Data Archiving for Time-series Databases
Data archiving for time-series databases is a systematic approach to storing and managing historical data while balancing performance, cost, and accessibility requirements. It involves moving older data to lower-cost storage tiers while maintaining query capabilities and compliance with retention policies.
Understanding time-series data archiving
Time-series data archiving is essential for financial institutions dealing with massive volumes of market data, trading activity, and regulatory reporting requirements. The process involves strategically moving historical data across storage tiers while preserving data integrity and maintaining query capabilities.
Key archiving strategies
Tiered storage architecture
Financial organizations typically implement a tiered storage approach:
- Hot tier: Recent market data and active trading information
- Warm tier: Historical analysis and backtesting data
- Cold tier: Long-term storage for compliance and occasional access
Data compression and downsampling
As data ages, organizations often implement:
- Lossless compression for regulatory data
- Downsampling of high-frequency market data
- Aggregation of tick-level data into OHLCV summaries
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Regulatory considerations
Financial institutions must comply with various retention requirements:
- Trade reconstruction data (typically 5-7 years)
- Market surveillance records
- Audit trails for regulatory reporting
Compliance with data retention policies
Organizations must maintain:
- Clear audit trails
- Data immutability
- Quick retrieval capabilities for regulatory inquiries
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Performance optimization
Query performance
Archival solutions must balance:
- Fast access to recent data
- Efficient querying of historical information
- Cost-effective storage utilization
Data retrieval strategies
Implementation of:
- Partition pruning
- Parallel query processing
- Intelligent caching mechanisms
Best practices for financial data archiving
- Define clear retention policies based on data type and regulatory requirements
- Implement automated archiving workflows
- Maintain data lineage and metadata
- Regular testing of restoration procedures
- Monitor storage costs and optimization opportunities
Integration with trading systems
Financial institutions must ensure their archiving solutions support:
- Real-time market data processing
- Historical backtesting requirements
- Regulatory reporting needs
- Risk management analysis
The archiving strategy must align with the organization's:
- Trading strategies
- Risk management requirements
- Compliance obligations
- Cost optimization goals
Modern archiving technologies
Contemporary solutions leverage:
- Cloud storage tiers
- Columnar compression
- Advanced partitioning schemes
- Automated lifecycle management
These technologies help organizations maintain optimal performance while managing costs and ensuring compliance with regulatory requirements.