python demo example for reconciliation added

tests/resources/recon/remorph_recon090.py

@@ -0,0 +1,107 @@
+# This is an example python code which can be used to perform reconciliation using remorph in Databricks notebook.
+# %pip install git+https://github.com/databrickslabs/remorph
+# dbutils.library.restartPython()
+from databricks.sdk import WorkspaceClient
+from pyspark.shell import spark
+
+from databricks.labs.remorph.config import ReconcileConfig, DatabaseConfig, ReconcileMetadataConfig
+from databricks.labs.remorph.config import TableRecon
+from databricks.labs.remorph.reconcile.exception import ReconciliationException
+from databricks.labs.remorph.reconcile.execute import recon
+from databricks.labs.remorph.reconcile.execute import reconcile_aggregates
+from databricks.labs.remorph.reconcile.recon_config import Table, ColumnMapping, Aggregate
+
+# Create a workspace client
+ws = WorkspaceClient(product="remorph", product_version="0.9.0")
+
+source_catalog = ""  # Provide the source catalog name ex: hive_metastore
+source_schema = ""  # Provide the source schema name ex: default
+target_catalog = ""  # Provide the target catalog name ex: users
+target_schema = ""  # Provide the target schema name ex: remorph
+
+# Configure recon source and target
+reconcile_config = ReconcileConfig(
+    data_source="databricks",  # provide the data source name ex: snowflake
+    report_type="row",  # schema,row,data or all https://github.com/databrickslabs/remorph/tree/main/docs/recon_configurations#types-of-report-supported
+    secret_scope="remorph_databricks",
+    database_config=DatabaseConfig(
+        source_catalog=source_catalog,
+        source_schema=source_schema,
+        target_catalog=target_catalog,
+        target_schema=target_schema,
+    ),
+    metadata_config=ReconcileMetadataConfig(catalog=target_catalog, schema=target_schema),
+)
+
+
+# Table recon configuration
+table_recon = TableRecon(
+    source_schema=source_schema,
+    target_catalog=target_catalog,
+    target_schema=target_schema,
+    tables=[
+        Table(
+            source_name="source_employee_table",  # Provide the source table name
+            target_name="target_employee_table",  # Provide the target table name
+            column_mapping=[
+                ColumnMapping(
+                    source_name="emp_id", target_name="employee_id"
+                ),  # Provide the source and target column name if they have different names
+                ColumnMapping(source_name="salary", target_name="sal"),
+            ],
+            join_columns=["emp_id"],  # for recon type all join_columns is mandatory
+        ),
+        Table(source_name="source_dept_table", target_name="target_dept_table", join_columns=["dept_id"]),
+    ],
+)
+
+
+# Performing reconciliation
+try:
+    result = recon(ws, spark, table_recon, reconcile_config)
+    print(f" Success : {result.recon_id}")
+    print("***************************")
+except ReconciliationException as e:
+    print(f"Exception : {str(e)}")
+    print("***************************")
+except Exception as e:
+    print(f"Exception : {str(e)}")
+    print("***************************")
+
+
+# Check target_catalog.target_schema.details table for the reconciliation report
+
+# Table configuration for aggregated reconciliation
+table_recon_agg = TableRecon(
+    source_schema=source_schema,
+    target_catalog=target_catalog,
+    target_schema=target_schema,
+    tables=[
+        Table(
+            source_name="source_employee_table",
+            target_name="target_employee_table",
+            aggregates=[
+                Aggregate(
+                    agg_columns=["emp_id"], type="count"
+                ),  # Provide the column name and aggregation type https://github.com/databrickslabs/remorph/tree/main/docs/recon_configurations#supported-aggregate-functions
+                Aggregate(agg_columns=["salary"], type="min"),
+                Aggregate(agg_columns=["salary"], type="max"),
+            ],
+            join_columns=["emp_id"],  # for recon type all join_columns is mandatory
+        ),
+    ],
+)
+
+try:
+    result = reconcile_aggregates(ws, spark, table_recon_agg, reconcile_config)  # _aggregates
+    recon_id = result.recon_id
+    print(f" Success : {recon_id}")
+    print("***************************")
+except ReconciliationException as e:
+    print(f"Exception : {str(e)}")
+    print("***************************")
+except Exception as e:
+    print(f"Exception : {str(e)}")
+    print("***************************")
+
+# Check target_catalog.target_schema.aggregate_details table for the aggregated reconciliation