Amazon S3 Glacier vs. Azure Blob Storage, with Azure Data Lake Storage

If your organization has a lot of archival data that it needs to be backed up for safekeeping, where it won't be touched except in a dire emergency, Amazon Glacier is perfect. In our case, we had a client that generates many TB of video and photo data at annual events and wanted to retain ALL of it, pre- and post- edit for potential use in a future museum. Using the Snowball device, we were able to move hundreds of TB of existing media data that was previously housed on multiple Thunderbolt drives, external RAIDs, etc, in an organized manner, to Amazon Glacier. Then, we were able to setup CloudBerry Backup on their production computers to continually backup any new media that they generated during their annual events.

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In Azure, it is the storage to use, and in my view, the Blob Storage offers more, or finer-grained configuration options, than S3. So my recommendation would be to check in detail what is offered. As the Blob Storage is more or less a Microsoft exclusive product, the "interoperability" is more limited than, for example, with S3. The S3 is more widely adopted, and if you cannot exclude a migration scenario from one cloud provider to another, additional effort is needed.

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• Cheap storage of backup data.
• Can be used as a part of the entire suite of tools from Amazon, without requiring you to leave the familiar stack.

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• The data lake analytics tool is good and provides loads of computing power to speed up the processing time.
• It provides unlimited storage for structured, semi-structured or unstructured data.
• Cloud-based service and we can easily use it for ETL and ELT processes.

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• Sometime due to slow drives there are operation failure noticed by us in testing
• Cost of restoring data is high and if you have regular restoring them it is not good option and slow as well.
• While we were setting up the system we took some support from AWS and in many cases their answers were not up to the mark.

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• study for the certifications also to have them as a reference for work when you have any questions about applying a configuration to the equipment.
• The Internet interface is simple and easy to use. Capacity is good and it's good that HP continues to innovate with this technology

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It is difficult to delete the data as you have to wait for inventory and then bucket modification has to expire.

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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.

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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.

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Since the rest of our infrastructure is in Amazon AWS, coding for sending data to Glacier just makes sense. The others are great as well, for their specific needs and uses, but having *another* third-party software to manage, be billed for, and learn/utilize can be costly in money and time.

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Azure Premium Blob offers better latency than competitors. It works best with the Azure ecosystem, and competitors lack it. Azure Blob even stands out in storage durability, providing up to 16 nines. It can have various use cases that can suit all the organisation's needs. The Azure Blob solution can also be deployed on-premises.

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• We seldom need to access our data in Glacier; this means that it is a fraction of the cost of S3, including the infrequent-access storage class.
• Transitioning data to Glacier is managed by AWS. We don't need our engineers to build or maintain log pipelines.
• Configuring lifecycle policies for S3 and Glacier is simple; it takes our engineers very little time, and there is little risk of errant configuration.

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• Data Migration projects from relational sources to Azure Data Lake Storage have given a great ROI, thanks to the less running costs, and High availability
• Pretty easy to work with in terms of Managing and accessing Data in containerized fashion.
• Further features like Archival of data which is accessed less frequently can significantly reduce cost

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