What is Database Management and DBMS?
What is database management?
Database management is not a singular entity; rather, it’s a series of actions, often facilitated by a database management system (DBMS) designed to efficiently store, organize, and manipulate business data throughout its lifecycle. As data volumes grow, businesses rely on DBMS solutions and database management to prevent poor application performance and reduce any impact on availability, compliance and security.
There are several techniques and actions under the umbrella of “database management” that a business can take to reduce or prevent the negative impacts of exponential, uncontrolled data growth.
Below is a list of some common, protective database management tasks:
Monitoring the performance of applications and their data and tuning as needed
Planning for storage and capacity growth requirements
Establishing a powerful backup and disaster recovery solution
Archiving, partitioning, replicating, and masking data
What is a database management system (DBMS)?
Whereas database management is a series of best practices, a database management system (DBMS) refers to a software-defined system that manages databases and their data.
Common database management systems include:
With a DBMS, users have control over the data in a database and are able to read, update, create, and delete data. A database management system behaves as an interface, offering end users access to their databases and enabling them to organize and access the data as needed.
A database management system enables users, such as database administrators, to manage the data, to manage the engine that allows users to access the data within the database, and to manage what is known as the database schema, the organizational structure of a database. Together, a DBMS delivers security and ensures data integrity, but some systems are even used to deliver automated rollbacks and restarts, logs, and audit activity within a database.
Database management systems can be categorized into various types:
A centralized system where all the data lives in a single location, and users can access that location to manipulate the data.
A distributed system where the data lives in various nodes.
A federated system is able to provision data without source data duplication. This category can be further split into two subcategories:
A loosely coupled database requires access to other component databases.
A tightly coupled database uses independent processes to work into a federated system.
Blockchain database systems manage transactions, financial and otherwise.
Database management systems vs databases: what’s the difference?
A database is a store of information that is collected and used by organizations for many different purposes. A DBMS is the software that allows organizations to manage, access, and modify that stored database information. The terms can sometimes be used interchangeably because organizations typically don’t have one without the other.
Database management system vs data warehouse vs data lake
Storing data is the objective of databases, data warehouses, and data lakes alike. But they are not the same thing.
A simple way to differentiate the three terms is as follows:
Databases store current information that applications and employees need to do their day-to-day business.
Data warehouses store both current and historical relational information from a variety of systems and sources, which is most often used for reporting, data analysis, and business intelligence.
Data lakes store current and historical information from a variety of systems like data warehouses, but it’s typically stored in its raw, unindexed, unprepped form. It’s most often used for big data analysis projects that require lots of data, such as artificial intelligence (AI) and machine learning projects and real-time analytics.
How does database management work?
Put simply, a database manager and their team are responsible for ensuring the health and performance of a business’s databases. Using best practices like the ones outlined above ensures organizational databases run as they should. However, operating with a database management system (DBMS) provides much-needed control and visibility.
In a nutshell, a database management system works as follows: It organizes your database files and provides end users more access and control over their data. To accomplish this, A DBMS allows users to manipulate the data in their database files, including creating, editing, and updating it when needed. From there, a DBMS can store and retrieve said data from the database.
5 main factors of a DBMS:
Granting multiple users concurrent access to a single database.
Establishing and maintaining security rules and user access rights.
Backing up data regularly and recovering it quickly should a breach occur.
Setting up database rules and standards to protect data integrity.
Providing “dictionary” definitions and descriptions of available data.
Of course, a DBMS does not replace a database manager or a database administrator. These specialists ensure the database structure runs as it should, and they can create user permissions to control who has access to the data. Together, a DBMS and a specialist can ensure quality control over the data.
Components of a database management system
A DBMS is typically a suite of software made up of various integrated components. Together, they form a system that enables organizations to efficiently and easily create, access, and modify data in databases.
Most DBMSes include the following:
Storage engine - As the core component of a DBMS, this stores the data. It’s the part of the system that communicates with the file system at the OS level. It’s the gateway for all the SQL queries that interact with the stored data.
System catalog or database dictionary - Also called the metadata catalog, this component is a centralized repository for all created database objects. It is used to confirm data requests from users and also to provide information about a database’s objects, security, performance, and more.
Database access language - every DBMS needs an application programming interface (API) to enable users to create databases and access data, and it usually comes in the form of a database access language. For instance, structured query language (SQL) is the default data access language in relational databases.
Optimization engine - This component processes data requests and transforms them into actionable commands. It also helps tune databases for optimal performance.
Query processor - Once a query (data request) has gone through the optimization engine, the query processor handles the request and feeds back the results. It acts as a sort of middleman between the database and user queries.
Lock manager - This component keeps multiple users from modifying the same data at the same time. It locks access for each user in turn.
Log manager - All DBMSes keep records of how and when data in the database is modified, created, or deleted. The log manager records this information and can also integrate with database utilities to recover data or make backups. It manages the logs by organizing them and keeping them easily accessible.
Data utilities - This category is an umbrella term for a variety of components that simplify database management and monitor activity. They can include software for data backup and restore, integrity checks, reporting and monitoring, simple repair, validations, and so on.
DBMS examples
There are many types and examples of DBMS tools and they differ most strikingly in the way they structure data. Common types of database models include relational, hierarchical, document-oriented, network-oriented, and object-oriented. It follows that a DBMS would need to match the model type and information structure. So here are the most common types of DBMS solutions:
RDBMS - Relational databases are the most widely used today and can adapt to the most use cases. They’re also sometimes called SQL DBMSes. RDMBSes organize structured data into rows with clearly defined relationships represented by values in columns. Examples of this type of DBMS include Oracle, MySQL, Microsoft SQL Server, and the open-source PostgreSQL.
NoSQL DBMS - A lot of today’s data is unstructured, which means it doesn’t fall neatly into rows and columns of an RDBMS spreadsheet. There are several types of NoSQL DBMS:
Document DBMS (DoDBMS) - These are mostly used for databases that store data in JSON document formats without clear relationships. MongoDB and Azure Cosmos DB are common examples of this type.
Columnar DBMS (CDBMS) - Also referred to as wide-column stores, this type manages databases that are more oriented to columns instead of rows to store data, which enables high performance. Examples of this type are Apache HBase and Apache Cassandra.
Graph-oriented DBMS - This type of NoSQL DBMS organizes data into nodes that depict relationships. Examples include GraphDB and Neo4j.
Key-value DBMS - This type manages key-value databases that store data by pairing a value with a unique key. It’s a simple model that can be highly scalable. Examples include Memcached and Redis.
NewSQL DBMS - This type is a sort of hybrid of RDBMS and NoSQL DBMS. It uses SQL but delivers the performance and scalability of NoSQL DBMSes. In addition, they help ensure data consistency through support for ACID transaction properties (atomicity, consistency, isolation, and durability). Examples include Volt Active Data, NuoDB, Google Cloud Spanner, and CockroachDB.
In-memory DBMS (IMDBMS) - This type of DBMS enables users to store, manage, and manipulate data by relying on the system’s main memory. It decreases latency by directly accessing memory and can provide speedier response times and increased performance. One disadvantage is that it consumes more resources than other types. Examples include SAP HANA and Redis.
Multimodel DBMS - This type allows organizations to select the DBMS model that is best for their requirements. One example is IBM Db2, a relational database that includes a columnar option. Some of the most commonly used DBMS employ add-ons to make them multimodal, such as MongoDB, Oracle, and PostgreSQL.
Why businesses need database management
The proliferation of data shows no signs of slowing down. As a result, businesses are investing in database management tasks, database managers, and database management systems to do the following:
- Keep business operations running as planned
- Keep track of customers, data inventory, and employees
- Maintain application and database performance
- Store and organize unique, varied types of data
- Automate database processes and procedures
Specifically, the benefits of a database management system are hard to ignore, especially as the amount of data people and machines create every day increases. One of the more notable benefits is improving the security of the data stored in a database. With DBMS, businesses can increase their access to data and help end users throughout the organizations share the data. As a result, these end users can deliver faster sales and make quicker decisions as they have access to the exact data they need.
Furthermore, with a database management system, businesses can eliminate the problems that occur with data inconsistency, which happens if multiple forms of the same data exist in different locations. A DBMS provides organizations a complete, clear view into the way data is shared, ensuring there aren’t unnecessary copies of data. Not to mention, A DBMS allows organizations to enforce data security and privacy policies to reduce the risk of a data breach.
Finally, end users will be empowered to make informed decisions based on the data available. Better, more consistent data means high-quality, usable information that can help users make decisions with the exact data they need. Ultimately, this results in improved productivity throughout the organization.
Database management systems advantages
As the volumes of data that need to be managed continue to grow across industries, organizations can realize many benefits of using a database management system. These benefits include:
Increased data integrity and accuracy - Enforcement of data validation rules and constraints help maintain accurate and consistent data.
Enhanced data security - Access controls, authentication, and encryption protect sensitive information.
Data abstraction and independence - Developers and users can interact with data without needing to know complex internal details. They can also make changes to the database structure without affecting applications that use that data.
More efficient data access - Sophisticated indexing and query optimization makes data retrieval faster.
Multi-user access and concurrency control - Multiple users can access the data at the same time without fear of creating inconsistencies.
Backup and recovery - Built-in capabilities back up data regularly and recover it in the event of failure.
Reduced data redundancy - Centralized control greatly reduces duplication of data, which helps maintain data consistency and also optimizes cloud storage resources.
More advanced data modeling and relationships - DBMSes enable complex data relationships and advanced queries (especially in relational DBMSes).
Database management systems potential disadvantages
While these systems offer a wide range of benefits, they can also present a few challenges. These can include:
Increased costs - Licensing, hardware, and staffing costs can get expensive, especially for enterprise-grade systems.
More complex deployment and maintenance - Setup, tuning, and administration of some systems can require significant technical expertise. A DBMS also needs regular maintenance, including backups, updates, and monitoring, to run smoothly.
Greater performance overhead - The built-in security, abstraction, and integrity checks inherent in a DBMS can sometimes negatively affect performance compared to simple file storage.
Scalability issues - Traditional relational DBMSes can sometimes struggle with large-scale, distributed, or unstructured data unless specifically designed or tuned to handle it.
Decreased resilience - A centralized DBMS is a single point of failure and can become a bottleneck or unless it is replicated or distributed properly.
Potential security risks - If not configured properly, a DBMS presents a very easy target for malicious actors.
Vendor lock-in - Some proprietary systems can limit flexibility and make migration difficult.
Database management system use cases
Databases are used in such a wide variety of ways that it’s fair to say that every organization has at least several use cases for employing a DBMS. Any company that stores data to access it later, or has applications that need a lot of data to operate are good use cases for a DBMS.
Here are some DBMS use cases in different industries:
Economics and finance firms store information about customer transactions, stock market trades, bonds, mortgages, and other financial products.
Telecommunications companies store information about customer calls, billing, archives, and plan features and capabilities.
Airlines store information about flight schedules, employee work histories, safety records, reservations, and maintenance.
Educational institutions store information about class schedules, student records, teacher reviews and remuneration, and class registration.
Sales and marketing organizations store information about customer meetings and sales, returns, feedback, and prospects.
Human resources firms store information about new and prospective hires, payroll, taxes, employee performance reviews, and more.
Manufacturers store information about inventory, operations, production blueprints, supply chain, and maintenance.
Streaming media platforms rely on databases to store massive volumes of media that need to be organized and accessed on demand.
Technology companies use database information to create virtual and augmented reality applications that require data-intensive AI and machine learning models.
Sports organizations store information about player statistics, team performance, game scheduling, and historical media that can be analyzed.
The future of database management
Database management is only going to grow more challenging for three main reasons.
First, organizations today are building and deploying more applications than ever before – IDC estimates there will be over 750 million cloud-native modern applications by 2025 - and virtually every application requires a database. As a result, the sheer number of databases organizations must manage is only going to grow larger and larger. The more databases an organization is running, the larger the challenge is to manage and secure them all and provide easy access for developers.
Second, the days of organizations standardizing on a single database platform are gone. Today, developers are empowered to decide for themselves what technologies they are going to use and they are increasingly looking beyond traditional commercial relational databases. They want to use open-source databases and non-relational databases, alongside traditional relational databases. That means most organizations will need to manage multiple different types of databases, adding yet more complexity. Effective database management will become more important than ever.
And third, most organizations are adopting a hybrid multicloud strategy, meaning they are or plan to run their applications and databases across multiple operating environments - some combination of on-premises data centers and private clouds, one or more public clouds, colocation facilities, and the edge. Each environment has its own operational model, requiring developers, DBAs, IT, and operations teams to learn multiple APIs and to use different tools and processes to manage their databases, increasing database management complexity even further.
How to choose a DBMS for your business
Selecting the right database management system (DBMS) depends on multiple factors, including your organization’s needs, existing infrastructure, and data management priorities. Consider the following key aspects when making your decision:
Assess your existing database – understanding your current database structure and data requirements is crucial. Each database holds unique data, so businesses should evaluate their needs before selecting a DBMS.
Centralized data management – a good DBMS provides a unified view of your data, making it easier to track where it is stored, how it operates, and how it interacts with various applications.
Application integration – ensure the DBMS can grant seamless access to data across different applications without requiring replication, improving efficiency and reducing redundancy.
Infrastructure requirements – DBMS solutions require memory, CPU, and storage resources. Businesses should assess whether their infrastructure can support these demands before implementation.
Scalability & security – as data continues to grow, especially in industries like healthcare, a DBMS should offer robust security features and the ability to scale efficiently to meet future needs.
Why Nutanix for database management?
Nutanix makes it simple to manage your most critical databases with Nutanix Database Service (NDB). NDB is a database-as-a-service offering that delivers automation and one-click simplicity to database provisioning and lifecycle management.
Designed with a rich but intuitive and user-friendly UI and CLI, you can provision, clone, refresh, and restore your databases to any point in time. It also allows you to automate patching and backups and other tedious tasks that take you away from more important work.
With NDB, you can:
Simplify database operation and management for such databases as Microsoft SQL Server, Oracle Database, PostgreSQL, MySQL, and MongoDB.
Maintain operational consistency while increasing productivity with automation of many tasks.
Retain control and maintain standards as you choose the right operating systems, database versions, and database extensions to meet your organisation’s unique needs.
Speed up software development and shorten time to market by integrating infrastructure management and developer tools for self-serve database deployment.
Manage thousands of databases across environments and locations, including the cloud, all from a single control plane.
