10 Future Apache Iceberg Developments to Look forward to in 2025

2. Interoperable Views in Apache Iceberg

Interoperable views are another major development to watch in the Apache Iceberg ecosystem for 2025. While Iceberg already supports a view specification, the current approach has limitations: it stores the SQL used to define the view, but since SQL syntax varies across engines, resolving these views is not always feasible in a multi-engine environment.

To address this challenge, two promising solutions are being explored:

• SQL Transpilation with Frameworks like SQLGlot: By leveraging SQL transpilation tools such as SQLGlot, the SQL defining a view can be translated between different dialects. This approach builds on the existing view specification, which includes a "dialect" property to identify the SQL syntax used to define the view. This enables engines to resolve views by translating the SQL into a dialect they support.



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  Intermediate Representation for Views: Another approach involves using an intermediate format to represent views, independent of SQL syntax. Two notable projects being discussed in this context are:

  
  
  
  
  
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    Apache Calcite: An open-source project that provides a framework for parsing, validating, and optimizing relational algebra queries. Calcite could serve as a bridge, converting SQL into a standardized logical plan that any engine can execute.
  
  
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    Substrait: A cross-language specification for defining and exchanging query plans. Substrait focuses on representing queries in a portable, engine-agnostic format, making it a strong candidate for enabling true interoperability.
  
  
  
  
  
  

These advancements aim to make views in Iceberg truly interoperable, allowing seamless sharing and resolution of views across different engines and workflows. Whether through SQL transpilation or an intermediate format, these improvements will significantly enhance Iceberg's flexibility in heterogeneous data environments.

3. Materialized Views in Apache Iceberg

A materialized view stores a query definition as a logical table, with precomputed data that serves query results. By shifting the computational cost to precomputation, materialized views significantly improve query performance while maintaining flexibility. The Iceberg community is working towards a common metadata format for materialized views, enabling their creation, reading, and updating across different engines.

Key Features of Iceberg Materialized Views

• Metadata Structure: A materialized view is realized as a combination of an Iceberg view (the "common view") storing the query definition and a pointer to the precomputed data, and an Iceberg table (the "storage table") holding the precomputed data. The storage table is marked with states like "fresh," "stale," or "invalid" based on its alignment with source table snapshots.



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  Storage Table State Management:

  
  
  
  
  
  • A fresh state indicates the precomputed data is up-to-date.
  
  
  • A stale state requires the query engine to decide between full or incremental refresh.
  
  
  • An invalid state mandates a full refresh.
  
  
  
  
  
  




• Refresh Mechanisms: Materialized views can be refreshed through various methods, including event-driven triggers, query-time checks, scheduled refreshes, or manual operations. These methods ensure the precomputed data remains relevant to the underlying data.





• Query Optimization: Queries can use precomputed data directly if it meets freshness criteria (e.g., the materialization.data.max-staleness property). Otherwise, the query engine determines the next steps, such as refreshing the data or falling back to the original view definition.





• Interoperability and Governance: The shared metadata format supports lineage tracking and consistent states, making materialized views easy to manage and audit across engines.

Impact on the Iceberg Ecosystem

Materialized views in Iceberg offer a way to optimize query performance while ensuring that optimizations are portable across systems. By providing a standard for metadata and refresh mechanisms, Iceberg hopes to enable organizations to harness the benefits of materialized views without being locked into specific query engines. This development will make Iceberg an even more compelling choice for building scalable, engine-agnostic data lakehouses.

5. Native Geospatial Data Type Support in Apache Iceberg

The integration of geospatial data types into Apache Iceberg is poised to open up powerful capabilities for organizations managing location-based data. While geospatial data has long been supported by big data tools like GeoParquet, Apache Sedona, and GeoMesa, Iceberg's position as a central table format makes the addition of native geospatial support a natural evolution. Leveraging prior efforts such as Geolake and Havasu, this proposal aims to bring geospatial functionality into Iceberg without the need for project forks.

Proposed Features

The geospatial extension for Iceberg will introduce:

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  Geospatial Data Types: Support for types like POINT, LINESTRING, and POLYGON.


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  Geospatial Expressions: Functions such as ST_COVERS, ST_COVERED_BY, and ST_INTERSECTS for spatial querying.


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  Geospatial Partition Transforms: Partitioning using geospatial transforms like XZ2 to optimize query filtering.


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  Geospatial Sorting: Sorting data with space-filling curves, such as the Hilbert curve, to enhance data locality and query efficiency.


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  Spark Integration: Built-in support for working with geospatial data in Spark.

Key Use Cases

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   Table Creation with Geospatial Types:
   

   CREATE TABLE geom_table (geom GEOMETRY);

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   Inserting Geospatial Data:
   

  INSERT INTO geom_table VALUES ('POINT(1 2)', 'LINESTRING(1 2, 3 4)');

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   Querying with Geospatial Predicates:
   

SELECT * FROM geom_table WHERE ST_COVERS(geom, ST_POINT(0.5, 0.5));

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   Geospatial Partitioning:
   

ALTER TABLE geom_table ADD PARTITION FIELD (xz2(geom));

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   Optimized File Sorting for Geospatial Queries:
   

CALL rewrite_data_files(table => `geom_table`, sort_order => `hilbert(geom)`);

Benefits

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  Efficient Geospatial Analysis: By natively supporting geospatial data types and operations, Iceberg will enable faster and more scalable location-based queries.


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  Improved Query Optimization: Partition transforms and spatial sorting will enhance filtering and reduce data scan overhead.


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  Broad Ecosystem Integration: With Spark integration and compatibility with geospatial standards like GeoParquet, Iceberg becomes a powerful tool for geospatial data management.

, is under discussion for inclusion in the Apache Polaris ecosystem. Adding catalog versioning to Polaris would unlock a range of powerful capabilities, positioning Polaris as a unifying force for the most innovative ideas in the Iceberg catalog space.

The Power of Catalog Versioning

Catalog versioning provides a robust foundation for advanced data management scenarios by enabling:

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  Multi-Table Transactions: Ensure atomic operations across multiple tables for consistent updates.


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  Multi-Table Rollbacks: Revert changes across multiple tables to a consistent state, enhancing error recovery.


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  Zero-Copy Environments: Create lightweight, zero-copy development or testing environments without duplicating data.


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  Multi-Table Isolation: Create a branch to isolate work on data without affecting the main branch.


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  Tagging and Versioning: Mark specific states of the catalog for easy access, auditing, or rollback.

Proposed Integration with Polaris

Discussions around bringing catalog versioning to Polaris also involve designing a new model that aligns with Polaris' architecture. This integration could enable:

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  Unified Catalog Management: Allow users to manage table states and snapshots across all their data directly in Polaris.


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  Enhanced Interoperability: Unify Polaris' capabilities with the multi-table capabilities of Nessie, creating a comprehensive solution for data management.

Potential Impact

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  Advanced Data Workflows: Catalog versioning would enable Polaris users to orchestrate complex workflows with confidence and precision.


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  Improved Collaboration: Teams could work in parallel using isolated views of the catalog, fostering innovation without risk to production data.


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  Ecosystem Leadership: By adopting catalog versioning, Polaris would become the definitive platform for managing Iceberg catalogs, consolidating the best ideas from the community.

If implemented, catalog versioning in Polaris would elevate its capabilities, making it an indispensable tool for organizations looking to modernize their data lakehouse operations.