Implemented OrcaVault psa schema #17
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* Persistent Staging Area (psa) is a layer in data warehouse where data is kept archived and track change history. The data table in this area also act as an intermediate storage location to provide downstream transformation. * Made use of spreadsheet_library_tracking_metadata table as another data source to feed into vault layer for change history and data consolidation use. * Typically, psa data tables is processed by dbt with append only incremental materialization and the data is sourced from tsa schema table counterpart. * Added `next.sh` script and `tsa.truncate_tables()` db function to simulate incremental data loading between tsa to psa transformation in local dev setup.
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Related #15 |
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* Story: Let Glue the Google LIMS! (continue) As discussed in #20, we now source `tsa.spreadsheet_google_lims` staging data table with dbt and feed into the downstream warehouse psa schema. * Technical steps are now mainly inherited by the framework implemented in PR #17. * With psa, Google LIMS is incrementally loaded with differential data records per daily scheduled run with dbt ELT job. * Chiefly note; since Google LIMS preserved "timestamp" date column, we made use of it as (replay) historical time for the row record. Warehouse load datetime is derived from this timestamp column as initial cutover data extraction date.
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kept archived and track change history. The data table in this area also
act as an intermediate storage location to provide downstream transformation.
source to feed into vault layer for change history and data consolidation use.
materialization and the data is sourced from tsa schema table counterpart.
next.shscript andtsa.truncate_tables()db function to simulateincremental data loading between tsa to psa transformation in local dev setup.