![]() ![]() Most vendors provide a dialect of the Structured Query Language (SQL) for retrieving and managing data. Relational databases organize data as a series of two-dimensional tables with rows and columns. Likewise, you can also learn about selecting storage tools and services. Learn more about identifying and reviewing your data service requirements for cloud adoption, in the Microsoft Cloud Adoption Framework for Azure. Then consider a particular data store within that category, based on factors such as feature set, cost, and ease of management. Generally, you should start by considering which storage model is best suited for your requirements. Data stores also support different programmatic and management interfaces. In other cases, the data storage and processing capabilities are separated, and there may be several options for processing and analysis. Sometimes this functionality is built into the data storage engine. Most data stores provide server-side functionality to query and process data. Not all data stores in a given category provide the same feature-set. But it's still useful to understand the different models at a high level. In fact, there is a general trend for so-called multi-model support, where a single database system supports several models. For example, a relational database management systems (RDBMS) may also support key/value or graph storage. ![]() Note that a particular data store technology may support multiple storage models. This article describes several of the most common storage models. Data stores are often categorized by how they structure data and the types of operations they support. There are literally hundreds of implementations to choose from among SQL and NoSQL databases. Selecting the right data store for your requirements is a key design decision. Therefore, it's important to understand the main storage models and their tradeoffs. The term polyglot persistence is used to describe solutions that use a mix of data store technologies. Instead, it's often better to store different types of data in different data stores, each focused toward a specific workload or usage pattern. This heterogeneity means that a single data store is usually not the best approach. Modern business systems manage increasingly large volumes of heterogeneous data. ![]()
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