Understand the Microsoft cloud storage model. Modern business systems manage increasingly large volumes of heterogeneous data. This heterogeneity means a single data store is usually not the best approach.
Instead, it’s often better to store different types of data in different data stores, each focused on a specific workload or usage pattern.
The term polyglot persistence is used to describe solutions that use a mix of Microsoft cloud storage technologies. Therefore, it is important to understand the primary storage model and its tradeoffs.
Choosing the right data store for your requirements is a key design decision. There are hundreds of implementations to choose from between SQL and SQL databases.
Data stores are often categorized based on how the data is organized and the types of operations supported. This article describes some of the most common storage models.
Note that certain data storage technologies may support multiple storage models. For example, a relational database management system may also support key or value, or graph storage.
In fact, there is a general trend for so-called multi-model support, which is when a single database system supports multiple models.
However, it is still useful for understanding different models at a high level. Not all data stores in a particular category provide the same set of features.
How Understand The Microsoft Cloud Storage Model?
Most data stores provide server-side functionality for querying and processing data. Sometimes this functionality is built into the data storage engine.
In other cases, data storage and processing capabilities are separated, and there may be several options for processing and analysis. The data store also supports different program and management interfaces.
Generally, you should start by considering which storage model will best suit your requirements. Next consider specific data stores within that category, based on factors such as feature set, cost, and ease of management. Relationship databases organize data as a series of two-dimensional tables with rows and columns.
Most vendors provide a Dialect of Structured Query Language (SQL) for retrieving and managing data. RDBMS typically implements consistent transactional mechanisms that conform to the ACID (Atom, Consistent, Isolated, Durable) model for updating information.
RDBMS usually supports a write schema model, when the data structure is predefined, and all read or write operations must use the schema.
This model is especially useful when a strong guarantee of consistency is important when all changes are atomic, and transactions always leave data in a consistent state.
However, RDBMS generally cannot be scaled horizontally without dividing the data in some way. In addition, the data in the RDBMS must be normalized, which is not appropriate for every data set.
And the key or value store associates each Microsoft cloud storage value with a unique key. Most key or value stores only support simple query, insert, and delete operations.
To modify a value (either partially or completely), the application must overwrite the existing data for the entire value. In most implementations, reading or writing a single value is an atomic operation. Applications can store arbitrary data as a set of values.