ML Models for Cortex
This table describe the required variablas and their uses
Name | Description | Mandatory | Default Value |
---|---|---|---|
project_id_src |
Source Google Cloud Project: Project where the source data is located which the data models will consume. |
Y | N/A |
project_id_tgt |
Target Google Cloud Project: Project where Data Foundation for SAP predefined data models will be deployed and accessed by end-users. This may or may not be different from the source project. |
Y | N/A |
dataset_raw_landing |
Source BigQuery Dataset: BigQuery dataset where the source SAP data is replicated to or where the test data will be created. |
Y | N/A |
dataset_cdc_processed |
CDC BigQuery Dataset: BigQuery dataset where the CDC processed data lands the latest available records. This may or may not be the same as the source dataset. |
Y | N/A |
dataset_reporting_tgt |
Target BigQuery reporting dataset: BigQuery dataset where the Data Foundation for SAP predefined data models will be deployed. |
N | SAP_REPORTING |
dataset_models_tgt |
Target BigQuery reporting dataset: BigQuery dataset where the Data Foundation for SAP predefined data models will be deployed. |
N | SAP_ML_MODELS |
mandt |
SAP Mandant. Must be 3 character. | Y | 800 |
sql_flavour |
Which database target type. Valid values are ECC or S4 |
N | ECC |
If you want to test the output of the jinja template locally you can use jinja-cli
for a quick check:
- First install jinja-cli:
pip install jinja-cli
- Then create a json file with the required input data:
cat <<EOF > data.json
"project_id_src": "your-source-project",
"project_id_tgt": "your-target-project",
"dataset_raw_landing": "your-raw-dataset",
"dataset_cdc_processed": "your-cdc-processed-dataset",
"dataset_reporting_tgt": "your-reporting-target-dataset-OR-SAP_REPORTING",
"dataset_models_tgt": "your-mlmodels-target-dataset-OR-ML_MODELS",
"mandt": "your-mandt-number-800",
"sql_flavour": "ECC"
}
EOF
Here is what an example looks like
{
"project_id_src": "kittycorn-dev",
"project_id_tgt": "kittycorn-dev",
"dataset_raw_landing": "ECC_REPL",
"dataset_cdc_processed": "CDC_PROCESSED",
"dataset_reporting_tgt": "SAP_REPORTING",
"dataset_models_tgt": "ML_MODELS",
"mandt": "800",
"sql_flavour": "ECC"
}
- Create an output folder
mkdir output
- Now generate the parsed file:
jinja -d data.json -o ouput/filename.sql filename.sql
Alternatively, if you want to generate all files:
for f in *.sql; do
echo "processing $f ..."
jinja -d data.json -o "output/${f}" "${f}"
done
All submitions are welcome. Please read our code of conduct and contributions guidelines.