{"categories":[{"label":"Build Tools","url":"https://skillfed.io/packages/category/software-development-build-tools/3"}],"enrichment":{"capability":"Integrates Monte Carlo data observability into Apache Airflow workflows via callbacks, hooks, and operators that send DAG and task events to Monte Carlo and execute data quality checks.","skillfed_tags":["airflow-provider","data-observability","data-quality"],"use_cases":["Send Airflow task and DAG events (success, failure, retry, SLA miss) to Monte Carlo for centralized incident detection and alerting.","Implement data quality gates in pipelines using SimpleCircuitBreakerOperator to block downstream tasks until custom SQL monitors pass.","Automatically report dbt run artifacts (models, tests, lineage) to Monte Carlo via DbtRunOperator and related dbt command operators.","Create custom operators extending BaseMcdOperator to implement domain-specific data observability logic using the pycarlo SDK.","Monitor data pipeline health and SLA compliance across Airflow DAGs without adding custom webhook or logging code to each task."],"what_it_does":"airflow-mcd is an Apache Airflow provider package that bridges Airflow workflows with Monte Carlo's data observability platform. It supplies callbacks that automatically notify Monte Carlo when DAGs and tasks succeed, fail, retry, or miss SLAs; hooks to create authenticated sessions with the Monte Carlo API; and operators including a circuit breaker for data quality gates and a suite of dbt operators that integrate dbt artifact reporting. The package is built on pycarlo, Monte Carlo's Python SDK, and integrates with Airflow's standard connection and configuration patterns.\n\nThe provider is designed for teams running data pipelines in Airflow who want centralized data quality monitoring and incident detection without manually instrumenting each task. It supports both broad, all-in-one callback patterns and granular, explicit callback selection, allowing integration into existing DAGs with minimal refactoring. The circuit breaker operator can halt downstream tasks if data quality rules fail, enforcing data contracts before dependent work executes.","worth_installing":"Yes, if you run Airflow and use Monte Carlo for data observability. The package has low install friction, active maintenance, no known vulnerabilities, and permissive licensing. It is well-suited for teams wanting to integrate data quality monitoring into existing Airflow workflows. Requires Airflow 1.10.14+ and a Monte Carlo account with API credentials configured in Airflow connections."},"id":"airflow-mcd","links":{"html":"https://skillfed.io/packages/airflow-mcd","md":"https://skillfed.io/packages/airflow-mcd.md","pypi":"https://pypi.org/project/airflow-mcd/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-07","license_spdx":null,"license_treatment":"permissive","name":"airflow-mcd","python_support":"supports_current","summary":"Monte Carlo's Apache Airflow Provider"},"popularity":{"monthly_downloads":382788,"position":7084,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.3.12"}