concept#Data#Platform#Architecture

Relational Database Management System (RDBMS)

Concept of a relational database management system that provides structured data storage, SQL querying and ACID transactions for consistent relational data management.

Relational Database Management Systems (RDBMS) organize data into tables with defined schemas, relations and ACID transactions.

Maturity

Established

Cognitive loadMedium

Classification

ComplexityMedium
Impact areaTechnical
Decision typeArchitectural
Organizational maturityIntermediate

Technical context

Integrations

Application layer (ORMs, DB clients)Backup and disaster recovery toolsMonitoring and observability stacks

Principles & goals

Principles

Data model first: clearly defined schemas and normalizationTransactional consistency: ACID guarantees for critical operationsSeparation of views and access: clear roles and permissions

Value stream stage

Build

Organizational level

Enterprise, Domain, Team

Use cases & scenarios

Use cases

Scenarios

Compromises

Risks

Single point of failure without replication
Lack of tuning leads to unexpected latency
Inconsistent migration scripts can cause data corruption

Best practices

Use schema versioning and automated migrations
Perform regular performance profiling and index reviews
Backup strategy with regular restore tests

I/O & resources

Inputs

Data model and entity relationships
Workload profiles (OLTP/OLAP/reporting)
Scaling and availability requirements

Outputs

Defined schema and migration scripts
Operational database instance with monitoring
Backup and restore processes documented

Resources

Description

Relational Database Management Systems (RDBMS) organize data into tables with defined schemas, relations and ACID transactions. They provide structured querying via SQL, integrity constraints and transaction control for consistent relational storage. RDBMS support indexes, access control, backup/restore and tuning, and are widely used in enterprise applications.

✔Benefits

Strong data integrity via constraints and transactions
Standardized query model (SQL) for developers
Mature ecosystems with tools for backup, monitoring and tuning

✖Limitations

Hard to scale for highly distributed, schema-less data
Schemas require upfront planning and can slow agile changes
Complex joins and very large tables can cause performance issues

Trade-offs

Metrics

Transactions per second (TPS)
Measure of processing capacity for write/read workloads.
Query latency (p95)
95th percentile of query response times to assess user experience.
Data consistency errors
Count or rate of detected integrity violations or inconsistencies.

Examples & implementations

Banking system (OLTP)

Critical account transactions are managed in an RDBMS with strong consistency guarantees.

ERP core modules

Business data, master data and accounting run in relational databases with defined schemas.

Customer reporting with materialized views

Aggregated views for reports are prepared and served in an RDBMS environment.

Implementation steps

Gather requirements and design the data model.

Select RDBMS, provision and configure infrastructure.

Implement schema, create indexes and migration scripts.

Run tests for consistency, performance and recovery.

Go-live with monitoring and operational playbooks.

⚠️ Technical debt & bottlenecks

Technical debt

Outdated schema designs without normalization standards
Undocumented migration paths
Monolithic databases without replication or sharding strategy

Known bottlenecks

Slow joins on large tablesNon-optimized queries and missing indexesI/O-bound workloads without appropriate storage strategy

Misuse examples

Using an RDBMS for purely schema-less, highly distributed events without appropriate architecture
Overusing denormalization leading to poor maintainability
Not testing backups and being unprepared for failures

Typical traps

Ignoring lock contention under parallel write load
Insufficient planning for index maintenance and statistics
Performing migrations without a rollback plan

Required skills

Relational modeling and SQL skillsPerformance tuning and index strategiesOperations and backup/restore procedures

Architectural drivers

Data integrity and transaction safetyQuery performance and latency optimizationBackup, recovery and availability requirements

Constraints

• Required schema definition before data ingestion
• License and operational costs for commercial systems
• Network and hardware dependencies for high availability