Introduction
For too long, organizations have faced a difficult choice: the unparalleled performance and ease of a managed data warehouse, or the flexibility and open interoperability of a data lake. Each path presented its own set of advantages and compromises.
But now, that paradigm shifts. BigQuery has launched its support for open table format Apache Iceberg - a significant leap forward for data architects and analysts. This isn’t just another feature; it’s a bridge, combining the best of both worlds. With Apache Iceberg table support, you can now harness the familiar power, fully managed experience, and robust governance of BigQuery, while your data resides in the open-standard Apache Iceberg format, stored securely in your own Cloud Storage buckets. This powerful combination unlocks a truly open data lake house on Google Cloud, allowing seamless interoperability with other data engines like Spark, Trino, Presto, and Flink, avoiding data duplication and giving strategic flexibility. Get ready to build a modern data architecture where you no longer have to choose between performance and openness.
Key benefits: How BigQuery’s Iceberg support transforms your data strategy
Integrating Apache Iceberg into BigQuery isn’t just a technical upgrade; it’s a feature that fundamentally changes how you manage and analyze data on Google Cloud. This brings significant advantages, empowering data teams with greater flexibility, efficiency, and control.
A. Enhanced interoperability & openness
BigQuery’s Iceberg support dramatically boosts interoperability. Your data, in open-standard Iceberg format within your Cloud Storage, can now be seamlessly queried by BigQuery and other popular engines like Spark, Trino, Flink, and Presto. This supports interoperability, facilitates multi-cloud/hybrid architectures, and fosters a more open data ecosystem, simplifying data sharing across teams and platforms.
B. Robust data lakehouse capabilities in BigQuery
Apache Iceberg brings data warehouse reliability directly to your data lake, now within BigQuery. This transforms your data lake into a true data lakehouse, enabling advanced data management:
Apache Iceberg brings data warehouse reliability directly to your data lake, now within BigQuery. This transforms your data lake into a true data lakehouse, enabling advanced data management and, crucially, adding powerful, proprietary BigQuery capabilities which are not available in the open-source Iceberg format:
- ACID Transactions & Mutations
- Schema Evolution
- Unified Batch & Streaming
- Data Governance
Unique BigQuery Enhancements for Iceberg:
- High-throughput streaming with zero read latency
- Vertex AI platform integration
- Multi Statement transactions (preview)
- Fine grained access control
- Perf acceleration (with advanced runtime preview, history based optimizations, fine-grained metadata integrated into query engine)
C. Cost optimization & storage flexibility
Leveraging BigQuery with Iceberg will ultimately result in cost optimization. By storing your data in the open Apache Iceberg format on cost-effective Google Cloud Storage, you can significantly reduce storage costs. You benefit from BigQuery’s powerful analytics engine while maintaining maximum control over data residency and cost.
For query execution, you primarily pay for BigQuery’s compute, based on the amount of data processed (on-demand pricing). However, for organizations with predictable workloads or those seeking consistent pricing and governance, BigQuery also offers slot commitments (flat-rate pricing) and spend commitments (discounted pricing tiers) as flexible alternatives to optimize costs further.
To achieve the best price-performance ratio, you can utilize BigQuery’s native capabilities to manage and optimize your Iceberg tables:
- Adaptive File-Sizing: The BigQuery engine automatically optimizes the size of your data files, preventing the “small file problem” common in data lakes and ensuring that each query efficiently scans the optimal amount of data.
- Clustering: Define clustering columns to physically co-locate related data, which reduces the amount of data scanned and accelerates queries.
- Garbage Collection: Automated processes efficiently manage and remove stale or deleted data files, reclaiming storage space and simplifying maintenance for your data lake.
You control where your data resides while benefiting from BigQuery’s powerful analytics and cost-management features.
Comparative analysis: Choosing the right table type for your needs
It’s essential to understand the different table types available and when to use each. Beyond the well-known BigQuery Native tables, we now have two distinct flavors of Iceberg integration. The following table offers a side-by-side breakdown, highlighting the key features and operational differences between BigQuery Native tables, BigLake tables for Apache Iceberg (short, BigLake Iceberg tables) and BigLake tables for Apache Iceberg in BigQuery (short, BigLake Iceberg tables in BigQuery) empowering you to make informed decisions for your data strategy.
| Feature | BigQuery Native table | BigLake Iceberg table | BigLake Iceberg table in BigQuery |
|---|---|---|---|
| Data Location | BigQuery storage | Customer-owned storage buckets GCS | Customer-owned storage buckets GCS |
| Storage Format | Proprietary columnar format (optimized for BigQuery) | Apache Parquet (open format) | Apache Parquet (open format) |
| Operations | Full CRUD (Create, Read, Update, Delete) | Read-only from BigQuery; Read/write via Spark | Full CRUD with BigQuery. Open source engines (read only with Iceberg libraries, read/write interoperability with BigQuery Storage API)Open-source DML for writes with external engines is on the roadmap |
| Security & Governance | Data masking, Row-Level Security (RLS), Column-Level Security (CLS) | Supports fine-grained access control in BigQuery via column-level security, row-level security, and data masking | Fine-grained access controls enforced in BigQuery and external engines accessing the data via Storage API |
| Metadata Support & External Integrations | Managed by BigMetadata; Integrates via BigQuery API | Iceberg metadata files (JSON); Integrates with BigLake Metastore, AWS Glue, or other external Iceberg catalogs; interoperability with Spark, Trino, Flink | BigMetadata and Iceberg metadata for interoperability; Integrates with BigLake metastore |
| Snapshot & Time Travel Support | Native BigQuery time travel up to 7 days (configurable) | Iceberg’s native snapshot & time travel; | Native BigQuery time travel. Iceberg native time travel on the roadmap |
| Partition Support | Yes | Yes | In product Roadmap - Launching soon |
| Clustering Support | Yes | No | Yes |
| Storage Optimization | Automatic re-clustering, compaction, intelligent data placement | Self-managed by customers | Fully managed by BigQuery ( Adaptive File Sizing, Re-clustering, Garbage Collection, Metadata Optimization) |
| Data Types Support | Rich set of SQL data types (e.g., STRING, INT64, BIGNUMERIC, JSON, STRUCT, ARRAY, etc ) | Standard Iceberg data types, which map to BigQuery data types | Standard Iceberg data types, which map to BigQuery data types |
| CDC Support | Native BigQuery CDC (using Storage Write API); upsert/merge operations | Iceberg’s native support for CDC patterns (upserts, deletes) via external engines (Spark, Flink) | BigQuery’s native CDC - is in the road-map |
| Easy Migration / Flexibility | Less flexible for migration to non-Google ecosystems | High flexibility due to open format, enabling easier migration across compute engines/clouds | High flexibility as tables are in open format, enabling easier migration |
| GCP Quotas and Limits | Subject to BigQuery quotas (storage, query, DML) | Subject to Cloud Storage/external storage limits and BigQuery query limits | Subject to BigQuery-managed Iceberg table quotas and Cloud Storage limits |
| Cost Factors | BigQuery storage pricing (active/long-term), query pricing (on-demand/capacity-based) | Cloud Storage costs for data, BigQuery query costs (Omni for multi-cloud), potential compute costs for external engines | Cloud Storage costs for data storage, BigQuery query costs, BigLake-table management costs |
Key factors to consider: Making the right choice for your data strategy
Choosing the optimal table format among BigQuery Native, BigLake Iceberg, and BigLake Iceberg tables in BigQuery depends heavily on your specific use cases, existing data ecosystem, and strategic priorities. While the comparison table provides a detailed breakdown, here are the key factors you should weigh:
Storage format:
- Consider BigQuery Native for its proprietary, highly optimized columnar format which gives the best performance. A lot of advanced capabilities are not available in BigLake and Iceberg
- BigLake Iceberg and BigLake Iceberg tables in BigQuery use Apache Parquet, an open-source columnar format, which offers broad multi-engine interoperability. This is the most crucial deciding factor.
Data location & sovereignty:
- Choose BigQuery native tables if you prefer Google to manage all aspects of your data storage, simplifying infrastructure management.
- Opt for BigLake Iceberg tables or BigLake Iceberg tables in BigQuery if you need to store data in your own google cloud storage for reasons like cost control, or leveraging existing data lake investments.
Metadata support & external integrations:
- If tight integration with the BigQuery ecosystem and managed metadata is paramount, BigQuery Native or BigLake Iceberg tables in BigQuery are suitable.
- For extensive use of external compute engines and open metadata catalogs (like Hive Metastore, AWS Glue, or BigQuery Metastore), BigLake Iceberg tables offer greater flexibility.
Data security and access control:
- All three options provide strong security features. BigQuery Native offers comprehensive RLS/CLS and data masking. The same will be available in BigLake Iceberg as well as BigLake Iceberg tables in BigQuery in a few months. It is there on the product roadmap.
Snapshot and time travel support:
- For native BigQuery time travel, BigQuery Native and BigLake Iceberg tables in BigQuery are ideal, as they support configurable time travel (up to 7 days) without external dependencies.
- BigLake Iceberg tables rely on Iceberg’s native time travel which supports more flexibility but at the expense of added storage cost. The same feature is on the roadmap for BigLake Iceberg tables in BigQuery..
External reads and writes (Spark, Trino etc.):
- If interoperability with various data processing engines (Spark, Trino, Flink) is a primary requirement for both reads and writes, BigLake Iceberg and BigLake Iceberg tables in BigQuery are superior. BigQuery native tables interact via API connectors.
GCP quotas and limits:
- Be aware of the specific quotas and limits that apply to each table type, which can influence architectural decisions for very large-scale data.
Query performance:
- For best query performance, choose BigQuery native tables with highly optimized native storage. BigLake Iceberg tables in BigQuery offer table management and metadata-driven acceleration for performance close to parity with native tables. BigLake Iceberg tables managed by customers have the biggest variation and depend on multiple customer choices.
Storage optimization:
- For fully automated storage optimization by Google, BigQuery Native and BigLake Iceberg tables in BigQuery are the go-to.
- BigLake Iceberg tables require external management of Iceberg-specific optimizations, often through self-managed Spark jobs
Data types support:
- BigQuery generally supports a broad range of SQL data types. Ensure compatibility between the data types used in your source systems and the chosen BigQuery table type, especially when dealing with nested or complex structures.
CDC support:
- Consider BigQuery Native and BigQuery Iceberg Managed (on product roadmap) for BigQuery’s native CDC capabilities.
- BigLake Iceberg tables rely on external engines like Spark or Flink for CDC operations that leverage Iceberg’s native support for upserts and deletes.
Easy migration / flexibility:
- If vendor lock-in is a concern, the open format nature of BigLake Iceberg tables and BigLake Iceberg tables offer the most flexibility for future migration or multi-engine use.
Cost factors:
- Analyze the cost implications carefully. This includes BigQuery storage and query costs, as well as the costs associated with external storage (Cloud Storage) and any additional compute resources (e.g., Dataproc for Spark) required for managing BigLake/Iceberg tables.
Decision tree
To further simplify your decision-making process, the following diagram provides a quick visual guide to help you choose the most suitable BigQuery table type based on your primary needs.
Example: Enterprise use cases for selection
Understanding the technical differences is one thing, applying them to real-world scenarios is another. Here are some common enterprise scenarios demonstrating when and why you might choose each BigQuery table type:
Use case for BigQuery Native Table:
- Scenario: A large e-commerce company relies heavily on real-time analytics for its sales dashboards, inventory management, and fraud detection systems. They need immediate insights from transactional data flowing continuously into their system. Their primary analytical tool is BigQuery, and they require strong data consistency, high-speed querying, and out-of-the-box security features without managing underlying storage infrastructure.
Why BigQuery Native:
- Native Streaming: BigQuery’s native streaming capabilities ensure that transactional data is available for analysis within seconds, critical for real-time dashboards and fraud detection.
- Full CRUD & Performance: The ability to perform direct DML operations and BigQuery’s optimized internal storage delivers the necessary performance for complex analytical queries and quick data updates.
- Managed Service: The company wants to offload infrastructure management, allowing their data team to focus purely on data analysis and business value. BigQuery’s fully managed nature, including automatic re-clustering and optimization, fits this need perfectly.
- Integrated Security: Built-in RLS, CLS, and data masking are essential for complying with data privacy regulations (e.g., GDPR, CCPA) when dealing with customer order information.
Use case for BigLake Iceberg tables in BigQuery:
- Scenario: A financial services firm has established a data lake on Cloud Storage for its raw historical transaction data, customer interactions, and compliance logs. They are adopting an open-source data lakehouse strategy but want to leverage BigQuery’s powerful query engine and managed services for their primary analytical workloads and data mart creation. They need the benefits of an open table format (Iceberg) for interoperability with certain Spark-based ETL pipelines, but they also want BigQuery to handle the complexities of metadata management, table optimization, and data consistency for their core analytical layer.
Why BigLake Iceberg tables in BigQuery:
- Managed service with open format: The firm gets the best of both worlds: BigQuery’s fully managed capabilities (automatic re-clustering, compaction) applied to their data in Cloud Storage, combined with the open format (Iceberg) for interoperability with Spark jobs that might process raw data or enrich it.
- Simplified operations: BigQuery manages the Iceberg metadata and table lifecycle, reducing the operational overhead typically associated with managing Iceberg tables manually, allowing the data engineering team to be more agile.
- Data consistency: BigQuery’s management ensures data consistency when performing DML operations directly from BigQuery, which is vital for financial reporting and auditing.
- Future flexibility: While BigQuery manages the table, the data remains in an open format, providing a pathway for seamless integration with other Iceberg-compatible tools or potential future migration if the data strategy evolves.
Use case for BigLake Iceberg Table:
- Scenario: A global pharmaceutical company has vast amounts of scientific research data, clinical trial results, and genomic sequences stored across various cloud storage platforms due to historical acquisitions and multi-cloud strategies. Their core ETL and table management processes are already standardized on Apache Spark and other third-party data engines (e.g., Snowflake, Trino). They need BigQuery primarily for querying and analytics, but the table lifecycle (data ingestion, optimization, compaction) must remain under the control of their existing external compute infrastructure. For this use case, maximizing performance within BigQuery is of secondary importance to maintaining maximum flexibility and control by the external engine.
Why BigLake Iceberg:
- External Engine Control: The organization maintains full control over the Iceberg table lifecycle, with Spark and other external tools managing data ingestion (ETL/ELT), optimization, and garbage collection. BigQuery acts solely as a query engine.
- Data location flexibility: The company can keep its massive datasets in their existing cloud storage buckets (e.g., GCS), avoiding costly data migration to a single vendor’s internal storage.
- Multi-engine interoperability: BigLake allows BigQuery to query the Iceberg tables directly, while Spark and other engines can perform read/write operations directly on the underlying Parquet files, enabling a flexible lakehouse architecture that accommodates diverse analytical tools.
- Open format: The use of Apache Iceberg and Parquet ensures vendor neutrality and long-term flexibility, allowing the company to adapt its compute strategy without data lock-in.
Conclusion
The integration of Apache Iceberg support marks a pivotal moment for BigQuery, transforming it into an even more versatile and powerful analytical platform. The days of choosing between a proprietary data warehouse and an open data lake are truly behind us. You now have a spectrum of robust options. The choice between BigQuery native tables, BigLake Iceberg tables, and BigLake Iceberg tables in BigQuery depends heavily on your specific organizational requirements, data governance policies, existing infrastructure, and performance expectations.
- BigQuery Native Tables offer unparalleled simplicity, performance, and integrated security when you’re comfortable with Google managing your data storage entirely.
- BigLake Iceberg Tables provide the flexibility to keep data in your own storage while still leveraging BigQuery’s powerful query engine, especially for multi-cloud and lakehouse use cases, emphasizing open formats and interoperability.
- BigLake Iceberg tables in BigQuery bring the power of fully managed experience with advanced enterprise capabilities of BigQuery to open Iceberg format.
By carefully evaluating all these factors—multi-engine interoperability, advanced data warehouse capabilities, price-performance —you can select the table type that best aligns with your current and future data strategy, empowering your organization with efficient and effective data analytics.
Authors:
- Guruprasad Srinivas Rao ( @sguruprasad ), Strategic Cloud Data Engineer
- Avani Garg ( @AvaniGarg ), Technical Solutions Consultant
Reviewer:
- Yuriy Zhovtobryukh, Senior Product Manager, GCP BigQuery Suite
- Abhimanyu Mitra, Data Analytics Customer Engineer