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clickzetta-realtime-sync-pipeline

Automates real-time single-table data synchronization from Kafka, MySQL, PostgreSQL, or SQL Server into Lakehouse.

8|3|Updated Mar 19, 2026
Search Tags:#postgresql#real-time#mysql#kafka#lakehouse#cdc#data-sync
One-click install
npx skills add https://github.com/yunqiqiliang/clickzetta-skills --skill clickzetta-realtime-sync-pipeline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: clickzetta-realtime-sync-pipeline
Source: https://github.com/yunqiqiliang/clickzetta-skills/tree/main/clickzetta-realtime-sync-pipeline
Command: npx skills add https://github.com/yunqiqiliang/clickzetta-skills --skill clickzetta-realtime-sync-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Creates and manages ClickZetta Lakehouse real-time single-table synchronization tasks from external sources to the Lakehouse, enabling low-latency data freshness and continuous operation.

Core Features & Use Cases

  • Continuous streaming real-time synchronization from a single source table or Kafka topic to Lakehouse.
  • Supports sources such as Kafka, MySQL, PostgreSQL, and SQL Server for CDC-style replication and low-latency data delivery.
  • Field mapping support, including JSONPath-based extractions and optional computed columns, plus deployment and operations via Studio.

Quick Start

Submit a real-time sync task in Studio, configure the source and Lakehouse sink, then publish to start continuous streaming.

Frequently Asked Questions about clickzetta-realtime-sync-pipeline

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

FAQPage Schema
How do I set up real-time data sync from MySQL to a Lakehouse?▼

You can set up real-time data sync from MySQL to a Lakehouse by submitting a streaming task in Studio, configuring the source and sink, mapping fields via JSONPath, and publishing the task for continuous operation.

What is CDC-style replication and how does it apply to PostgreSQL streaming into a Lakehouse?▼

CDC-style replication for PostgreSQL streaming into a Lakehouse captures continuous source table changes and applies them to target tables, ensuring low-latency data freshness and continuous operation without batch processing delays.

Can I use JSONPath field mappings for Kafka data synchronization tasks?▼

Yes, Kafka data synchronization tasks support JSONPath field mappings to extract specific fields from source topics, configure optional computed columns, and load structured data directly into Lakehouse tables.

Does real-time single-table data sync support SQL Server sources?▼

Yes, real-time single-table data sync supports SQL Server sources alongside MySQL, PostgreSQL, and Kafka, enabling continuous streaming replication and low-latency data delivery into Lakehouse tables.

What is the best way to monitor continuous streaming data pipelines from external databases?▼

The best way to monitor continuous streaming data pipelines is through Studio's deployment and operations interface, which manages task creation, source and sink configuration, and tracks low-latency data delivery status.

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