データベースとは？

What is a Database? | IBM What is a database? Authors Matthew Kosinski Staff Editor IBM Think What is a database? A database is a digital repository for storing, managing and securing organized collections of data. Different types of databases store data in different ways. For example, relational databases store it in defined tables with rows and columns, while nonrelational databases can store it as a variety of data structures, including key-value pairs or graphs. Organizations use these different kinds of databases to manage different types of data. Relational databases excel with structured data such as financial records. Nonrelational databases are best for unstructured data types such as text files, audio and video. Vector databases store data as vector embeddings , a format used by many generative AI applications. Businesses own large amounts of data—often measured in petabytes, or quadrillions of bits—on everything from customer transactions and product inventory to internal processes and proprietary research. This data must be organized in a coherent data architecture for users and apps to access it when they need it. Databases are foundational to building such a data architecture. They are more than a place to store information. Rather, they enable organizations to centrally manage data, enforce data integrity and security standards and facilitate data access. With the proper database systems in place, organizations can use high-quality data sets for key business initiatives, including business intelligence (BI), artificial intelligence (AI) and machine learning (ML) projects. The latest tech news, backed by expert insights Stay up to date on the most important—and intriguing—industry trends on AI, automation, data and beyond with the Think newsletter. See the IBM Privacy Statement . Thank you! You are subscribed. What a database is not People often use the term “database” rather loosely, which can cause confusion about what a database is—and what it is not. A database is a system for storing and managing data, comprising both the physical hardware on which the data is stored and the software that organizes and controls access to the data. Databases underpin much modern IT infrastructure, including websites, apps and platforms such as Amazon and Google. These services are not databases themselves, but they do rely on databases to manage information, such as product inventories or search results. It is also worth noting that Microsoft Excel is not a database, but a spreadsheet application. An Excel spreadsheet organizes data in rows and columns much like a relational database does, but that spreadsheet is a single file. Databases, however, are robust, centrally managed systems that can store many different types of data, in many different formats, while supporting more advanced queries. Types of databases Organizations use different types of databases to manage different types of data and support different applications. Some of the most common types of databases include: Navigational databases Relational databases Nonrelational databases or NoSQL databases Object-oriented databases Vector databases Cloud databases Navigational databases Navigational databases store data in sets of linked records. Users must navigate between these records to reach the data they want, hence the name. The 2 most common types of navigational databases are hierarchical databases and network databases. Hierarchical databases arrange data in a tree-like structure of parent records and child records. Each child record can have only a single parent, but parent records can have multiple children. To reach the wanted record, users must start at the top of the tree and work their way down. Network databases behave much like hierarchical databases, except they allow each child record to be linked to multiple parent records. Users must still navigate through linked records, typically by using pointers to arrive at the data they want. Navigational databases were once common, but advancements in database technology—particularly the development of the relational data model—have made them much less popular. Relational databases Relational databases store data in formatted tables of rows and columns. They are sometimes called “SQL databases” because many relational databases support the use of structured query language (SQL) to query and manipulate data. (For more information, see “Database languages”). Each table in a relational database contains information on one type of entity. For example, an organization might have a table that contains information on all its customers, plus separate tables detailing each individual customer’s purchase history. IBM scientist Edgar F. Codd developed the relational model in the 1970s. The model quickly outpaced the navigational model’s popularity because it greatly simplifies the act of retrieving data. Instead of specifying paths between records, users can use SQL statements to name the data they want. The database figures out how to retrieve the relevant records, often by using indexes instead of full-table scans to speed up the process. Relational databases also cut down on redundancy, as each datapoint needs to be stored only once. Data from different tables can be combined into a single view without needing to duplicate the data. Relational databases are some of the most common databases today. They are well suited for managing structured data sets with a standard format, such as financial transactions or user contact information. A more recent class of relational databases, called “NewSQL databases,” aims to make the relational model more scalable by adopting a distributed database architecture, that is, distributing data across multiple database servers. Learn more about relational databases Nonrelational databases or NoSQL databases “Nonrelational database” is essentially a catch-all term for any database that does not store data in a rigid format, such as a table. They are sometimes called “NoSQL databases” because they generally don’t require SQL to navigate. Nonrelational databases arose to support unstructured and semistructured data types—such as free-form text and images—that doesn’t fit neatly into relational tables. Common types of nonrelational databases include: Graph databases that store data as “nodes” (representing entities) and “edges” (representing relationships between them). Graph databases are often used to track relationships, such as the connections between users of a social networking site. Document databases store data as documents, including formats such as JSON, XML and BSON. Document databases are common in content management systems. Key-value databases store information as key-value pairs, where keys are unique identifiers (such as a digital shopping cart ID) and values are arrays of data (such as the items in the cart). Wide-column databases use rows and columns much like relational databases. The difference is that each row can have its own distinct set of columns that store different information than the other rows. Wide-column databases are often used to support data warehouses, where data must be extracted from multiple sources and centralized. Learn more about NoSQL databases Object-oriented databases Object-oriented databases, also called object databases, store data as objects in the sense of object-oriented programming. Objects are basically bundles of information and associated code. Each object represents an entity. Objects are grouped in classes and have attributes that describe their characteristics and methods that define their behavior. For example, an object in the “cat” class might have the attributes “color” and “weight,” and the methods “purr” and “hunt.” Object-oriented databases gained popularity in the 1990s alongside object-oriented programming. Relational databases can pose problems for some apps built with object-oriented languages, as data objects must be converted to tables to be stored in these databases. Object-oriented databases allow developers to avoid that problem. Vector databases Vector databases store information as arrays of numbers called “vectors,” which are clustered based on similarity. For example, a weather model might store the low, mean and high temperatures for a single day in vector form: [62, 77, 85]. Vectors can also represent complex objects such as words, images, videos and audio. This high-dimensional vector data is essential to machine learning, natural language processing (NLP) and other AI tasks. Vector databases are common in AI and ML use cases. For example, many implementations of retrieval augmented generation (RAG) frameworks—which enable large language models (LLMs) to retrieve facts from an external knowledge base—use vector databases. Learn more about vector databases Cloud databases Cloud databases are databases hosted in the cloud. Any kind of database—relational, nonrelational or otherwise—can be a cloud database. There are 2 main types of cloud databases. The first, and most basic, is a self-managed database system that runs in the cloud. The second is called database as a service (DBaaS). DBaaS is a cloud computing service that enables users to access and use database software without managing the system themselves. As the name suggests, DBaaS providers offer a suite of database services, including upgrades, backups, database security and more. Cloud databases are more scalable than on-premises databases. If an organization needs more storage space or performance starts to drop, it can spin up more resources as needed. Learn more about cloud databases Other types of databases Multimodel databases can store more than one type of data. For example, IBM® Db2® cloud database can support XML, JSON, text and spatial data in a single database instance. In-memory databases store information in a device’s main memory or RAM. Applications can typically retrieve data from RAM faster than from a traditional d