Microsoft Azure Object Storage Reviews

I have been working with this solution for more than one year, focusing on infrastructure. My goal was to provide tools to developers and my team to help them utilize the Azure platform effectively. 

Specifically, I aimed to deploy products like Kubernetes servers on Azure, which includes blob storage, file storage, and table storage for my clients.

It's similar to S3 for AWS. The ability to store everything inside Blob or Object storage and use it for archiving data is beneficial. For example, you can transform data from relational databases, flatten it, and store it in Object storage to save space within the databases. 

If I want to save data and do not require legacy access, it's a good solution, for instance, to migrate archives from databases to Object storage. This can also be used for business intelligence purposes. Having a storage solution for data makes it the best place to store it. Azure has its own solution for StatsQ Web Apps.

We have two types of storage that we are primarily using: Blob storage and file storage. File storage is mostly used for structured data that is consumable by users directly, such as file reports and Excel files. Blob Storage is mainly for binary data, bigger objects, and unorganized data that we manage.

Due to the ease Microsoft Azure Object Storage provides, we don't need to procure the hardware. It gives scalability, even though it is manual, so we don't worry about procuring the hardware again when we need to increase the size. It helps save costs.

We use it for a lot of different use cases. Besides general backup and data retention, we use it for various analytics purposes. We build custom analytics solutions and use Blob Storage for configuration and OTA software updates for each solution. 

On the consulting side with AWS, we store custom objects related to training in S3 buckets. All of this is managed and controlled through relational databases.

We use it more for colder storage compared to Cosmos DB or DocumentDB.

We design and build custom solutions. We mostly use Azure for our cloud and go straight through. If we don't use the customer's network, we use cell routers.

I design and build industrial IoT equipment, so that's part of what we do.

Cost is the most valuable feature. Otherwise, I'd be using a NoSQL database. 

Blob Storage is also much better suited for large files like videos and anything in the multi-gigabyte range. Different services access it in different ways, but none are particularly harder than the others.

I think it is not an improvement from the tool, but we were able to arrange our layers on the data lake using objects and a best practice for speeding them up or for making partitions in objects to protect against security vulnerabilities and increase the performance of data access. I don't think there are improvements in our company from the use of the tool. It is just the best practice that we are using in our company.

Microsoft Azure Object Storage makes some valuable compliance certifications for healthcare, and we need to obey the compliance part. In the healthcare sector, we have HIPAA, and in Brazil, we have LDPD compliance, which is similar to GDPR compliance. When we have to make proof of data access, proof of deletion, or datasheet reading, we can do it all easily with Azure.

We use it basically for binary or any text data that needs to be there. Or if we want to solve something like data redundancy problems.

And some image processing. We have also done some image processing in one of the POCs. So that's one of the use cases for us.

The index search and block storage are valuable features.

We use Microsoft Azure Object Storage for file sharing.

Microsoft Azure Object Storage is flexible and has all the features we need.

Microsoft Azure Object Storage offers hassle-free usage. It is easy to configure. 

The product is expensive.

We use Microsoft Azure Object Storage for our data streams and logs. We bring all our live data streams and store them in Microsoft Azure Object Storage. When the files are added to the folder, the Lambda series will kick in, and then we will run and train the models with new data and publish the results. The outputs will be written to Snowflake.

What I like most about Microsoft Azure Object Storage is that you have many ways to access it.