discovering-datavon astronomer

Query, manage, and troubleshoot Apache Airflow DAGs, runs, tasks, and system configuration. Supports 30+ commands across DAG inspection, run management, task logging, configuration queries, and direct REST API access Manage multiple Airflow instances with persistent configuration; auto-discover local and Astro deployments Trigger DAG runs synchronously (wait for completion) or asynchronously, diagnose failures, clear runs for retry, and access task logs with retry/map-index filtering Output...

Human approval gates, form inputs, and branching in Airflow DAGs using deferrable operators. Four operator types: ApprovalOperator for approve/reject decisions, HITLOperator for multi-option selection with forms, HITLBranchOperator for human-driven task routing, and HITLEntryOperator for form data collection All operators are deferrable, releasing worker slots while awaiting human response via Airflow UI's Required Actions tab or REST API Supports optional features including custom...

Build Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use…

Query your data warehouse to answer business questions with cached patterns and concept mappings. Supports pattern lookup and caching for repeated question types, with outcome recording to improve future queries Includes concept-to-table mapping cache and table schema discovery via INFORMATION_SCHEMA or codebase grep Provides run_sql() and run_sql_pandas() kernel functions returning Polars or Pandas DataFrames for analysis CLI commands for managing concept, pattern, and table caches, plus...

Annotate Airflow tasks with data lineage using inlets and outlets. Supports OpenLineage Dataset objects, Airflow Assets, and Airflow Datasets for defining inputs and outputs across databases, data warehouses, and cloud storage Use as a fallback when operators lack built-in OpenLineage extractors; follows a four-tier precedence system where custom extractors and OpenLineage methods take priority Includes dataset naming helpers for Snowflake, BigQuery, S3, and PostgreSQL to ensure consistent...

Guided workflow for creating Apache Airflow DAGs with validation and testing integration. Structured six-phase approach: discover environment and existing patterns, plan DAG structure, implement following best practices, validate with af CLI commands, test with user consent, and iterate on fixes CLI commands for discovery ( af config connections , af config providers , af dags list ) and validation ( af dags errors , af dags get , af dags explore ) provide immediate feedback on DAG...

Define reusable Airflow task group templates with Pydantic validation and compose DAGs from YAML. Use when creating blueprint templates, composing DAGs from…

Verify data freshness by checking table timestamps and update patterns against a staleness scale. Identifies timestamp columns using common ETL naming patterns ( _loaded_at , _updated_at , created_at , etc.) and queries their maximum values to determine age Classifies data into four freshness statuses: Fresh (< 4 hours), Stale (4–24 hours), Very Stale (> 24 hours), or Unknown (no timestamp found) Provides SQL templates for checking last update time and row count trends over recent days to...