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Apache Iceberg

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SUMMARY

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:

This integration capability makes it particularly valuable for organizations managing large-scale time-series data across multiple platforms and use cases.

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