Microsoft's Blob Storage system on Azure is designed to make unstructured data available to customers anywhere through REST-based object storage.
$0.01
per GB/per month
MongoDB
Score 8.5 out of 10
N/A
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.
$0.10
million reads
Pricing
Azure Blob Storage
MongoDB
Editions & Modules
Block Blobs
$0.0081
per GB/per month
Azure Data Lake Storage
$0.0081
per GB/per month
Files
$0.058
per GB/per month
Managed Discs
$1.54
per month
Shared
$0
per month
Serverless
$0.10million reads
million reads
Dedicated
$57
per month
Offerings
Pricing Offerings
Azure Blob Storage
MongoDB
Free Trial
Yes
Yes
Free/Freemium Version
No
Yes
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
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Fully managed, global cloud database on AWS, Azure, and GCP
More Pricing Information
Community Pulse
Azure Blob Storage
MongoDB
Considered Both Products
Azure Blob Storage
No answer on this topic
MongoDB
Verified User
Employee
Chose MongoDB
Also, using DocumentDB, and both are good, each was chosen based on the developer's expertise. So take advantage of in-house skills.
Blob storage is well suited to hosting/sharing zipped files rather than several smaller files, as folder enumeration/listing is not supported. Files uploaded are case-sensitive, so users need to be educated on the correct naming convention format if they are delegating the file-sharing process outside of IT.
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.
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.
If we are transferring huge amount of data (outbound), it can get quite expensive.
With new features being added constantly, although a good thing, at times it becomes difficult to keep up with the changes. Documentation needs to keep UpToDate and should include best practices.
Performance can be improved especially when it comes to cold storage.
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.
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.
Blob storage is fairly simple, with several different options/settings that can be configured. The file explorer has enhanced its usability. Some areas could be improved, such as providing more details or stats on how many times a file has been accessed. It is an obvious choice if you're already using Azure/Entra.
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.
Microsoft has improved its customer service standpoint over the years. The ability to chat with an issue, get a callback, schedule a call or work with an architecture team(for free) is a huge plus. I can get mentorship and guidance on where to go with my environment without pushy sales tactics. This is very refreshing. Typically support can get me to where I need to be on the first contact, which is also nice.
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.
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.
Azure Blob Storage is the best choice to store files when the app runs in Azure. It also has some advantages over S3, like Shared Access Signatures, that make it easy to control access to files directly via a URL. Azure Blob Storage is very fast and we have not had any major issues with it after using it for several years.
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.
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