Can it help with data transformation during ingestion?

Absolutely. It provides specific configurations for Single Message Transforms (SMTs) to mask sensitive data, route messages, or flatten nested JSON structures.

Does it support Change Data Capture (CDC)?

Yes, it includes detailed patterns and configurations for Debezium CDC across various databases like MySQL, PostgreSQL, and MongoDB.

How do I troubleshoot failed connector tasks using this skill?

The skill provides specific REST API commands to check status, validate configurations, and restart tasks, along with solutions for common schema evolution issues.

What is the Confluent Kafka Connect skill?

It is a specialized capability for Claude Code that provides expert knowledge on building, managing, and troubleshooting Kafka Connect data pipelines and connectors.

Confluent Kafka Connect

Name: Confluent Kafka Connect
Author: anton-abyzov

byanton-abyzov

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数据库管理

Configures and optimizes Kafka Connect pipelines for seamless data movement between databases, cloud storage, and search indexes.

关于

This skill provides deep expertise in Confluent Kafka Connect, enabling developers to build, configure, and troubleshoot robust data pipelines. It covers a wide range of source and sink connectors—including JDBC, Debezium for Change Data Capture (CDC), Amazon S3, and Elasticsearch—alongside complex Single Message Transforms (SMTs) for data masking, routing, and flattening. Whether you are setting up real-time database streaming or archiving event logs to a data lake, this skill provides the configuration patterns, best practices, and monitoring commands needed to ensure reliable, idempotent data integration.

主要功能

Debezium CDC implementation for real-time database change capture
Integration patterns for Schema Registry and Avro serialization
13 GitHub stars
Advanced Single Message Transforms (SMTs) for on-the-fly data manipulation
Comprehensive connector configuration for JDBC, S3, and Elasticsearch
Idempotent pipeline design and advanced error handling strategies

使用场景

Archiving high-volume Kafka topics to AWS S3 for long-term storage and analytics
Indexing streaming events into Elasticsearch for real-time search and monitoring
Streaming real-time database changes (CDC) to Kafka using Debezium

关于

主要功能

Debezium CDC implementation for real-time database change capture
Integration patterns for Schema Registry and Avro serialization
13 GitHub stars
Advanced Single Message Transforms (SMTs) for on-the-fly data manipulation
Comprehensive connector configuration for JDBC, S3, and Elasticsearch
Idempotent pipeline design and advanced error handling strategies

使用场景

Archiving high-volume Kafka topics to AWS S3 for long-term storage and analytics
Indexing streaming events into Elasticsearch for real-time search and monitoring
Streaming real-time database changes (CDC) to Kafka using Debezium