Solution

MongoDB

Document Database for the Modern Era

Document Database for the Modern Era

Document Database for the Modern Era

The global No. 1 in NoSQL and the standard for next-generation data platforms

The global No. 1 in NoSQL
and the standard for
next-generation data platforms

Founded in New York in 2007, MongoDB is a global document-database company
and the No. 1 platform in NoSQL, with over 62,500 customers worldwide
and more than 500 million cumulative downloads.

Founded in New York in 2007, MongoDB is a global
document-database company and the No. 1 platform
in NoSQL, with over 62,500 customers worldwide
and more than 500 million cumulative downloads.

Founded in New York in 2007, MongoDB is a global document-database company and the No. 1 platform in NoSQL, with over 62,500 customers worldwide
and more than 500 million cumulative downloads.

It fundamentally resolves the limitations of RDBMSs—schema rigidity,
difficulty scaling horizontally, and inefficient processing of unstructured data—
and is being adopted in mission-critical environments such as finance, telecommunications,
public sector, and manufacturing as a replacement for, or a complement to, RDBMSs.

It fundamentally resolves the limitations of RDBMSs—
schema rigidity, difficulty scaling horizontally,
and inefficient processing of unstructured data—
and is being adopted in mission-critical environments
such as finance, telecommunications, public sector,
and manufacturing as a replacement for,
or a complement to, RDBMSs.

It fundamentally resolves the limitations of RDBMSs—schema rigidity, difficulty scaling horizontally, and inefficient processing of unstructured data— and is being adopted in mission-critical environments such as finance, telecommunications, public sector, and manufacturing as a replacement for, or a complement to, RDBMSs.

Headquarters: New York, USA (NASDAQ: MDB)

Founded: 2007 (Korean subsidiary: 2018)

FY'26 revenue: USD 2.43B (KRW 3.4T, up 19% YoY)

FY'26 revenue: USD 2.43B
(KRW 3.4T, up 19% YoY)

Market capitalization: USD 26.58B (KRW 37.7T, as of February 2026)

Market capitalization: USD 26.58B
(KRW 37.7T, as of February 2026)

Global headcount: 5,500+ (40+ in the Korean subsidiary)

Global headcount: 5,500+
(40+ in the Korean subsidiary)

Cumulative customers: 62,500+ / Cumulative downloads: 500M+

Request an adoption consultation

Why MongoDB Now

The Limits of RDBMSs and the Evolution of the Data Model

The Limits of RDBMSs and the Evolution
of the Data Model

The data of digital business is no longer composed solely of structured rows and columns. Unstructured and semi-structured data of varying formats—news, terms and conditions, electronic subscription documents, user-behavior logs, IoT sensor data, and more—now accounts for more than 80% of all data.

The data of digital business is no longer composed solely of structured rows and columns. Unstructured and semi-structured data of varying formats—news, terms and conditions, electronic subscription documents, user-behavior logs, IoT sensor data, and more—now accounts for more than 80% of all data.

Four Limitations Facing Existing RDBMSs

Four Limitations Facing Existing RDBMSs

Four Limitations Facing Existing RDBMSs

01 | Schema Rigidity

01 | Schema Rigidity

  • Adding or changing fields requires modifying the table schema

  • It cannot keep pace with the speed of business change

  • New-service launches are delayed

02 | Complex JOINs and Performance Degradation

02 | Complex JOINs and Performance
Degradation

  • Relational data is split across multiple tables → multiple JOINs are needed for queries

  • JOIN performance degrades sharply as data volume grows

  • Relational data is split across multiple tables → multiple JOINs are needed for queries

  • JOIN performance degrades sharply as data volume grows

  • Relational data is split across multiple tables → multiple JOINs are needed for queries

  • JOIN performance degrades sharply as data volume grows

03 | Difficulty Scaling Horizontally

  • Only scale-up is possible once a single server reaches its capacity limit

  • Scale-out configurations require separate middleware or sharding solutions

  • Difficulty handling event-driven traffic (e.g., IPO subscriptions)

  • Only scale-up is possible once a single server reaches its capacity limit

  • Scale-out configurations require separate middleware or sharding solutions

  • Difficulty handling event-driven traffic
    (e.g., IPO subscriptions)

  • Only scale-up is possible once a single server reaches its capacity limit

  • Scale-out configurations require separate middleware or sharding solutions

  • Difficulty handling event-driven traffic
    (e.g., IPO subscriptions)

04 | Licensing-Cost Burden

  • Core-based billing for commercial RDBMSs such as Oracle

  • Roughly KRW 2.0B over a 3-year TCO for an 18-core × 2 configuration

  • Roughly KRW 0.6B for the same environment when migrating to MongoDB (case-based)

  • Core-based billing for commercial RDBMSs such as Oracle

  • Roughly KRW 2.0B over a 3-year TCO for an 18-core × 2 configuration

  • Roughly KRW 0.6B for the same environment when migrating to MongoDB (case-based)

  • Core-based billing for commercial RDBMSs such as Oracle

  • Roughly KRW 2.0B over a 3-year TCO for an 18-core × 2 configuration

  • Roughly KRW 0.6B for the same environment when migrating to MongoDB (case-based)

What is needed now is not 'replacing the database,'
but 'redesigning the data model.'

What is needed now is not
'replacing the database,'
but 'redesigning the data model.'

The MongoDB Data Model

Document Data Model
Intuitive, Flexible Data Representation

MongoDB stores related data within a single JSON document.
Because the way the application uses data matches the way the database stores it,
both development productivity and query performance improve.

MongoDB stores related data within a single
JSON document. Because the way the application uses data matches the way the database stores it,
both development productivity and query performance improve.

RDBMS vs. MongoDB Comparison

Layer

RDBMS

RDBMS

RDBMS

MongoDB

MongoDB

MongoDB

Top-Level Unit

Top-Level Unit

Top-Level Unit

Storage Unit

Storage Unit

Storage Unit

Record

Record

Record

Field

Field

Field

Relationship Representation

Relationship Representation

Relationship Representation

Transaction

Transaction

Transaction

Schema

Schema

Schema

Database

Database

Database

Table

Table

Table

Row

Row

Row

Column

Column

Column

Parent–Child Tables + JOIN

Parent–Child Tables + JOIN

Parent–Child Tables + JOIN

Multi-Record ACID

Multi-Record ACID

Multi-Record ACID

fixed schema

fixed schema

fixed schema

Database

Database

Database

Collection

Collection

Collection

Document (JSON / BSON)

Field

Embedding / Linking / $lookup

Multi-Document ACID

flexible schema

Sroll

Query-Comparison Example

RDBMS (SQL)
SELECT * FROM Cars WHERE Cars.owner = 'Jake'
INNER JOIN Wheels ON Cars.id = Wheels.car_id
INNER JOIN Seats ON Cars.id = Seats.car_id
INNER JOIN Brakes ON Cars.id = Brakes.car_id ...

MongoDB (MQL)

db.cars.find( { "owner": "Jake" } )
→ wheels, seats, and brakes are all contained within a single document,

so no JOIN is needed

db.cars.find( { "owner": "Jake" } )
→ wheels, seats, and brakes are all contained within a single document, so no JOIN is needed

MongoDB Core Capabilities

MongoDB
Core Capabilities

Six Core Capabilities for Enterprise Environments

Six Core Capabilities
for Enterprise Environments

01 | High Availability — Replica Set

  • Automatic self-healing — fail-over guaranteed within 5 seconds

  • Retryable reads and writes for transient connection failures

  • Separation of operational (OLTP) and analytical (OLAP) workloads

  • Support for disaster recovery (DR) and zero-downtime maintenance

  • Automatic self-healing — fail-over guaranteed within 5 seconds

  • Retryable reads and writes for transient connection failures

  • Separation of operational (OLTP) and analytical (OLAP) workloads

  • Support for disaster recovery (DR) and zero-downtime maintenance

02 | Scale-Out — Native Sharding

  • Automated scaling beyond single-node constraints

  • Adding shards without modifying application code

  • User-configurable data distribution such as hashed, ranged, and zoned

  • Compliance with regional data-residency regulations

  • Automated scaling beyond single-node constraints

  • Adding shards without modifying application code

  • User-configurable data distribution such as hashed, ranged, and zoned

  • Compliance with regional data-residency regulations

03 | Multi-Document ACID Transactions

03 | Multi-Document ACID
Transactions

  • Transaction guarantees on par with RDBMSs

  • Essential for financial, payment, and settlement systems

  • Transaction guarantees on par with RDBMSs

  • Essential for financial, payment, and settlement systems

04 | A Rich Query Language (MQL)

  • Compound indexes, text search, geospatial queries, and graph traversal

  • A powerful Aggregation Framework — real-time aggregation

  • Intuitive query authoring with the MongoDB Compass GUI tool

05 | AI / Vector Search Integration

  • Atlas Vector Search — storing embedding vectors and performing similarity search

  • Implementing Gen AI and RAG architectures within a single database

  • Integrated management of operational data and vector data

  • Atlas Vector Search — storing embedding vectors and performing similarity search

  • Implementing Gen AI and RAG architectures within a single database

  • Integrated management of operational data and vector data

06 | Multi-Cloud and Hybrid

  • Support for on-premises, private cloud, public cloud, and hybrid cloud

  • Simultaneous operation across multiple clouds such as AWS, Azure, and GCP

  • Availability of both Atlas (managed cloud) and Enterprise Advanced (self-managed)

  • Support for on-premises, private cloud, public cloud, and hybrid cloud

  • Simultaneous operation across multiple clouds such as AWS, Azure, and GCP

  • Availability of both Atlas (managed cloud) and Enterprise Advanced (self-managed)

Global Market Standing

DB-Engines Ranking — No. 1 Globally in NoSQL

DB-Engines Ranking
No. 1 Globally in NoSQL

2026 DB-Engines Ranking, Top 5 among all databases and No. 1 in document DBs

2026 DB-Engines Ranking, Top 5 among all databases and No. 1 in document DBs

No. 1 globally in the NoSQL category for 5+ consecutive years

No. 1 globally in the NoSQL category

for 5+ consecutive years

Growing preference in Korea — "Most Wanted Database in Korea 2023"

(NIPA Open-Source Database Survey), Stack Overflow Developer Survey

Growing preference in Korea — "Most Wanted Database in Korea 2023"

(NIPA Open-Source Database Survey),

Stack Overflow Developer Survey

MongoDB is not a mere NoSQL database;
it is establishing itself as a standard data platform for replacing or
complementing RDBMSs in the enterprise market.

MongoDB is not a mere NoSQL database;
it is establishing itself as a standard data platform for replacing
or complementing RDBMSs in the enterprise market.

MongoDB is not a mere NoSQL database;
it is establishing itself as a standard data
platform for replacing or complementing
RDBMSs in the enterprise market.

Financial-Sector Use Cases

Financial-Sector
Use Cases

Six Validated Application Areas Across Major Korean Financial Institutions

Six Validated Application Areas
Across Major Korean Financial Institutions

Six Validated Application Areas
Across Major Korean
Financial Institutions

MongoDB has supported mission-critical workloads across diverse Korean financial sectors,
including commercial banks, securities, cards, and insurance.

MongoDB has supported mission-critical workloads across diverse Korean financial sectors, including commercial banks, securities, cards, and insurance.

MongoDB has supported mission-critical workloads
across diverse Korean financial sectors, including
commercial banks, securities, cards, and insurance.

01 | Unstructured-Data Integration / Channel Services

01 | Unstructured-Data Integration /
Channel Services

  • Representative case — Mirae Asset Securities HTS

  • Target functions — news services, electronic-subscription systems, and unified messaging (UMS)

  • MongoDB value — data can be stored immediately without schema changes even as formats change → faster new-service launches

02 | Real-Time Personalization and Tailored Services

02 | Real-Time Personalization and
Tailored Services

  • Representative case — BC Card personalized recommendations and notifications

  • Target functions — personalized asset management and marketing offers

  • MongoDB value — consolidating all of a customer's behavior and information into a single document → operating personalization without complex JOINs

03 | Concentrated Events / Traffic Distribution

03 | Concentrated Events /
Traffic Distribution

  • Representative case — Kakao Pay settlement system

  • Target functions — IPO-subscription systems and event-driven push-notification servers

  • MongoDB value — unlimited throughput scaling via sharding → absorbing massive concentrated loads without system paralysis

04 | Big-Data Real-Time Analytics / Dashboards

04 | Big-Data Real-Time Analytics /
Dashboards

  • Representative case — AIA next-generation big-data platform

  • Target functions — real-time trading dashboards and investment-behavior analysis

  • MongoDB value — serving as the serving layer that ingests Hadoop analysis results,
    plus real-time aggregation

05 | Strategic Co-existence with RDBMSs

05 | Strategic Co-existence
with RDBMSs

  • Representative cases — KB Kookmin Bank paperless and MetLife Single View of Customer

  • Target functions — a ledger system (RDBMS) plus an integrated data-query layer (MongoDB)

  • MongoDB value — building fragmented ERP and WMS data into a unified ODS layer → achieving a single view

06 | Real-Time Logging / FDS

  • Representative cases — Shinhan Securities and Meritz Fire & Marine Insurance

  • Target functions — anomaly-pattern tracking (FDS) and integrated system-log management

  • MongoDB value — collecting and analyzing 1,000+ logs per second at scale, with security- and backup-compliance via Ops Manager

Oracle-Replacement Effect

Oracle-Replacement
Effect

Cost savings impact across general-purpose service domains

Application case for the unstructured data domain in K Bank's paperless project
(based on an actual evaluated case)

Application case for the unstructured data domain
in K Bank's paperless project
(based on an actual evaluated case)

Category

Oracle baseline (3-year TCO)

MongoDB baseline (3-year TCO)

Environment

18 cores × 2

Equivalent environment

Cost

Approx. KRW 2.0 billion

Approx. KRW 0.6 billion

Savings

ㅡ

Approx. KRW 1.4 billion saved
(70% reduction)

Sroll

Adopting MongoDB is not mere cost reduction;
it simultaneously achieves three KPIs—DBMS modernization, cost reduction,
and Gen AI-based expansion

Adopting MongoDB is not mere cost reduction;
it simultaneously achieves three KPIs—
DBMS modernization, cost reduction, and Gen AI-based expansion

Adopting MongoDB is not mere cost reduction;
it simultaneously achieves three KPIs—
DBMS modernization, cost reduction,
and Gen AI-based expansion

Proposed and Review Areas

Proposed and
Review Areas

High-Level Agenda
(for Decision-Makers)

High-Level Agenda
(for Decision-Makers)

Oracle replacement — reducing Oracle costs in general-purpose service areas

Oracle replacement — reducing Oracle costs in general-purpose service areas

Securities-firm FDS — a system for detecting anomalous user-behavior patterns

Securities-firm FDS — a system for detecting anomalous user-behavior patterns

Gen AI + operational-system integration — a single database based on Vector Search

Gen AI + operational-system integration —

a single database based on Vector Search

Card-issuer personalized-recommendation systems

Card-issuer personalized-

recommendation systems

Practitioner-Level Agenda

Practitioner-Level Agenda

Unstructured-data integration / channel services (HTS, UMS)

Unstructured-data integration /

channel services (HTS, UMS)

Real-time personalization / tailored services (CRM, marketing offers)

Concentrated events / traffic distribution (IPOs, event notifications)

Big-data real-time analytics

Strategic co-existence configurations with RDBMSs
(Co-existence Strategy)

Strategic co-existence configurations with RDBMSs (Co-existence Strategy)

Real-time logging data / anomaly detection (Logging & FDS)

Product Types and Support Framework

Product Types
and Support Framework

Enterprise Advanced & Cloud (Atlas)

Deployment Options

On-Premises

On-Premises | your own data center

On-Premises |

your own data center

your own data center

Private Cloud

Private Cloud | a private-cloud environment

Private Cloud |

a private-cloud environment

a private-cloud environment

Hybrid Cloud

Hybrid Cloud | on-premises + cloud

Hybrid Cloud |

on-premises + cloud

on-premises + cloud

Public Cloud

Public Cloud | AWS / Azure / GCP

Public Cloud |

AWS / Azure / GCP

AWS / Azure / GCP

Managed Cloud (Atlas)

Managed Cloud (Atlas) | Atlas, managed directly by MongoDB

Managed Cloud (Atlas) |

Atlas, managed directly by MongoDB

Atlas, managed directly by MongoDB

Multi-Cloud

Multi-Cloud | operating several clouds simultaneously

Multi-Cloud |
operating several clouds simultaneously

operating several clouds simultaneously

Self-Managed (Enterprise Advanced) Core Features

Self-Managed
(Enterprise Advanced)
Core Features

Ops Manager — monitoring, backup, automation, and compliance

LDAP and Kerberos integrated authentication

Encryption at Rest / In Transit

Auditing — security audit logs

In-Memory Storage Engine

Compass — a GUI management tool

Partnership Structure

With no distributor structure,

partners can operate freely as Tier 1 or Tier 2 (direct contracts with customers)

With no distributor structure, partners can operate freely

as Tier 1 or Tier 2 (direct contracts with customers)

With no distributor structure, partners can operate freely as Tier 1 or Tier 2

(direct contracts with customers)

Active partnerships in the Korean financial, public, manufacturing,

and telecommunications sectors

The Differentiated Value SIMPLEKEY Creates

Redesigning the Data Architecture,
Not Merely Adopting a Database

SimpleKey does not simply supply MongoDB licenses.
It defines a database transition not as a product swap,
but as a data-architecture transformation project.

SimpleKey does not simply supply MongoDB licenses. It defines a database transition
not as a product swap, but as a data-
architecture transformation project.

MongoDB is a next-generation data platform. SimpleKey designs a sustainable data-operations structure on top of it—a partner that proves itself through operational results, not technology supply. That is SimpleKey's differentiation.

MongoDB is a next-generation data platform. SimpleKey designs a sustainable data-operations structure on top of it—a partner that proves itself through operational results, not technology supply. That is SimpleKey's differentiation.

MongoDB is a next-generation data platform. SimpleKey designs a sustainable data-operations structure on top of it—a partner that proves itself through operational results, not technology supply. That is SimpleKey's differentiation.

Data modeling and workload assessment from the pre-adoption stage

3-to-5-year TCO simulation versus Oracle/RDBMS

Design of an RDBMS co-existence strategy

Architectural advisory on using Gen AI and Vector Search

Establishment of operational standards for sharding and replica sets

An ITO-based 24×365 operations-support framework

An ITO-based 24×365 operations-support framework

Data is no longer expressed solely
in "rows and columns."

Data is no longer expressed solely
in "rows and columns."

The explosion of unstructured data, the demand for real-time processing, and AI integration—

all of these trends are changing the very nature of the database.

Through a flexible document model, automatic sharding, multi-cloud support,

and Gen AI integration, MongoDB redefines the data foundation of digital business.

The explosion of unstructured data, the demand for real-time processing, and AI integration—

all of these trends are changing the very nature of
the database. Through a flexible document model, automatic sharding, multi-cloud support,

and Gen AI integration, MongoDB redefines
the data foundation of digital business.

And SimpleKey designs a stable, scalable data-operations structure on top of it.

And SimpleKey designs a stable, scalable
data-operations structure on top of it.

Inquire about a MongoDB adoption-assessment service
Inquire about a MongoDB
adoption-assessment service
Receive a TCO simulation versus Oracle
Receive a TCO simulation
versus Oracle
Consult on a Gen AI and Vector Search PoC
Consult on a Gen AI
and Vector Search PoC

Simplyfying Complex Enterprise IT

SERVICES

Integrated Maintenance

ITO

BPO

Rimini Street

SOLUTIONS

AICC

HPE / SANGFOR

QUEUE-IT

SUSE LINUX

FASTLY

LTZERO

MONGODB

INDUSTREIS

Finance

Retail

Public Sector

Manufacturing

COMPANY

ABOUT US

BRAND & CULTURE

MEDIA

BLOG

NEWS

CONTACT

Privacy Policy

Terms of Use

CEO: Seongcheol Park

406, 407, 408-ho, Hangang G Tree Tower, 738 Yangcheon-ro, Gangseo-gu, Seoul 07539, Republic of Korea

Tel : 02-784-5700

Fax: 02-784-5701

Customer Center: 1599-9731

Copyright © 2026 SimpleKey. All Rights Reserved.

Simplyfying Complex Enterprise IT

SERVICES

Integrated Maintenance

ITO

BPO

Rimini Street

SOLUTIONS

AICC

HPE / SANGFOR

QUEUE-IT

SUSE LINUX

FASTLY

LTZERO

MONGODB

INDUSTREIS

Finance

Retail

Public Sector

Manufacturing

COMPANY

ABOUT US

BRAND & CULTURE

MEDIA

BLOG

NEWS

CONTACT

Privacy Policy

Terms of Use

CEO: Seongcheol Park

406, 407, 408-ho, Hangang G Tree Tower, 738 Yangcheon-ro, Gangseo-gu, Seoul 07539, Republic of Korea

Tel : 02-784-5700

Fax: 02-784-5701

Customer Center: 1599-9731

Copyright © 2026 SimpleKey. All Rights Reserved.

Simplyfying Complex Enterprise IT

SERVICES

Integrated Maintenance

ITO

BPO

RIMINI STREET

SOLUTIONS

AICC

HPE / SANGFOR

QUEUE-IT

SUSE LINUX

FASTLY

LTZERO

MONGODB

INDUSTREIS

FINANCE

RETAIL

PUBLIC SECTOR

MANUFACTURING

MEDIA

NEWS

BLOG

COMPANY

ABOUT US

BRAND & CULTURE

CONTACT

Privacy Policy

Terms of Use

Customer Center: 1599-9731

406, 407, 408-ho, Hangang G Tree Tower, 738 Yangcheon-ro,

Gangseo-gu, Seoul 07539, Republic of Korea

Fax: 02-784-5701

Tel : 02-784-5700

CEO: Seongcheol Park

Copyright © 2026 SimpleKey. All Rights Reserved.