Apache Iceberg
Apache Iceberg is an open table format designed for massive analytic datasets. It provides transactional guarantees, schema evolution, and time travel capabilities while managing large-scale data lake tables. Iceberg enables reliable, high-performance access to data lake storage through its table format specification.
How Apache Iceberg works
Iceberg manages tables through a series of immutable snapshots, each representing a complete version of the table. This approach enables atomic transactions and time travel queries while maintaining performance at scale.
Key features and capabilities
Schema evolution
Iceberg supports in-place schema evolution, allowing columns to be added, removed, or reordered without copying data. This flexibility is crucial for time-series data management where schema changes are common.
Time travel and versioning
Users can query historical versions of tables using timestamps or snapshot IDs, enabling:
- Point-in-time analysis
- Audit trails
- Data recovery
- Reproducible queries
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.
Integration with time-series workloads
Iceberg works particularly well with time-series data due to its:
Partition evolution
Supports dynamic partition schemes that can evolve over time, crucial for managing temporal data efficiently.
Optimized reads
Employs metadata filtering and partition pruning to accelerate time-range queries common in time-series analysis.
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 considerations
Hidden partitioning
Iceberg abstracts partition complexity from users while maintaining performance benefits:
- Automatic partition selection
- Transparent partition evolution
- Optimized metadata handling
Concurrent access
Supports multiple concurrent readers and writers through:
- Snapshot isolation
- Atomic transactions
- Optimistic concurrency control
Integration with modern data stack
Iceberg integrates with various components of the modern data ecosystem:
- Data Lake systems for storage
- Stream processing engines for real-time updates
- BI tools for analytics
- Lakehouse Architecture implementations
This integration capability makes it particularly valuable for organizations managing large-scale time-series data across multiple platforms and use cases.