Skip to Content
Dismiss
혁신
모두를 위한 AI 비전

대규모 환경에서 데이터를 인텔리전스로 전환하는 통합된 자동화 기반의 플랫폼

자세히 알아보기
Dismiss
6월 16-18일, 라스베이거스
Pure//Accelerate® 2026

데이터의 진정한 가치를 실현하는 방법을 알아보세요.

지금 등록하기
Dismiss
2025 가트너 매직 쿼드런트 리포트
실행력 최상위, 비전 완성도 최우수 평가

에버퓨어가 실행력 부문 최상위, 비전 완성도 부문 최우수 평가를 받으며, 2025 Gartner® Magic Quadrant™ Enterprise Storage Platforms 리더로 선정됐습니다.

리포트 다운로드

DBMS vs. RDBMS

A database management system (DBMS) is any type of system that can store and retrieve data. DBMS applications can use any type of storage strategy, including files. A relational database management system (RDBMS) stores data specifically in a tabular format, mainly tables. Although RDBMS systems have strict limitations on the way they store information, they’re beneficial for organizing and querying data.

Definition of a DBMS

An RDBMS is a type of DBMS, while a DBMS is an umbrella term used to describe any system that stores data, including open source databases. Users are shown a graphical interface to edit, add, and review data. Mainframes, relational databases, and NoSQL databases are just a few examples of a DBMS. 

Files organized in a way that can be queried can also represent a DBMS. A DBMS is any storage system that organizes information in a way that can later be queried and edited. Every DBMS application has its own way of organizing data in a way that makes it efficient with performance during queries. However, not every DBMS can handle large amounts of data without suffering from speed degradation. 

Definition of an RDBMS

An RDBMS is a form of database management system, but it’s specific to the way the management system stores data. RDBMS applications use tables to store data, where each column defines the data type and what is stored in each record. Each record is represented as a row. Most table designers limit the number of columns in a table, but a table can contain millions of rows.

Users query the database using Structured Query Language (SQL), which is the language for most relational databases. SQL has slight differences depending on the database engine storing data, but SQL is generally the same syntax across all databases. To speed up queries, administrators must use indexes on columns commonly used in query filters. Primary keys are used to distinguish between records, and related data between tables is linked using joins and foreign keys.

Key Differences between a DBMS and an RDBMS

The key difference between a DBMS and an RDBMS is in how data is stored. An RDBMS specifically uses tables, and most relational databases on the market use their own strategy to store and retrieve data. Administrators familiar with one RDBMS will have a small learning curve to work with another. They all use primary keys, tables, and indexes to speed up queries.

DBMS is more of an umbrella term and can be used to describe any simple or complex database system. For example, an RDBMS uses tables to store data, while a DBMS might use simple files. Instead of primary keys, files in this example might simply have their own randomly generated unique string for a name. The way a DBMS manages data depends on the engine, while an RDBMS always stores data in tables.

Use Cases for DBMS

Any industry can use a DBMS, but choosing the right one—including an RDBMS—depends on the application. For example, banks might use a DBMS to organize financial data or store scanned PDFs for consumer loans. DBMS applications scale well and can handle massive amounts of data as long as administrators know how to configure for performance and set up query frontend applications for users.

Unstructured, larger data silos used in analytics require a NoSQL DBMS. For example, suppose you want to scrape a list of web pages from competitor sites and analyze them for marketing purposes and gap analysis. A NoSQL DBMS would store the data without limiting it to a specific column. A NoSQL DBMS is often used when you’re unsure of the type and size of data that you need to store.

Use Cases for RDBMS

Relational databases are much older than NoSQL DBMS applications. When selecting a database, you’ll need to know the type of data that you plan to store. For example, an e-commerce store can work with an RDBMS to store customer information, order data, products, and user preferences. Provided administrators configure tables properly, queries filter through millions of records within milliseconds and return a data set to users.

MySQL, SQL Server, and Oracle are a few examples of RDBMS technology that powers popular enterprise applications. WordPress software, for instance, runs on MySQL. If you can fit each data item into a column with a specific data type, you can use an RDBMS for storage and common SQL querying syntax. The learning curve for an RDBMS is also smaller, but it scales well for growing business applications.

데이터베이스 환경 최적화
데이터베이스 환경 최적화
분석 보고서

데이터베이스 환경 최적화에 스토리지가 어떤 역할을 하는지

퓨어스토리지는 조직이 이러한 목표를 달성할 수 있도록 차별화된 혁신을 제공하고 있습니다.

Examples of a DBMS

DBMS systems store data as files, but data is often stored in a hierarchy. For example, you have files stored in directories on your computer, and this file system could be considered a DBMS. If you have Windows, the Windows Registry could be considered a DBMS. Each section of the registry is stored in a directory, and subsequent keys and data are stored in subdirectories.

Web applications might also store data in files, but data is formatted for the web application to consume and digest. XML or JSON files are data files stored in a DBMS system. As an example, the web server stores the files and uses them when a user sends a command. The command could be to change user preferences, and a JSON file stores current preferences. Most data is stored in an RDBMS, but these specific use cases are examples of using flat files to store data without a backend database engine.

Examples of an RDBMS

Relational database management systems use tables to organize data. Database engines are faster at querying data than using a flat file system, so they’re preferred for most applications. A few popular RDBMS options include MySQL, PostgreSQL, Microsoft SQL Server, and Oracle. Data in all these databases is organized in tables, and tables use common columns to link to each other. Most web applications use RDBMS engines. Even applications with other types of databases use RDBMS for structured data.

In an enterprise, relational databases are often distributed across locations. Data can be synchronized across locations such as data centers to make querying faster for local users. For example, if an enterprise has applications servicing multiple geographical locations, databases might be located at local data centers to speed up requests from users.

Conclusion

Once you choose a database, it’s very difficult to switch to a different solution. The first step is to determine the type of data that you will store, and then figure out which database is the most convenient and efficient for your application. Remember that DBMS is an umbrella term, so any database engine you choose will likely fall under the umbrella. If you need a more organized, rigid storage solution, an RDBMS could be the right choice. 

In addition to choosing a database engine, you’ll also need a place to store your data. Pure Storage cloud services support enterprise-tier database applications for businesses that need high uptime and cloud support.

다음을 추천드립니다.

07/2023
퓨어스토리지 플래시어레이//X (FlashArray//X) | 퓨어스토리지
강력한 성능과 효율성으로 핵심 애플리케이션 가속화
데이터시트
5 pages

주요 유용한 자료 및 이벤트를 확인하세요

THOUGHT LEADERSHIP
혁신을 향한 레이스

스토리지 혁신의 최전선에 있는 업계 리더들의 최신 인사이트 및 관점을 확인하세요.

더 알아보기
동영상
동영상 시청: 엔터프라이즈 데이터 클라우드의 가치

찰스 쟌칼로(Charles Giancarlo) CEO가 전하는 스토리지가 아닌 데이터 관리가 미래인 이유 통합 접근 방식이 기업 IT 운영을 어떻게 혁신하는지 알아보세요

지금 시청하기
유용한 자료
레거시 스토리지는 미래를 지원할 수 없습니다.

현대적 워크로드에는 AI 지원 속도, 보안, 확장성이 필수입니다. 귀사의 IT 스택, 준비됐나요?

지금 확인하기
퓨어360(PURE260) 데모
퓨어스토리지를 직접 탐색하고, 배우고, 경험해보세요.

퓨어스토리지의 역량을 확인할 수 있는 온디맨드 비디오와 데모를 시청하세요.

데모영상 시청하기
지원하지 않는 브라우저입니다.

오래된 브라우저는 보안상 위험을 초래할 수 있습니다. 최상의 경험을 위해서는 다음과 같은 최신 브라우저로 업데이트하세요.

Personalize for Me
Steps Complete!
1
2
3
Personalize your Everpure experience
Select a challenge, or skip and build your own use case.
미래를 대비한 가상화 전략

모든 요구 사항에 맞는 스토리지 옵션.

모든 규모의 AI 프로젝트 지원

데이터 파이프라인, 교육 및 추론을 위한 고성능 스토리지

중요한 데이터 손실을 사전에 방지하세요.

비즈니스 리스크를 최소화하는 사이버 복원력 솔루션

클라우드 운영 비용 절감

Azure, AWS 및 프라이빗 클라우드를 위한 비용 효율적인 스토리지.

애플리케이션 및 데이터베이스 성능 가속화

로우 레이턴시 스토리지로 애플리케이션 성능을 극대화하세요.

데이터센터 전력 및 공간 사용량 절감

리소스 효율을 극대화하는 스토리지로 데이터센터 활용도를 최적화

Confirm your outcome priorities
Your scenario prioritizes the selected outcomes. You can modify or choose next to confirm.
Primary
Reduce My Storage Costs
Lower hardware and operational spend.
Primary
Strengthen Cyber Resilience
Detect, protect against, and recover from ransomware.
Primary
Simplify Governance and Compliance
Easy-to-use policy rules, settings, and templates.
Primary
Deliver Workflow Automation
Eliminate error-prone manual tasks.
Primary
Use Less Power and Space
Smaller footprint, lower power consumption.
Primary
Boost Performance and Scale
Predictability and low latency at any size.
What’s your role and industry?
We've inferred your role based on your scenario. Modify or confirm and select your industry.
Select your industry
Financial services
Government
Healthcare
Education
Telecommunications
Automotive
Hyperscaler
Electronic design automation
Retail
Service provider
Transportation
Which team are you on?
Technical leadership team
Defines the strategy and the decision making process
Infrastructure and Ops team
Manages IT infrastructure operations and the technical evaluations
Business leadership team
Responsible for achieving business outcomes
Security team
Owns the policies for security, incident management, and recovery
Application team
Owns the business applications and application SLAs
Describe your ideal environment
Tell us about your infrastructure and workload needs. We chose a few based on your scenario.
Select your preferred deployment
Hosted
Dedicated off-prem
On-prem
Your data center + edge
Public cloud
Public cloud only
Hybrid
Mix of on-prem and cloud
Select the workloads you need
Databases
Oracle, SQL Server, SAP HANA, open-source

Key benefits:

  • Instant, space-efficient snapshots

  • Near-zero-RPO protection and rapid restore

  • Consistent, low-latency performance

 

AI/ML and analytics
Training, inference, data lakes, HPC

Key benefits:

  • Predictable throughput for faster training and ingest

  • One data layer for pipelines from ingest to serve

  • Optimized GPU utilization and scale
Data protection and recovery
Backups, disaster recovery, and ransomware-safe restore

Key benefits:

  • Immutable snapshots and isolated recovery points

  • Clean, rapid restore with SafeMode™

  • Detection and policy-driven response

 

Containers and Kubernetes
Kubernetes, containers, microservices

Key benefits:

  • Reliable, persistent volumes for stateful apps

  • Fast, space-efficient clones for CI/CD

  • Multi-cloud portability and consistent ops
Cloud
AWS, Azure

Key benefits:

  • Consistent data services across clouds

  • Simple mobility for apps and datasets

  • Flexible, pay-as-you-use economics

 

Virtualization
VMs, vSphere, VCF, vSAN replacement

Key benefits:

  • Higher VM density with predictable latency

  • Non-disruptive, always-on upgrades

  • Fast ransomware recovery with SafeMode™

 

Data storage
Block, file, and object

Key benefits:

  • Consolidate workloads on one platform

  • Unified services, policy, and governance

  • Eliminate silos and redundant copies

 

What other vendors are you considering or using?
Thinking...
Your personalized, guided path
Get started with resources based on your selections.