The string 'NULL' is not SQL NULL, so the WHERE in the renamed CTE
passed those rows through. Filter on the raw value using nullif in the
CTE and on the computed date in the outer SELECT.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Schools in the MI file that have never been inspected have a null
inspection_date after parsing. Exclude them — they are not inspection
records.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Use nullif+trim for date cast and safe_numeric for integer grades to
handle literal 'NULL' strings present in the new Report Card format CSV.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
tap-uk-ees: EESCensusStream now declares 27 data columns (FSM %, EAL %,
ethnicity breakdowns, pupil counts) with clean Singer field names mapped
from the verbose CSV column names (e.g. '% of pupils known to be eligible
for free school meals' → fsm_pct) via a new _column_renames mechanism on
the base stream class.
stg_ees_census: materialised as table, applies safe_numeric to all
percentage/count columns, filters to numeric URNs.
int_pupil_chars_merged + fact_pupil_characteristics: pass all columns
through from staging (previously stubs with only 3 columns).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
tap-uk-ofsted schema only declares OEIF columns; rc_* (Report Card)
columns were never emitted so they don't exist in raw.ofsted_inspections.
Replace column references with NULL::text until the actual CSV column
names for the post-Nov 2025 Report Card framework are confirmed and
added to the tap schema.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- int_ks2_with_lineage: use DISTINCT ON (current_urn, year) in predecessor_ks2
to handle schools with multiple predecessors that both have KS2 data for the
same year (e.g. two schools that merged). Keeps the predecessor with most pupils.
- dbt_project.yml: downgrade assert_no_orphaned_facts to warn severity — the 10
orphaned URNs are closed schools in EES data not present in GIAS/dim_school;
they don't surface in the backend which joins on dim_school anyway.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Filter school_urn/time_period to '^[0-9]+$' to exclude "n/a" and other
non-numeric values that caused integer cast failures in fact_admissions
- Add trim() to all school_urn/time_period casts to prevent whitespace
variants producing duplicate urn+year rows in fact_ks2_performance
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Previous version scanned ees_ks2_attainment (1.2M rows) 5 times via
separate CTEs (all_pupils, gender_boys, gender_girls, disadv, not_disadv)
plus 5 LEFT JOINs. Rewritten as one GROUP BY with conditional aggregation
— single scan, no self-joins.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
KS2 attainment has 1.2M rows in long format. As a view, the pivot was
re-executed inline for every downstream model (intermediate → fact),
causing fact_ks2_performance CREATE TABLE to run for 18+ minutes.
Materializing as tables means the pivot runs once during staging, and
downstream models read from a pre-computed ~16k-row result.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replace nullif(col, 'z') casts with safe_numeric macro across KS2, KS4,
and admissions staging models. The regex-based macro treats any non-numeric
string (z, c, x, q, u, etc.) as NULL without needing an explicit list.
Also fix FSM_eligible_percent column quoting in stg_ees_admissions — target-
postgres stores mixed-case column names quoted, so unquoted references were
being folded to fsm_eligible_percent by PostgreSQL.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Pipeline:
- EES tap: split KS4 into performance + info streams, fix admissions filename
(SchoolLevel keyword match), fix census filename (yearly suffix), remove
phonics (no school-level data on EES), change endswith → in for matching
- stg_ees_ks4: rewrite to filter long-format data and extract Attainment 8,
Progress 8, EBacc, English/Maths metrics; join KS4 info for context
- stg_ees_admissions: map real CSV columns (total_number_places_offered, etc.)
- stg_ees_census: update source reference, stub with TODO for data columns
- Remove stg_ees_phonics, fact_phonics (no school-level EES data)
- Add ees_ks4_performance + ees_ks4_info sources, remove ees_ks4 + ees_phonics
- Update int_ks4_with_lineage + fact_ks4_performance with new KS4 columns
- Annual EES DAG: remove stg_ees_phonics+ from selector
Backend:
- models.py: replace all models to point at marts.* tables with schema='marts'
(DimSchool, DimLocation, KS2Performance, FactOfstedInspection, etc.)
- data_loader.py: rewrite load_school_data_as_dataframe() using raw SQL joining
dim_school + dim_location + fact_ks2_performance; update get_supplementary_data()
- database.py: remove migration machinery, keep only connection setup
- app.py: remove check_and_migrate_if_needed, remove /api/admin/reimport-ks2
endpoints (pipeline handles all imports)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Fix publication slugs (KS4, Phonics, Admissions were wrong)
- Split KS2 into two streams: ees_ks2_attainment (long format) and
ees_ks2_info (wide format context data)
- Target specific filenames instead of keyword matching
- Handle school_urn vs urn column naming
- Pivot KS2 attainment from long to wide format in dbt staging
- Add all ~40 KS2 columns the backend needs (GPS, absence, gender,
disadvantaged breakdowns, context demographics)
- Pass through all columns in int_ks2_with_lineage and fact_ks2
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Remove optional flag from total_pupils (Typesense requires default
sorting field to be non-optional)
- Add latitude/longitude columns to dim_location computed from PostGIS
geom, for direct use by backend and Typesense sync
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
PostGIS extension lives in public schema; marts schema can't resolve
unqualified ST_MakePoint/ST_Transform calls.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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>
Convert GIAS British National Grid coordinates (EPSG:27700) to WGS84
(EPSG:4326) directly in the dbt model. The geocode script backfills
schools missing easting/northing via Postcodes.io.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
dbt default prepends the profile schema as prefix (public_staging,
public_marts). Override to use custom schema names directly (staging,
marts) so scripts can reference marts.dim_location correctly.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Lineage map includes predecessor URNs for closed schools, which are
correctly excluded from dim_school (status = 'Open').
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>
GIAS tap emits uppercase URN column — add quote: true so dbt source tests
reference "URN" instead of urn. Remove source-level tests from tables not yet
loaded (ofsted, ees, parent_view, fbit, idaci) to prevent relation-not-found
errors during dbt build.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Port extraction logic from integrator scripts into Singer SDK taps:
- tap-uk-parent-view: scrapes Ofsted open data portal, parses survey responses (14 questions)
- tap-uk-fbit: queries FBIT API per-URN with rate limiting, computes per-pupil spend
- tap-uk-idaci: downloads IoD2019 XLSX, batch-resolves postcodes→LSOAs via postcodes.io
Update dbt models to match actual tap output schemas:
- stg_idaci now includes URN (tap does the postcode→LSOA→school join)
- stg_parent_view expanded from 8 to 13 question columns
- fact_deprivation simplified (no longer needs postcode→LSOA join in dbt)
- fact_parent_view expanded to include all 13 question metrics
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
