第8章データ管理

While XML data are hierarchical, relational data are represented in a model of relationships. XML data are self-describing, but relational data are not. XML data have inherent ordering, but relational data do not. RDBMS only supports atomic data types such as integer, string, and date. XML DTDs do not support atomic data types. XML Schema supports both atomic and aggregated types (aggregated types modeled in object-relational databases using user-defined types). XML data are semistructured (can include certain anomalies, and a change to DTD or XSD necessitates regeneration of tables). For example, suppose a banking application needs to transfer money from one account to another. The transaction may involve multiple steps, such as deducting the amount from the sender's account and adding it to the recipient's account. If any of these steps fail, the entire transaction should be rolled back to ensure that the database remains consistent. Atomicity ensures that the transaction either completes successfully and all the changes are committed, or it fails and none of the changes are committed, leaving the database in a consistent state.

An ontology is semantic data used to describe entities of the real world and the relationship between the entities using the Web Ontology Language (OWL) (Figure 8.16). To convert and map XML data into relational databases, we can use table-based mapping, which copies data from an external source such as XML into the selected table. A database schema is the structure of a database that defines how data are organized within a database. Schema-oblivious mapping stores both OWL class and instance data in a single table, which allows files to be easily parsed and loaded into the database. Schema-aware mapping maps the OWL schema into corresponding tables and then loads the instance data into the selected tables.

IT solutions can help to address a range of security issues that organizations may face. Some common IT solutions for security issues include encryption and antivirus software.

Given the diversity of nonrelational database management systems and the fact that multiple systems may sometimes be used as part of the same solution, what are possible security issues that need to be considered?

Unstructured data are data that are not arranged according to the data model and cannot be stored in a traditional relational database. NoSQL databases are schema-agnostic and provide the flexibility needed to store and manipulate large volumes of unstructured and semistructured data. Users do not need to know what types of data are stored during setup, and the system can accommodate changes in data types and schema. Designed to distribute data across different nodes, NoSQL databases are generally more horizontally scalable and fault-tolerant. MongoDB Atlas is an example of a NoSQL database system that is fully managed and operates on the cloud.

NoSQL DBMS is not ACID compliant and data consistency is not guaranteed. NoSQL DBMSs provide eventual consistency instead: when old data are getting overwritten, results that are a little wrong are temporarily returned. For example, Google’s search engine index cannot overwrite its data while people are simultaneously searching a given term so it does not give the most up-to-date results during a search, but it gives the best answer it can. While this setup does not work in situations where data consistency is necessary, such as financial transactions, it is just fine for tasks that require speed rather than pinpoint accuracy.

A key-value store is a simple database that uses an associative array such as Redis, DynamoDB, and Cosmos DB. It stores only key-value pairs, provides basic functionality for retrieving the value associated with a known key, and works best with a simple database schema.