clickzetta-table-stream-pipeline

Orchestrate Table Stream CDC pipelines with change_tracking and MERGE consumption.

8|3|Updated Mar 19, 2026
One-click install
npx skills add https://github.com/yunqiqiliang/clickzetta-skills --skill clickzetta-table-stream-pipeline
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: clickzetta-table-stream-pipeline
Source: https://github.com/yunqiqiliang/clickzetta-skills/tree/main/clickzetta-table-stream-pipeline
Command: npx skills add https://github.com/yunqiqiliang/clickzetta-skills --skill clickzetta-table-stream-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Orchestrates end-to-end Table Stream change data capture (CDC) pipelines to reliably track and deliver table changes for incremental ETL, auditing, and downstream processing across a data lake or warehouse.

Core Features & Use Cases

  • Enable source table change tracking and create Table Streams to capture INSERTS/UPDATES/DELETES
  • Support STANDARD and APPEND_ONLY modes to cover data synchronization and historical auditing
  • Provide idempotent consumption via MERGE to ensure no duplicates during ETL
  • Preview changes and manage offsets to facilitate fault-tolerant, resumable pipelines
  • Suitable for real-time data replication, incremental ETL, and audit trails across data platforms

Quick Start

Enable change_tracking on the source table, create a TABLE STREAM on the source, prepare a target, and consume via MERGE for an end-to-end CDC pipeline.

Frequently Asked Questions about clickzetta-table-stream-pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build an incremental ETL pipeline using change data capture?

To build an incremental ETL pipeline, enable change_tracking on the source table, create a TABLE STREAM to capture inserts, updates, and deletions, prepare the target table, and consume changes via MERGE for idempotent synchronization.

What is the difference between STANDARD and APPEND_ONLY modes in table stream CDC?

STANDARD mode captures all inserts, updates, and deletions for data synchronization, while APPEND_ONLY mode captures only new inserts, making it suitable for historical auditing and log-based data tracking without update overhead.

How can I prevent duplicate records during incremental ETL?

You can prevent duplicates during incremental ETL by applying offset-based consumption and using MERGE operations, which ensures idempotent data delivery and reliable synchronization even if the pipeline is rerun.

Can I preview table changes before committing them to my target data warehouse?

Yes, the pipeline supports data preview functionality, allowing you to inspect captured table stream changes before applying them, which facilitates fault-tolerant processing and ensures accurate downstream delivery.

How do I make a resumable CDC pipeline after a failure?

A resumable CDC pipeline is achieved through offset-based consumption and change_tracking management, allowing the pipeline to restart from the last processed offset without losing data integrity or duplicating records.

When should I use table stream CDC instead of full batch loads?

Use table stream CDC for near real-time data replication and incremental ETL when source tables change frequently, as it tracks only modifications rather than repeatedly processing entire datasets.