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Data & Analytics
Data Pipeline Monitor
Monitor ETL pipelines and data quality with Dagster. Track pipeline runs, catch data anomalies, and ensure reliable data delivery.
Claude Code Codex Cursor
Overview
Dagster is a modern data orchestration platform that lets you define, test, and monitor data pipelines as software-defined assets. Unlike traditional ETL tools, Dagster treats pipelines as code, with type checking, testing, and observability built in from the start.
AI agents can help build and maintain Dagster pipelines by generating asset definitions, configuring resources, writing data quality checks, and debugging pipeline failures. When a pipeline run fails, the agent can analyze the error logs, trace the issue to a specific asset or resource, and implement the fix.
The monitoring capabilities are where AI agents add the most value. Your agent can query Dagster's GraphQL API to check pipeline status, identify failed runs, analyze execution times, and set up alerts for data quality anomalies. This proactive monitoring ensures your data pipelines deliver reliable data to downstream consumers.
AI agents can help build and maintain Dagster pipelines by generating asset definitions, configuring resources, writing data quality checks, and debugging pipeline failures. When a pipeline run fails, the agent can analyze the error logs, trace the issue to a specific asset or resource, and implement the fix.
The monitoring capabilities are where AI agents add the most value. Your agent can query Dagster's GraphQL API to check pipeline status, identify failed runs, analyze execution times, and set up alerts for data quality anomalies. This proactive monitoring ensures your data pipelines deliver reliable data to downstream consumers.
Who Is This For?
- Data engineers building ETL pipelines with Dagster assets and resources
- Teams monitoring pipeline health and debugging failed runs with AI assistance
- Engineers implementing data quality checks and validation rules
- Developers setting up automated data pipeline alerting and reporting
Installation
Setup for Claude Code
pip install dagster dagster-webserver
Claude Code generates Dagster assets and runs: dagster devConfiguration
# pyproject.toml (Dagster)
[tool.dagster]
module_name = "my_pipeline"
# workspace.yaml
load_from:
- python_module: my_pipelineRelated Skills
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