This article details the deep integration of StarRocks with Apache Paimon, describing the unified architecture, version evolution, performance enhancements, time‑travel queries, native readers/writers, distributed planning, and future roadmap for achieving lakehouse‑native analytics at scale.
This article details Gaode Maps' end‑to‑end lakehouse solution for massive, high‑frequency trajectory data, covering the challenges of real‑time visibility, query performance, and storage cost, and explaining how a hot‑warm‑cold tiering architecture built on Apache Flink, Paimon, StarRocks, Redis and Lindorm delivers millisecond‑level queries while cutting storage expenses.
This article examines Apache Paimon’s innovative lakehouse architecture, detailing its LSM‑Tree storage, flexible merge engine, and multi‑engine integration, and showcases two real‑world deployments—an operator’s real‑time fraud‑prevention system and a manufacturing firm’s unified data platform—highlighting performance gains and cost reductions.
StarRocks and Apache Paimon have been progressively integrated across multiple releases, enabling a unified lakehouse architecture that supports multi-source federated analysis, time-travel queries, native readers/writers, distributed planning, and advanced profiling, while delivering performance gains that bring Paimon query speed on par with native StarRocks tables.
This article explains the challenges of frequent column changes in AI feature engineering, introduces Paimon’s column‑separation storage with a global continuous Row ID, details its Blob data type for efficient multi‑modal handling, and outlines production results and future roadmap for building an AI‑native data lakehouse.
The 2025 Apache Paimon release brings major performance boosts, AI‑centric multimodal storage, deeper streaming‑batch integration, and broader engine compatibility, detailing query and write optimizations, memory management tweaks, and a unified lake format for structured and unstructured data.
Hisense Juhau, an AI‑enabled TV cloud service, overhauled its massive offline‑centric data platform by adopting a real‑time data lake, compute‑storage separation, and serverless Spark/StarRocks on Alibaba Cloud, achieving sub‑5‑minute data freshness, elastic scaling, and dramatically improved performance for personalized content recommendation and smart operations.
This article explains the concept of a real‑time lakehouse, compares it with traditional batch warehouses, introduces Apache Paimon and its innovations such as native upserts, LSM storage, tags and branches, and showcases multiple enterprise use cases that demonstrate its low‑cost, low‑latency stream‑batch integration.
This article analyzes the limitations of a traditional Flink + Talos + Iceberg real‑time lakehouse, introduces Apache Paimon's lakehouse table format and LSM storage, and demonstrates three practical use cases—partial‑update widening, streaming upsert, and lookup join—showing cost, stability, and performance improvements while outlining future roadmap.
This article explains Apache Paimon's snapshot expiration mechanism, comparing synchronous and asynchronous execution modes, their advantages and drawbacks, and how table properties control expiration to balance data consistency, performance, and back‑pressure in large‑scale data processing systems.
This article explains how Apache Paimon guarantees data timeliness and consistency through snapshot files, two‑phase commit, and configurable expiration policies, and it outlines practical optimization and cleanup techniques for maintaining efficient storage and query performance.
This article provides a comprehensive overview of Apache Paimon, covering its real‑time lake ingestion, unified stream‑batch processing, table types (primary‑key and append‑only), LSM‑tree storage, bucket mechanisms, merge‑engine options, compaction strategies, concurrency control, consumption methods, tag management, data cleanup, and system tables for big‑data workloads.
This article explains how Apache Paimon can be used as a real‑time state store for Flink, detailing its low‑cost, scalable storage, lookup‑join design, table schema, bucket configuration, memory tuning, and practical use cases such as handling refund‑adjusted order tags and cumulative metrics.
The 8th Tongcheng Travel Big Data Technology Salon in Suzhou featured four expert talks covering Tencent Cloud’s Meson Spark engine, near‑line computing for travel itineraries, a Flink‑based real‑time risk control system, and Apache Paimon’s latest lake‑warehouse innovations, followed by a data‑driven business perspective session.
This article introduces Apache Paimon, an open‑source table format that supports batch and streaming reads and writes, explains its architecture, unified storage model, and core concepts such as file layout, snapshots, manifests, data files, partitions, and consistency guarantees.
The Apache Paimon 0.9.0 release introduces production‑ready Branch support, native Iceberg compatibility, a caching catalog for faster OLAP queries, improved Bucketed Append tables with reduced small‑file issues, and full DELETE/UPDATE/MERGE‑INTO capabilities for Append tables, making the system more usable and efficient.
This article presents Xiaomi's real-time lakehouse architecture, outlines its current challenges, introduces Apache Paimon and several use‑case scenarios—including stream join optimization, streaming upserts, and lookup joins—while discussing expected benefits and future directions for a more efficient, unified data platform.
This article presents a comprehensive overview of Apache Paimon, covering the concept of real‑time lakehouses, the underlying technologies such as LSM and merge‑on‑write, practical application cases across enterprises, and the latest frontier features like tags, branches, and advanced indexing, illustrating how Paimon bridges batch and streaming workloads in modern big‑data ecosystems.
This article shares a personal, experience‑driven overview of Apache Paimon, highlighting its design simplicity, key capabilities such as schema evolution, stream‑batch unified processing, primary‑key support, and closed‑loop data handling, while discussing when its features are appropriate for production environments.
This guide provides a comprehensive overview of Apache Paimon’s architecture, key features, and advantages, explains how to integrate it with StarRocks for real‑time lakehouse analytics, and walks through a complete quick‑start setup including component installation, Flink and Kafka deployment, data ingestion, table creation, and query execution with time‑travel support.
This article provides a practical deep‑dive into StarRocks and Apache Paimon, covering data‑lake fundamentals, the technical advantages of both platforms, performance gains over traditional engines, step‑by‑step migration strategies, deployment options on Alibaba Cloud EMR, and future roadmap plans.
The DataFunCon2024 Beijing conference on July 5‑6 showcases a series of technical talks about real‑time lakehouse architectures, big‑data analytics, and cloud‑native data warehouses, offering practitioners insights into Apache Paimon, SelectDB, and Doris implementations for faster, more agile data processing.
This article summarizes Wang Feng's presentation on the next‑generation Lakehouse architecture, explaining how Apache Paimon provides a unified, real‑time data lake format that bridges batch and streaming workloads, enabling low‑latency analytics and AI integration for modern big‑data applications.
The article introduces Apache Paimon 0.8, highlighting new Deletion Vectors, a universal file index, memory and I/O optimizations, record‑level TTL, and integration improvements with Flink and Spark, while also discussing broader lake‑house performance trends and future directions.
The article reviews Apache Paimon's graduation to an Apache Top-Level Project, outlines the essential capabilities of modern lakehouse frameworks—including streaming and batch I/O, multi‑engine integration, and advanced features—and discusses the problems they solve and the promising direction of the lakehouse ecosystem.
This article explains how to build a high‑performance data‑lake analytics stack by combining StarRocks with Apache Paimon, covering direct queries, Data Cache acceleration, and asynchronous materialized views, and presents benchmark results that show StarRocks achieving up to 4.3× faster query speeds than Trino and significant latency reductions with caching and materialized views.
Apache Paimon, originally launched as Flink Table Store, has graduated to an Apache Top‑Level Project after a year of incubation, showcasing real‑time lakehouse capabilities, extensive ecosystem integration, and strong adoption by major enterprises, marking a significant milestone for streaming and batch data processing.
Apache Paimon 0.7.0 introduces significant improvements such as optimized lookup join handling, new CDC functionalities, and tighter Spark/Hive integration, while also highlighting practical considerations for using lake‑table lookups in production environments.
Apache Paimon provides multiple table modes—including primary key tables with fixed or dynamic buckets, Append scalable and queue tables—each with specific configurations, compaction behavior, and suitable scenarios, and the article explains their structures, performance considerations, and how to use them with Flink.
The Flink Forward Asia 2023 conference showcased major updates to Apache Flink (versions 1.17 and 1.18), introduced the Apache Paimon lakehouse project, announced Flink CDC 3.0, and highlighted community growth, cloud‑native deployments, and real‑time data‑warehouse use cases across industry leaders.
This article provides an in‑depth overview of Apache Paimon as a streaming lakehouse, explains its core features, file layout, consistency guarantees, and offers detailed guidance on integrating and tuning Paimon with Apache Flink for both write and read performance, multi‑writer concurrency, table management, and bucket rescaling.
The article describes how NetEase News' recommendation team replaced a slow, batch‑oriented data‑warehouse pipeline with a Flink‑based, Apache Paimon real‑time wide‑table solution that supports partial updates, reduces latency from hours to minutes, and lowers processing costs while handling both deduplication and non‑deduplication recommendation scenarios.
This article explains how Apache Paimon simplifies CDC data lake ingestion with one‑click, low‑cost, low‑latency pipelines, details its architecture and tag‑based Hive compatibility, provides best‑practice configurations, and presents benchmark results showing Paimon outperforming Hive and Hudi in both write and query performance.
The article traces the shift from traditional Hive‑based warehouses to modern lakehouse architectures, explains the advantages of lake formats, introduces Apache Paimon as a streaming‑first data lake integrated with Flink, presents performance benchmarks showing its superiority over Hudi, and demonstrates a real‑time streaming lakehouse workflow.
Apache Paimon was adopted at Tongcheng Travel to replace Hudi, achieving three‑fold write speed gains and ten‑fold query acceleration, with detailed discussion of lakehouse challenges, performance issues, migration steps, configuration examples, and future plans for the platform.
Apache Paimon is an open‑source streaming data lake storage system that combines LSM‑based real‑time updates, open file formats, and deep integration with Flink, Spark, and Trino to deliver high‑throughput ingestion, low‑latency queries, and unified batch‑stream processing for modern big‑data workloads.
Apache Paimon, newly incubated by the Apache Software Foundation, combines Flink's real‑time streaming capabilities with open lakehouse storage formats, offering high‑throughput, low‑latency data ingestion, partial‑update merges, and seamless integration with engines like Flink, Spark, and Trino for unified batch and streaming analytics.
