SQL language (meaning Structured Query Language by the way) can then be used to query that data. A good example is a relational database (RDBMS) where the data gets stored in an organized way like names, birth dates, bank account numbers, you name it. Structured data (sometimes referred to as quantitative data) refers to data that is well organized and can be easily manipulated, the kind of data that would look good in an Excel spreadsheet. Let’s take a look at it from a different perspective. Now if you aren’t familiar with this space, this definition won’t help much in understanding and this is completely normal. “Unstructured data is information that is not arranged according to a pre-set data model or schema, and therefore cannot be stored in a traditional relational database or RDBMS.” But first, let’s start with MongoDB’s definition of it. Note that the majority of data generated by organizations is unstructured with growing volumes. In order to understand Azure Blob storage, it is best to understand the term “unstructured data” first as it is the crux of this storage type. Now with all that said, in this article we will have a look at the first two options, Azure Blob storage vs Azure File storage. Azure Disks: Block-level storage volumes for Azure VMs. Azure Tables: A NoSQL store for schema-less storage of structured flexible data using a lot of metadata.Azure Queues: A messaging store messaging between applications to create a backlog of work to process asynchronously.Azure File storage: Managed file shares usable by cloud and on-premises environments.
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