Columnar Databases
Columnar databases store data in columns instead of rows, leading to faster queries and more efficient analytics.
Classification
- ComplexityMedium
- Impact areaTechnical
- Decision typeArchitectural
- Organizational maturityIntermediate
Technical context
Principles & goals
Use cases & scenarios
Compromises
- Dependency on specific tools.
- Difficulties in data migration.
- Lack of standardization.
- Conduct regular performance testing.
- Follow security protocols.
- Maintain close contact with data providers.
I/O & resources
- Data Sources
- User Requirements
- Analytical Project Setup
- Analytical Dashboards
- Real-Time Reporting
- Data Analyses
Description
Columnar databases provide differentiated data storage that is particularly advantageous for analytical applications. By storing data in columns, query performance can be significantly improved, and focusing on specific attributes allows for deeper analyses.
✔Benefits
- High query speed due to columnar storage.
- Efficient storage utilization through compression.
- Better support for analytical workloads.
✖Limitations
- Not suitable for transactional workloads.
- Higher query complexity in certain scenarios.
- Potential latency in large queries.
Trade-offs
Metrics
- Query Speed
Measures the time taken to execute queries.
- Storage Optimization
Assesses the efficiency of storage utilization.
- System Resource Usage
Monitors system resource consumption during operation.
Examples & implementations
Analytics Application at a Financial Services Provider
A financial services provider uses a columnar database to analyze large amounts of data in seconds.
Use of a Columnar DB in Telecommunications
A telecommunications company uses columnar databases for fraud detection and network optimization.
Real-Time Analytics for Retailers
A retailer uses a columnar database to gain real-time insights into customer behavior.
Implementation steps
Identify and analyze data sources.
Select and implement the columnar database.
Set up dashboards and analytics tools.
⚠️ Technical debt & bottlenecks
Technical debt
- Outdated database software
- Insufficient system resources
- Lack of documentation
Known bottlenecks
Misuse examples
- Accessing the database without requirements.
- Using outdated data models.
- Non-compliance with data policies.
Typical traps
- Merging unnecessary data.
- Misleading data visualization.
- Ignoring usage policies.
Required skills
Architectural drivers
Constraints
- • Limited support for transaction processing.
- • Dependency on specialized vendors.
- • Requires specific hardware for optimal performance.