Browsing by Subject "multi-model database"

Until recently, database management systems focused on the relational model, in which data are organized into tables with columns and rows. Relational databases are known for the widely standardized Structured Query Language (SQL), transaction processing, and strict data schema. However, with the introduction of Big Data, relational databases became too heavy for some use cases. In response, NoSQL databases were developed. The four best-known categories of NoSQL databases are key-value, document, column family, and graph databases. NoSQL databases impose fewer data consistency control measures to make processing more efficient. NoSQL databases haven’t replaced SQL databases in the industry. Many legacy applications still use SQL databases, and newer applications also often require the more strict and secure data processing of SQL databases. This is where the idea of SQL and NoSQL integration comes in. There are two mainstream approaches to combine the benefits of SQL and NoSQL databases:multi-model databases and polyglot persistence. Multi-model databases are database management systems that store and process data in multiple different data models under the same engine. Polyglot persistence refers to the principle of building a system architecture that uses different kinds of database engines to store data. Systems implementing the polyglot persistence principle are called polystores. This thesis introduces SQL and NoSQL databases and their two main integration strategies: multi-model databases and polyglot persistence. Some representative multi-model databases and polystores are introduced. In conclusion, some challenges and future research directions for multi-model databases and polyglot persistence are introduced and discussed.