Welcome back.

In this video we're diving into object storage, which is a foundational part of any multi-cloud strategy. More specifically, we're going to compare AWS S3 and Azure Blob Storage to understand how to design workloads that use both. Because while they both store massive volumes of unstructured data, like images and logs and backups, their underlying differences are key to successful multi-cloud architecture.

So first, let's look at how data is structured. In AWS, you create a bucket in a region and then upload objects into it, each with a unique key. The model is simple: objects go into buckets, and that's it.

In Azure, you start with a storage account. You create containers within the storage account, and then you store Blobs inside those containers. This hierarchy is storage account, container, and then Blob.

Understanding this difference is crucial when scripting deployments or designing permission flows across both platforms. It's also important to understand that both services use tiered storage to balance cost against access frequency.

When designing for multicloud, you have to map these tiers consistently. For your hot or frequently accessed data, the standard class in AWS maps directly to Azure's hot tier. As data becomes less frequently accessed, you could use AWS Infrequent Access, which aligns with Azure's cool and cold tiers. And of course, for long-term low-cost archival, the AWS glacier classes correspond directly to Azure's archive tier.

Getting this mapping right ensures your cost and performance expectations remain consistent, preventing surprise bills or latency issues.

As you'd expect, security is paramount, and both platforms are private by default. More specifically, access to S3 buckets is controlled via IM policies, while Azure Blob Storage primarily uses identity services like Entra ID to control access.

A critical design consideration is data consistency. Fortunately, both S3 and Blob Storage offer strong read-after-write consistency. This means that after a successful write or a delete operation, any subsequent read will immediately reflect that change. This robust guarantee simplifies application logic, as you can reliably write and then read that data without complex workarounds.

When building a multicloud application, you should treat object storage as an abstract layer. Your application logic for uploads and downloads and life cycle rules should be generic enough to work with either service. For example, you might implement a pattern that ingests raw data into S3 for processing, and then replicates the results to Blob Storage to leverage specific analytics tools in Azure.

Another use case is resilience. For example, you might store primary copies of user uploaded images in an S3 standard bucket and then replicate them to an Azure hot tier container.

So at the end of the day, the choice isn't about which service is better, but how to design for both. AWS S3 offers a mature bucket key model and a vast ecosystem, while Azure Blob Storage provides deep integration with the Azure platform and its enterprise identity services.

A successful multicloud strategy abstracts the object storage layer, maps cost and performance tiers correctly, and standardizes security to ensure your data remains portable and resilient across clouds.

AWS S3 vs. Azure Blob Storage

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