicated
$57
per month
Low Priority Fixed
$0.01
per MCU/per hour + 0.10 per GB/per month
Standard Reserved
$0.07
per MCU/per hour + 0.10 per GB/per month
Standard On-Demand
$0.13
per MCU/per hour + 0.10 per GB/per month
Offerings
Pricing Offerings
MongoDB
MarkLogic Server
Free Trial
Yes
No
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
Fully managed, global cloud database on AWS, Azure, and GCP
—
More Pricing Information
Community Pulse
MongoDB
MarkLogic Server
Features
MongoDB
MarkLogic Server
NoSQL Databases
Comparison of NoSQL Databases features of Product A and Product B
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.
In an area where it will be built once and maintained, it shines. If you aim to use CI, temporary environments, or anything else, it is not very effective. Licensing is almost impossible on boxes that are to be created on the fly.
Easy to learn. When I picked up MongoDB for the first time, I had little background in database management or modeling. If you have a background in javascript (and JSON)... then you can figure out how to use MongoDB pretty fast.
Fast performance.
It's relatively easy to set up in certain environments because there are lots of ready-made solutions out there.
There's a lot of support in the existing ecosystem for it —, especially in the node.js realm.
Query syntax is pretty simple to grasp and utilize.
Aggregate functions are powerful.
Scaling options.
Documentation is quite good and versioned for each release.
Indexing is a major strength of MarkLogic. The out-of-the-box configuration is set up to handle a combination of text and fielded data. The indexing is also highly configurable. Those configuration options are at the heart of a lot of our high-volume, high-performance applications.
The industrial strength transactions and security are also a strength, particularly when we are dealing with user-created intellectual property.
The engineering support is a strength. They are big enough to have a really strong support and engineering staff, but small enough so that a medium-sized customer has access to it. They are very responsive to questions and problem reports.
The ability to move easily among XML and JSON is a strength.
How to do complete data profiling on documents loaded in Marklogic database?
Customers need a tools which can be customized to suit their data profiling needs but currently the tools which MarkLogic provides fall short on this requirement.
Unit testing framework which is using only XQuery as the language is lacking some features.
MongoDB is one of the most famous non-relational databases in the world, there are famous active projects that use this database. I think that the same company that develops the database gives you the online induction totally free is something that really is very positive. Accounts with a first-class support to be able to relate the correct implementation of the database, in addition to teaching you the best practices to optimize your projects, I believe that with this decision it is more than obvious which is the best decision at the time of seeing with which database to work.
Our firm has consultants for a number of technologies/disciplines. While I am capable and experienced in other areas, my preference is always to work on engagements with MarkLogic. As an architect and developer, I get far more flexibility and performance from one product instead of cobbling together a stack of several products to provide a capability that MarkLogic has rolled into one great product.
It is one of the reasons why we prefer it to store documents in a JSON-style format, to access the desired document very quickly regardless of its size, to be readable by human eyes, and to be easily scalable and manageable.
Very little about it can be done better or with greater ease. Even things that seem difficult aren't really that bad. There's multiple ways to accomplish any admin task. MarkLogic requires a fraction of administrative effort that you see with enterprise RDBMS like Oracle. MarkLogic is continually improving the tools to simplify cluster configuration and maintenance.
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.
There's always room for improvement. Some problems get solved faster than others, of course. MarkLogic's direct support is very responsive and professional. If they can't help immediately, they always have good feedback and are eager to receive information and details to work to replicate the problem. They are quick to escalate major support issues and production show-stopping problems. In addition to MarkLogic's direct support, there are several employees who are very active among the community and many questions and common issues get quick attention from helpful responses to email and StackOverflow questions.
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.
The environment I work in is somewhat unique in that we use both MySQL and MongoDB. However, each is used for specific purposes that the other is not well suited for. MongoDB is not a relational database like MySQL, so it serves as the perfect place to dump key bits of data for quick retrieval later. This is something we can't easily do with MySQL. On this smaller database, MongoDB also lets us retrieve data more quickly with its fast and efficient querying.
In comparison to both Mongo and HBase, MarkLogic wins in terms of integration to other systems, while loosing in terms of pricing. In terms of documentation all will be in same range putting MarkLogic a bit forward.
We can make more open and flexible systems due to its easy adaptation to new evolutions in web applications.
In the latest versions it offers support for different transactions and we could carry out real tests related to the concurrency of the application.
MongoDB allows you to have distributed clusters, which improves the speed of the queries by reducing the latency that exists between the database cluster and the service that executes the query.
It took longer than expected to develop our application and get the level of consistent performance necessary. As a result, profit was flat for a couple of years but the benefits are really starting to kick in.