Object Storage

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SUMMARY

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.

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