Apache Paimon: Real-Time Lake Storage with Iceberg Compatibility 2025

Introduction

In the rapidly evolving landscape of big data and artificial intelligence, the need for unified, efficient, and scalable storage solutions has never been more critical. Apache Paimon emerges as a revolutionary real-time lake format that bridges the gap between traditional batch processing and modern streaming requirements, while simultaneously addressing the growing demands of multimodal AI applications.

This comprehensive exploration delves into Apache Paimon's innovative architecture, its seamless integration with Apache Flink, and its groundbreaking compatibility with Apache Iceberg. We'll examine how Paimon's unique Log-Structured Merge-tree (LSM) implementation enables unprecedented performance in streaming data lakes, and how its multimodal storage capabilities position it as a cornerstone technology for the future of AI-driven data infrastructure.

The Evolution of Data Lake Technologies: From Hive to Paimon

The Historical Timeline of Data Lake Development

The journey of data lake technologies began in 2008 with Apache Hive, which introduced the foundational components of the big data ecosystem: Hive Metastore, Hive SQL, and Hive Tables. Built on HBase, Hive established the cornerstone of early data lake architectures.

As data volumes grew exponentially, the limitations of row-based storage became apparent, catalyzing the development of columnar storage formats like ORC and Parquet. These formats delivered significant efficiency gains, particularly for analytical workloads accessing only subsets of columns from wide tables.

The next evolutionary step brought table formats including Apache Hudi, Apache Iceberg, and Delta Lake. These technologies provided enhanced control over file organization and metadata management, enabling ACID transactions, delete and update operations, and sophisticated merge capabilities that were previously challenging in traditional data lake architectures.

The Birth of Apache Paimon

Recognizing the growing importance of streaming data processing, the Apache Flink community initiated Flink Table Store, which evolved into Apache Paimon. Unlike previous table formats designed primarily for batch processing, Paimon represents a fundamental shift toward real-time lake formats optimized for streaming applications.

The release of Apache Paimon 1.0 and 1.2 versions demonstrates production readiness, with validation across major technology companies handling petabytes of data and supporting demanding real-time analytics workloads.

Understanding Apache Paimon's Core Architecture

The Streaming Lake House Paradigm

Apache Paimon introduces the concept of a "streaming lake house," enabling real-time data processing without sacrificing the scalability and cost-effectiveness of data lake storage. This architecture provides several transformative capabilities:

Real-time ingestion becomes the default mode, reducing data latency from hours to minutes or seconds. This capability proves invaluable for fraud detection, real-time personalization, and operational monitoring where timely access to fresh data drives business success.

Full pipeline streaming allows organizations to selectively convert batch workloads to streaming pipelines, optimizing data processing latency for critical business processes while maintaining batch processing where appropriate.

LSM-Tree: The Foundation of Real-Time Performance

The cornerstone of Apache Paimon's exceptional streaming performance lies in its implementation of the Log-Structured Merge-tree (LSM)data structure. LSM-trees have proven effective in numerous real-time systems including RocksDB and ClickHouse, making them the standard for high-throughput write applications.

Paimon distinguishes itself as the only lake format successfully combining LSM-tree technology with data lake storage paradigms. This combination delivers write performance characteristics of real-time databases while maintaining data lake scalability and cost-effectiveness.

The LSM implementation utilizes Parquet as the underlying storage format, providing both write optimization and analytical query performance. Minor compaction operations allow incremental storage optimization, balancing write and read performance without requiring full table rebuilds.

Performance Benchmarking and Validation

Performance benchmarking on Alibaba Cloud demonstrates Paimon's superior update performance compared to Apache Hudi and Apache Iceberg in streaming workloads, with benefits becoming more pronounced as table sizes increase and update frequencies intensify.

Advanced Features: Schema Evolution and CDC Integration

Streaming Schema Evolution Capabilities

Apache Paimon provides robust schema evolution support through two complementary approaches. Flink SQL CDC enables direct streaming ingestion from database sources with automatic schema evolution, while Paimon CDC handles scenarios where data flows through Apache Kafka.

Both approaches support sophisticated schema evolution scenarios, including nested schema changes that are particularly challenging in traditional data lake architectures. When source systems introduce new fields or modify existing structures, Paimon automatically adapts without requiring manual intervention or pipeline restarts.

Industry Adoption and Real-World Use Cases

Major technology companies demonstrate Paimon's practical value through extensive production deployments that showcase its scalability and reliability under demanding conditions. These real-world implementations provide compelling evidence of Paimon's readiness for enterprise-scale deployments.

Alibaba Group

Alibaba Group manages hundreds of petabytes across Taobao and Tmall, with individual tables processing up to 40 million rows per second while achieving unified streaming and batch processing capabilities. This massive scale deployment demonstrates Paimon's ability to handle the most demanding e-commerce workloads where data freshness directly impacts business outcomes.

Vivo

Vivo's migration from traditional Hive tables to Paimon enabled advanced features including data sorting and data skipping, significantly improving query performance for their analytical workloads. The implementation of CDC-based real-time ingestion replaced batch-oriented data loading processes, reducing data latency and improving business insight timeliness.

Bytedance & TikTok

ByteDance and TikTok leverage Paimon for real-time streaming pipelines supporting high-velocity social media applications. These platforms generate massive volumes of user interaction data requiring real-time processing for content recommendation, trend analysis, and user engagement optimization.

Shopee

Shopee's implementation demonstrates Paimon's value for e-commerce applications where data freshness directly impacts business outcomes. The improved data freshness enabled by Paimon's streaming capabilities supports real-time inventory management, dynamic pricing, and personalized recommendation systems.

Common success patterns across all implementations include streaming updates replacing batch data loading processes, streaming change log generation enabling real-time system reactions, and data skipping capabilities delivering substantial query performance improvements that scale with table size and query selectivity.

Ecosystem Integration and Compatibility

Comprehensive Engine Support

Apache Paimon provides native support for major data processing engines, ensuring adoption without wholesale infrastructure changes. Deep integration with Apache Flink leverages streaming capabilities for minimal latency, while Apache Spark integration supports both batch and structured streaming workloads.

Support for Apache Hive ensures backward compatibility, enabling gradual migration strategies. Integration with modern analytical engines like StarRocks, Apache Doris, and Trino provides high-performance query capabilities for interactive analytics and business intelligence applications.

Apache Iceberg Compatibility

Bridging Real-Time and Ecosystem Requirements

Apache Paimon's Iceberg compatibility addresses one of the most challenging aspects of modern data architecture: enabling real-time processing while maintaining compatibility with existing Iceberg-based ecosystems.

The fundamental challenge stems from architectural differences between Paimon's LSM-based approach and Iceberg's traditional file management. Historically, Paimon could generate Iceberg snapshots but couldn't include real-time files still undergoing LSM operations.

Technical Implementation with Deletion Vectors

The introduction of deletion vector files in Iceberg V3 provided the technical foundation to bridge this gap. When Paimon tables are configured with Iceberg compatibility, the system automatically generates Iceberg snapshots including real-time data files with corresponding deletion vectors.

This integration provides minute-level latency access to real-time data updates, representing significant improvement over traditional batch processing approaches. Organizations can leverage Paimon's superior streaming performance while maintaining full compatibility with existing Iceberg-based analytical infrastructure.

Multimodal AI Storage: Future-Ready Infrastructure

Addressing AI Data Requirements

The emergence of multimodal artificial intelligence creates fundamentally different storage requirements. Traditional AI applications focused on structured data and text, but multimodal AI models processing combinations of text, images, audio, and video require storage systems designed for diverse data types with complex relationships.

Current AI data persistence approaches are often inefficient, resulting in significant resource waste. Organizations maintain separate storage systems for different data types, creating data silos that complicate multimodal AI application development and deployment.

Lance File Format Integration

Apache Paimon's integration with Lance file format addresses multimodal AI storage requirements. Lance is specifically designed for large blob storage scenarios, providing optimized performance for AI and machine learning workload access patterns.

This integration enables organizations to store multimodal data alongside traditional structured data within unified storage architecture, eliminating complexity associated with maintaining separate storage systems while providing optimized performance for each data type.

Integration with AI-focused processing engines like Apache Arrow and Ray enables distributed machine learning directly against Paimon-stored data, eliminating data movement bottlenecks traditionally created in AI pipelines.

Production-Ready Platform: Alibaba Cloud DLF

Enterprise-Grade Implementation

Alibaba Cloud's Data Lake Formation (DLF)platform provides comprehensive, managed implementation of Paimon-based data lake infrastructure. DLF integrates core components including Paimon as primary lake format, comprehensive metadata management, and intelligent optimization features.

The platform's global deployment demonstrates production readiness and scalability, currently available in multiple regions including Singapore and Jakarta, with continued expansion planned to serve growing global demand for advanced data lake capabilities.

Conclusion: The Future of Unified Data Infrastructure

Apache Paimon represents a fundamental evolution in data lake technology, successfully bridging traditional batch processing and modern real-time streaming requirements while addressing emerging multimodal AI application needs. The technology's unique combination of LSM-tree architecture, comprehensive ecosystem integration, and forward-thinking AI workload support positions it as a cornerstone technology for next-generation data infrastructure.

The extensive production validation across major technology companies demonstrates enterprise deployment readiness at massive scale, with consistent performance benefits observed across diverse use cases providing compelling evidence of practical value and broad applicability. From e-commerce giants like Alibaba processing millions of transactions per second to social media platforms like TikTok handling massive user interaction volumes, Paimon has proven its capability to support the most demanding real-time analytics workloads.

Seamless Apache Iceberg integration ensures organizations can adopt Paimon without sacrificing existing infrastructure investments, while multimodal AI storage capabilities position the technology to address emerging market requirements that traditional data lake solutions cannot efficiently handle.

As data volumes continue growing exponentially and the demand for real-time insights intensifies across industries, Apache Paimon's unified streaming and batch processing approach provides a sustainable architectural foundation that evolves with changing business requirements. The technology's open-source nature and active community development ensure continued innovation and adaptation to emerging use cases, making it an essential component for organizations building modern data platforms.

For organizations evaluating their data infrastructure strategies, Apache Paimon offers a compelling combination of immediate practical benefits and long-term architectural flexibility that addresses both current streaming analytics needs and future multimodal AI requirements.

Frequently Asked Questions

What is Apache Paimon and how does it differ from Apache Iceberg?

Apache Paimon is a real-time lake format that combines LSM-tree architecture with traditional data lake capabilities, offering superior streaming performance compared to Apache Iceberg's batch-oriented design.

How does Apache Paimon's LSM-tree architecture improve performance?

The LSM-tree structure enables high-throughput writes and efficient compaction, allowing Paimon to handle millions of updates per second while maintaining analytical query performance.

Can Apache Paimon replace existing Iceberg deployments?

Yes, Apache Paimon offers Iceberg compatibility through V3 deletion vectors, enabling gradual migration while maintaining ecosystem compatibility.