Building a New Lakehouse Analytics Paradigm with StarRocks and Paimon

Introduction: The article presents a lakehouse analytics paradigm based on StarRocks and Paimon, outlining their combined use cases and key technologies.

Data lake analysis technology development: Discusses the shift from ETL‑based data warehouses to ELT‑based data lakes, the emergence of the four “lakehouse swords” (Iceberg, Hudi, Delta, Paimon) and their challenges such as batch updates, ACID transactions, concurrency, and schema evolution.

StarRocks overview: Describes StarRocks as a fast, unified analytical engine, its architecture spanning storage (HDFS, object storage) to query layers, support for external tables (Hive, Elastic, MySQL), performance improvements from version 1.x to 3.x, and major features including external table materialized views, JSON/map/struct support, IO merging, pipeline optimization, and C++ vectorized execution.

Use cases of StarRocks + Paimon: Covers federated queries across multiple lake formats, transparent acceleration via materialized views, data modeling with layered warehouse architecture (ODS, DWD, DWS, ADS), and cold‑hot data fusion using partitioned materialized views to accelerate recent data while efficiently handling historical data.

Key technical principles: Explains the JNI Connector that enables C++ StarRocks to read Java‑based data sources like Paimon, the memory layout handling for fixed‑ and variable‑length types, and performance optimizations such as native readers, metadata caching, and efficient memory copying.

Future roadmap: Plans include supporting Append‑Only tables, optimizing date/datetime handling, native reader acceleration, column statistics, metadata cache, time‑travel and snapshot queries, and sink capabilities to write back to Paimon, enhancing the lakehouse analysis ecosystem.

Conclusion: Summarizes the presented content and thanks the audience.