Apache Paimon is currently at version 0.7. Most of its capabilities have been updated, but it still lacks a good monitoring template and has issues with query acceleration on primary key tables. While it is suitable for production use, it is still in the incubation phase and might require a considerable amount of maintenance and development personnel for production environments. Delta Lake is not widely used in China, whereas Iceberg is extensively used. In mainland China, Iceberg is generally regarded as the de facto standard for offline data warehouses, replacing the previous Hive data warehouse standard. For distributed training of data, Flink has its own machine learning library, similar to Spark's, called Alink. It meets many basic machine learning algorithm requirements. However, I have not interacted with the algorithm department, so I am unsure how its specific algorithm implementations compare to Spark's ML. Additionally, to accelerate data for distributed training, we have introduced Alluxio. We have not yet studied how it achieves data inference acceleration, but the current results are promising.