Azure, Microsoft's cloud platform, boasts a robust set of Data Platform technologies designed to cater to a diverse range of data varieties. Let's embark on a brief exploration of the two primary types of data: structured and unstructured.
Structured Data:
In the realm of structured data, Azure leverages relational database systems such as Microsoft SQL Server, Azure SQL Database, and Azure SQL Data Warehouse. Here, data structure is meticulously defined during the design phase, taking the form of tables. This predefined structure includes the relational model, table structure, column width, and data types. However, the downside is that relational systems exhibit a certain rigidity—they respond sluggishly to changes in data requirements. Any alteration in data needs necessitates a corresponding modification in the structural database.
For instance, adding new columns might demand a bulk update of all existing records to seamlessly integrate the new information throughout the table. These relational systems commonly employ querying languages like Transact-SQL (T-SQL).
Unstructured Data:
Contrary to the structured paradigm, unstructured data finds its home in non-relational systems, often dubbed NoSQL systems. Here, data structure is not predetermined during design; rather, raw data is loaded without a predefined structure. The actual structure only takes shape when the data is read. This flexibility allows the same source data to be utilized for diverse outputs.
Unstructured data includes binary, audio, and image files, and NoSQL systems can also handle semi-structured data such as JSON file formats. The open-source landscape presents four primary types of NoSQL databases:
Key-Value Store: Stores data in key-value pairs within a table structure.
Document Database: Associates documents with metadata, facilitating efficient document searches.
Graph Database: Identifies relationships between data points using a structure composed of vertices and edges.
Column Database: Stores data based on columns rather than rows, providing runtime-defined columns for flexible data retrieval.
Next Steps: Common Data Platform Technologies
Having reviewed these data types, the logical next step is to explore common data platform technologies that empower the storage, processing, and querying of both structured and unstructured data. Stay tuned for a closer look at the tools and solutions Azure offers in this dynamic landscape.
In subsequent posts, we will delve into the practical aspects of utilizing Azure Data Platform technologies to harness the full potential of structured and unstructured data. Stay connected for an insightful journey into the heart of Azure's data prowess.
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