GIAS grid references are the actual school location — far more accurate
than postcode centroids. Remove geocode_postcodes.py from the daily DAG
and the postcode-not-null filter from dim_location.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
GIAS CSV dates are DD-MM-YYYY format — use to_date() instead of cast().
Exclude int_ks2_with_lineage+ and int_ks4_with_lineage+ from daily DAG
selector since they depend on EES data not yet loaded.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Declare all 34 columns needed by dbt in GIAS tap schema (target-postgres
only persists columns present in the Singer schema message)
- Use nullif() for empty-string-to-integer/date casts in staging models
- Scope daily DAG dbt build to GIAS models only (stg_gias_establishments+
stg_gias_links+) to avoid errors on unloaded sources
- Scope annual EES DAG similarly; remove redundant dbt test steps
- Make dim_school gracefully handle missing int_ofsted_latest table
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The `catalog` capability forced Meltano to run --discover and generate
a catalog file (tap.properties.json) before each extraction. This fails
because our Singer SDK taps emit schemas inline and don't need external
catalog files. Removing the capability makes Meltano invoke taps
directly without catalog generation.
Also switch from deprecated `meltano elt` to `meltano run` for
Meltano 4.x compatibility.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- MELTANO_BIN/DBT_BIN pointed to .venv/bin/ but Dockerfile installs globally
- Add try/except for BashOperator import to handle both Airflow 3 provider
path and legacy path, preventing silent DAG import failures
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
