This PIP is co-worked by Ming Li , Guojun Li and Fang Yong .

Motivation

Paimon is a Streaming Lakehouse, Flink streaming and batch jobs can use Paimon tables as data sources and sinks. Flink can be combined with Paimon to complete the entire ETL processing, and the overall process is as follows.

We would like to manage Flink Streaming & Batch ETL processing in Paimon based on the current ability of Flink, including:

1. Table lineage in Paimon. We want to create lineage between Paimon source and sink tables based on Flink job name, then we can the entire ETL topology for Paimon tables and Flink jobs.

2. Data lineage for Table. Flink streaming job creates a snapshot for Paimon table when it completes a checkpoint, we can create the snapshot lineage for source and sink tables.

Based on the above lineage, we could support the following capabilities in the integrated Flink ETL processing and Paimon tables

1. Query Paimon tables dependency, which allows users to manage their tables and Flink jobs better.

2. Locating and analyzing issues. Describe the problems you are trying to solve.

Public Interfaces

Briefly list any new interfaces that will be introduced as part of this proposal or any existing interfaces that will be removed or changed. The purpose of this section is to concisely call out the public contract that will come along with this feature.

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