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Replaces the hand-rolled integrator with a production-grade ELT pipeline using Meltano (Singer taps), dbt Core (medallion architecture), and Apache Airflow (orchestration). Adds Typesense for search and PostGIS for geospatial queries. - 6 custom Singer taps (GIAS, EES, Ofsted, Parent View, FBIT, IDACI) - dbt project: 12 staging, 5 intermediate, 12 mart models - 3 Airflow DAGs (daily/monthly/annual schedules) - Typesense sync + batch geocoding scripts - docker-compose: add Airflow, Typesense; upgrade to PostGIS - Portainer stack definition matching live deployment topology Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
37 lines
870 B
SQL
37 lines
870 B
SQL
-- Macro: Generate a CTE that unions current and predecessor data for a given source
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{% macro chain_lineage(source_ref, urn_col='urn', year_col='year') %}
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with current_data as (
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select
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{{ urn_col }} as current_urn,
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{{ urn_col }} as source_urn,
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*
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from {{ source_ref }}
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),
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predecessor_data as (
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select
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lin.current_urn,
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src.{{ urn_col }} as source_urn,
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src.*
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from {{ source_ref }} src
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inner join {{ ref('int_school_lineage') }} lin
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on src.{{ urn_col }} = lin.predecessor_urn
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where not exists (
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select 1 from {{ source_ref }} curr
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where curr.{{ urn_col }} = lin.current_urn
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and curr.{{ year_col }} = src.{{ year_col }}
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)
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),
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combined as (
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select * from current_data
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union all
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select * from predecessor_data
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)
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select * from combined
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{% endmacro %}
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