Object Storage
Object storage is a storage architecture that manages data as objects, each containing the data, metadata, and a unique identifier. Unlike traditional file systems, object storage provides unlimited scalability, built-in redundancy, and rich metadata capabilities, making it ideal for large-scale data storage and time-series applications.
How object storage works
Object storage organizes data into containers (often called buckets) that hold objects. Each object consists of:
- The actual data
- Metadata describing the object
- A globally unique identifier
Unlike file systems that use hierarchical directory structures, object storage uses a flat address space, making it highly scalable and efficient for large datasets.
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.
Key features and benefits
Scalability and durability
Object storage systems can scale to petabytes and beyond without performance degradation. They typically offer:
- Automatic replication across multiple locations
- Built-in data integrity checks
- Self-healing capabilities
Rich metadata support
Unlike block storage, object storage allows extensive metadata attachment to each object, enabling:
- Detailed tracking of data lineage
- Custom attributes for advanced querying
- Enhanced data governance capabilities
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.
Applications in time-series data
Object storage is particularly valuable for time-series data management:
Cold storage tier
Organizations often use object storage as a cost-effective solution for:
- Historical time-series data archival
- Data retention compliance
- Long-term analytics storage
Data lake foundation
Object storage serves as the foundation for modern data lakes, enabling:
- Efficient storage of raw time-series data
- Integration with analytics platforms
- Support for multiple data formats
Performance considerations
While object storage excels at scalability, certain characteristics affect its use:
Access patterns
- Best for read-heavy workloads
- Optimal for large objects (typically >1MB)
- Higher latency compared to block storage
Cost efficiency
- Lower storage costs compared to block storage
- Pay-for-what-you-use pricing models
- Reduced operational overhead
Integration with time-series databases
Modern time-series databases often leverage object storage for:
- Cold data tiering
- Backup and recovery
- Data export and import operations
This hybrid approach combines the performance of local storage with the scalability and cost benefits of object storage.