Implemented OrcaVault tsa schema spreadsheet Google LIMS #20
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* Story: Let Glue the Google LIMS! (continue) Now that we have `tsa.spreadsheet_google_lims` in staging data area by Glue ETL job in #19, we can source this data table with dbt to further feed into the downstream warehouse layers in psa and vault schema. * Technical steps are now mainly inherited by the framework implemented in PR #15. Hence, this step becomes pretty straight forward task and template code.
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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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Now that we have
tsa.spreadsheet_google_limsin staging data areaby Glue ETL job in Implemented Glue ETL spreadsheet Google LIMS processing #19, we can source this data table with dbt to further feed into
the downstream warehouse layers in psa and vault schema.
Hence, this step becomes pretty straight forward task and template code.