What is a Database Management System (DBMS)?
What is a Database Management System (DBMS)?
A database management system (DBMS) is a software technology that manages the information in your database. A database is an electronically stored data collection that lets you read, write, delete, and update data. A DBMS makes storing data easy while increasing availability, reliability, and performance. It also provides tools to identify data correlations and perform analytics within the system as needed. A DBMS helps organizations manage small and large volumes of data in an organized manner.
What are the benefits of a database management system?
Database management systems (DBMSs) are critical for managing digital data of all kinds. Benefits include:
Keeps data organized
Users need data to be organized in a specific way to be useful. Both structured and unstructured data require specific storage and operational configurations. A database management system ensures that data is organized in a specific format and can be manipulated without worrying about database operational failure.
Gives users a structured way to manage data
A DBMS allows users to manage and manipulate data using repeatable, codified instructions without understanding how data is stored, or operations work at a lower level. Once a user knows how to use a specific DBMS, they can work with any database that uses the same underlying database type and may be able to transfer these skills to other database types.
Various integrated functionalities
As DBMSs differ, some offer broader feature sets than others. For instance, many DBMSs have built-in functionality for backups, data redundancy, data validation, and encryption of data in transit and at rest.
What are the functions a database management system performs?
A database management system is a software system that makes it easy for multiple users and applications to create, read, update, and delete data. Database administrators manage databases through specialized tools and interfaces that allow them to monitor performance, implement security measures, handle backups, optimize queries, and maintain data integrity.
Makes data more accessible
A browser-based interface in DBMSs provides users easy access to data through a web form, a direct dashboard, or a third-party distributed network. Apart from visual tools, users may also be able to access and interact with data using applications, a data manipulation language (DML), query languages, or API connections.
A DBMS also provides the tools and mechanisms to manipulate, group, aggregate, and transform that data as needed. Users can modify data dynamically, ensuring it is structured and formatted correctly for various applications.
Manages metadata
The DBMS maintains dictionaries that store metadata or data about data, such as data structures, table and column names, data types, constraints, indexes, and relationships. This enables applications to work with data using structural abstractions rather than complex coding.
Metadata management also allows you to evolve data design without breaking applications. Schema changes, such as adding, modifying, or removing rows and columns, are automatically reflected in metadata tables.
Handles backup and recovery
The DBMS simplifies the database backup process by providing an intuitive interface for managing backups and snapshots. Database administrators can store the backups in third-party locations such as cloud storage for fast recovery in case of incidents. Some DBMSs also provide automatic data backups at pre-determined intervals or continuous backups. Most offer recovery tools for full or partial restoration of databases to a previous state with minimal effort.
Provides user management controls
A DBMS allows database administrators to effectively manage database users and regulate user actions. They can configure user accounts, define data access policies, and modify restrictions to control access to underlying data.
The basic data operations are create, read, update, and delete (CRUD). The administrator can set the availability of each operation on a user, role, or group-level basis. For example, some users may only have read access to a database and cannot create or alter entries.
Many DBM systems support a data control language(DCL) to define complex access controls.
Manages performance for scale
DBMS supports tens of thousands of concurrent users by implementing several of the following:
- Index optimization to speed up query execution, reducing the need for full-table scans.
- Query optimization to analyze SQL queries and select the most efficient execution plan, minimizing I/O and processing time.
- Partitioning and sharding to distribute the workload across multiple database nodes or storage partitions, improving query response times and fault tolerance.
- Parallel query execution so tasks are processed concurrently across multiple CPUs or nodes.
- Replicating data and load balancing across multiple servers, with read and write requests distributed to maintain system responsiveness, and to prevent data loss from failures.
Many DBMS software also have built-in caching capabilities, so frequently accessed data is stored in memory to reduce the need for repeated disk access. Integrated tools allow users to further monitor, configure, and optimize their data.
How does a database management system work?
A database management system(DBMS) includes several interconnected components that provide database management, transaction processing, and querying services.
The database
A database is the core of the DBMS. It is an electronically stored, systematic data collection, including words, numbers, images, videos, and files. It has built-in rules and conditions to maintain data consistency. While a database can exist independently, manual data management is complex, especially as it scales. Hence, DBMS provides a software layer that automatically handles several common management tasks like indexing, performance optimization, and access control.
The schema
A schema defines a database's structure, including tables, columns, data types, constraints, and entity relationships. The DBMS stores schema information as metadata, allowing applications to understand and interact with data without hardcoding structures. Users can define the schema using a data definition language (DDL).
The storage manager
The storage manager is responsible for efficiently storing, retrieving, and updating data on physical devices. It handles:
- File organization and data storage structure for fast retrieval.
- Indexing and caching to speed up lookups and caching frequently accessed data.
- Data compression to reduce storage footprint while maintaining quick access.
- Backup and recovery to manage snapshots and logs to restore data in case of failure.
The transaction manager
The transaction manager controls all the rules to ensure multiple users can perform concurrent operations within the database while maintaining data integrity. It may provide compliance with ACID (Atomicity, Consistency, Isolation, Durability). It manages:
- Commit and rollback so changes are finalized or undone in case of failure.
- Locking mechanisms to prevent conflicts in multi-user environments.
- Concurrency control for handling simultaneous reads and writes without compromising data integrity.
The query engine
The query engine interprets and executes queries, allowing users to efficiently retrieve, insert, update, or delete data. It also provides a mechanism for users to create complex questions that can be answered with the data. The DBMS typically leverages a data manipulation language (DML) like SQL for queries.
Query engine functions include:
- Analyzing queries to find the most efficient execution plan.
- Fetching data using indexes, joins, and aggregate functions.
- Returning structured results in tabular or hierarchical formats.
The user interface
The interface layer allows the user to interact with the database, or through an application, business intelligence tools. Depending on the DBMS, it contains various tools for extra functionality. The software also links together and orchestrates all the other DBMS components.
What are the different types of database management systems?
Many database management system products have different underlying database types, features, and tools. Choosing one depends on the form and structure of the underlying data, performance requirements, cost constraints, scalability, integration capabilities, technical expertise, data security, and more. We share common types of DBMS below.
Relational
A relational database management system (RDBMS) contains a collection of data points with pre-defined relationships between them. Data is stored in a tabular format, with each row (record) in the table (relation) containing a list of associated values (attributes) for that entry. Each record is a singular entity with a unique primary key attribute for reference. Tables can be linked in one-to-one, one-to-many or many-to-many relationships. An example of a relational database system is Amazon Aurora.
Structured query language (SQL) is the default querying language for most relational databases. Applications use structured query language to store, update, delete, and retrieve data from a relational database management system (RDBMS).
Non-relational
A non-relational or NoSQL database does not store records in tables or have a strict schema. Instead, the data may be structured in various ways, including:
Key-value store
A key-value store is a database where every record has a unique key (e.g., name, number) with an associated value (e.g., integer, string, JSON). An example of a database management system that allows key-value stores is Amazon DynamoDB.
Document store
A document store is a modified key-value store in which each unique key has an associated document containing semi-structured data (e.g., JSON, XML, YAML). This form of database uses APIs or a querying language to extract meaning. Amazon DocumentDB for JSON is an example of a DBMS for a document store.
Graph
A graph database uses a graph-style structure with nodes (records) and edges (relationship connections to other nodes). The edges also contain attributes concerning the relationship. For instance, nodes can represent a family tree and their attributes, like name and age, and the edges between nodes show the relationship, such as the granddaughter and mother’s side. Amazon Neptune is an example of a database management system for graph databases.
Object-oriented
An object-oriented or object database stores and manages a series of object instances, each with its own set of related attributes. Objects can be linked via foreign keys, like relational databases. Amazon Aurora can be used as an object-relational database management system (ORDBMS), a hybrid of relational and object-oriented databases. PostgreSQL is another popular ORDBMS.
Hierarchical
A hierarchical database is organized hierarchically, with one parent node having many children and two or more tiers. Amazon DynamoDB can be a DBMS for hierarchical databases, and nested relationships are represented in JSON.
Time series
Time series databases store data linked to a specific timestamp. They may be relational or non-relational and often require streaming data ingestion. The DBMS required for a time series database depends on the underlying data. Amazon Timestream is an example of a time series database.
Multi-model
Multi-model DBMSs can simultaneously manage different types of databases, such as document and graph databases. They are popular because they provide users with a standard interface to manage various databases without switching software. Some multi-model DBMSs also allow users to combine queries across different database types.
What are the future trends in DBMS technology?
Advances in data and systems have led to the following advances in DBMS technology.
Generative AI
Generative AI can be used for:
- Enhancing database creation and schema design from natural language instructions
- Optimizing and debugging queries
- Complex modeling of underlying data
- Generating human-readable reports from DBMS output
- Automatic reconfiguration of underlying data storage based on identified efficiencies while maintaining data integrity
Instead of writing scripts, you can construct queries with natural language, which AI models convert to the corresponding query language.
Vector databases
Vector databases are increasingly common in large language model (LLM) customization, known as Retrieval-Augmented Generation (RAG). Adding a vector database to an LLM allows organizations to integrate their own knowledge base into preexisting LLMs quickly. In RAG, vectorization helps to more easily find relationships between similar words and phrases, increasing model accuracy and relevance.
Several specialized vector databases have emerged. Additionally, some databases integrate vector data types in their existing architecture.
Amazon Aurora PostgreSQL-Compatible Edition and Amazon RDS for PostgreSQL both support the pgvector extension. Other AWS databases like Amazon Neptune, Amazon MemoryDB, and Amazon DocumentDB also offer a managed solution for vector similarity search.
Agentic AI and Model Context Protocol (MCP) Servers
Agentic AI incorporates specific tools and resources with generative AI capabilities to perform distinct tasks or roles. AI agents can interact with one another to perform coordinated tasks, not unlike a team of workers, each with separate roles. Here, there is a need for managing agent states, goals, and decision-making processes.
Within this technology platform, a Model Context Protocol (MCP) Server is the coordinator for the AI agents, managing multiple resources. MCP Servers for database services enable easy integration of AI applications and agents with data sources and services. In agentic AI systems, MCP Servers orchestrate interactions between autonomous AI agents and associated databases, ensuring efficient data flow and processing.
Agentic AI and MCP Servers require databases that offer a scalable and reliable infrastructure capable of handling state management, complex queries, and data processing requirements from multiple autonomous agents simultaneously. When integrated as MCP Servers, databases help organizations maintain consistent performance, security, and reliability across both traditional database operations and agentic AI interactions within their infrastructure.
Several database services can be integrated with MCP Servers to enable AI agents and applications to interact with data sources using natural language queries or programmatic interfaces. The MCP Servers act as intermediaries, translating requests from AI models into appropriate database operations and queries.
Zero ETL integrations
Zero ETL refers to removing the traditional extraction, transformation, and loading phases of moving, cleaning, and combining data into databases. Instead, data is instantly replicated and rapidly ingested.
With Zero ETL, it is possible to perform near real-time queries on streaming transactional data or combine multiple types of databases quickly for machine learning operations.
Deployment options
The infrastructure and deployment style used for databases and DBMS matters for speed, scalability, availability, cost, and ease of use. Options include on-premise, cloud, edge, containerized, serverless, and hybrid deployments.
Large international database operations may choose containerized, distributed deployment configurations, while manufacturing operations may choose a combination of edge and cloud deployments. For example, Biblica migrated its Microsoft SQL Server to AWS to effectively handle high traffic levels.
How can AWS help with your DBMS requirements?
AWS Cloud Databases provides a broad selection of purpose-built database management systems for any enterprise. AWS databases support all backend tasks, such as server provisioning, patching, configuration, and backups. You can focus on application development while AWS continuously monitors and automates scaling.
For example, Amazon Aurora is a cloud-native relational database engine developed by AWS that's compatible with MySQL and PostgreSQL. It combines the speed, reliability, and ease of use of traditional enterprise databases with the simplicity and cost-effectiveness of open-source databases.
As another example, Amazon Relational Database Service (Amazon RDS) is a managed service that makes it easy to set up, operate, and scale relational DBMS in the cloud. It provides cost-efficient and resizable capacity while managing time-consuming database administration tasks, freeing you to focus on your applications and business. Amazon RDS gives you access to the capabilities of familiar database engines like PostgreSQL, MySQL, MariaDB, SQL Server, Oracle, and Db2.
AWS also has several NoSQL DBMS services to meet all your NoSQL requirements. For example:
- Amazon DynamoDB is a serverless, fully managed, key-value database service that provides consistent, single-digit millisecond performance with limitless scalability.
- Amazon DocumentDB (with MongoDB compatibility) is a fully managed, native JSON document database that makes operating critical document workloads at virtually any scale easy and cost-effective without managing infrastructure.
- Amazon Neptune is a serverless, fully managed graph database service designed for superior scalability and availability with the ability to query billions of relationships in seconds.
- Amazon Timestream is another fully managed, purpose-built time-series database engine for workloads from low-latency queries to large-scale data ingestion.
AWS Cloud Databases provide a high-performance, secure, and reliable foundation to power generative AI solutions and data-driven applications at any scale.
Get started with DBMS on AWS by creating a free account today.