[Fabric E2E Sample] Added updates on notebook and pipeline (#959)

* Updated notebooks, pipelines, and standardized folder structure; improved logging and linting.  

* Enhanced dim table insertion, streamlined tests, and optimized DDO module compatibility.

---------

Co-authored-by: Sean Ma <34230764+maye-msft@users.noreply.github.com>
Co-authored-by: yuna-s <38641545+yuna-s@users.noreply.github.com>
Co-authored-by: Anuj Parashar <promisinganuj@gmail.com>
Co-authored-by: yunasugimoto <yunasugimoto@microsoft.com>

e2e_samples/fabric_dataops_sample/config/application.cfg.template

@@ -25,6 +25,7 @@ workspace_name = <workspace-name>
 workspace_id = <workspace-id>
 lakehouse_name = <lakehouse-name>
 lakehouse_id = <lakehouse-id>
+landing_directory = ${setup:adls_shortcut_name}/data/lnd/
 
 [keyvault]
 name = <keyvault-name>

e2e_samples/fabric_dataops_sample/config/fabric_environment/ddo_transform_standardize.py

@@ -0,0 +1,95 @@
+# -*- coding: utf-8 -*-
+
+"""Main module."""
+
+
+from typing import Tuple
+
+from pyspark.sql import DataFrame
+from pyspark.sql.functions import col, lit, to_timestamp
+from pyspark.sql.types import ArrayType, DoubleType, StringType, StructField, StructType, TimestampType  # noqa: E501
+
+
+def get_schema(schema_name: StringType) -> StructType:
+    if schema_name == "in_parkingbay_schema":
+        schema = StructType(
+            [
+                StructField(
+                    "the_geom",
+                    StructType(
+                        [
+                            StructField("coordinates", ArrayType(ArrayType(ArrayType(ArrayType(DoubleType()))))),
+                            StructField("type", StringType()),
+                        ]
+                    ),
+                ),
+                StructField("marker_id", StringType()),
+                StructField("meter_id", StringType()),
+                StructField("bay_id", StringType(), False),
+                StructField("last_edit", StringType()),
+                StructField("rd_seg_id", StringType()),
+                StructField("rd_seg_dsc", StringType()),
+            ]
+        )
+    elif schema_name == "in_sensordata_schema":
+        schema = StructType(
+            [
+                StructField("bay_id", StringType(), False),
+                StructField("st_marker_id", StringType()),
+                StructField("status", StringType()),
+                StructField(
+                    "location",
+                    StructType(
+                        [StructField("coordinates", ArrayType(DoubleType())), StructField("type", StringType())]
+                    ),
+                ),
+                StructField("lat", StringType()),
+                StructField("lon", StringType()),
+            ]
+        )
+    return schema
+
+
+def standardize_parking_bay(
+    parkingbay_sdf: DataFrame, load_id: StringType, loaded_on: TimestampType
+) -> Tuple[DataFrame, DataFrame]:
+    t_parkingbay_sdf = (
+        parkingbay_sdf.drop_duplicates(["bay_id"])
+        .withColumn("last_edit", to_timestamp("last_edit", "yyyyMMddHHmmss"))
+        .select(
+            col("bay_id").cast("int").alias("bay_id"),
+            "last_edit",
+            "marker_id",
+            "meter_id",
+            "rd_seg_dsc",
+            col("rd_seg_id").cast("int").alias("rd_seg_id"),
+            "the_geom",
+            lit(load_id).alias("load_id"),
+            lit(loaded_on.isoformat()).cast("timestamp").alias("loaded_on"),
+        )
+    ).cache()
+    # Data Validation
+    good_records = t_parkingbay_sdf.filter(col("bay_id").isNotNull())
+    bad_records = t_parkingbay_sdf.filter(col("bay_id").isNull())
+    return good_records, bad_records
+
+
+def standardize_sensordata(
+    sensordata_sdf: DataFrame, load_id: StringType, loaded_on: TimestampType
+) -> Tuple[DataFrame, DataFrame]:
+    t_sensordata_sdf = (
+        sensordata_sdf.select(
+            col("bay_id").cast("int").alias("bay_id"),
+            "st_marker_id",
+            col("lat").cast("float").alias("lat"),
+            col("lon").cast("float").alias("lon"),
+            "location",
+            "status",
+            lit(load_id).alias("load_id"),
+            lit(loaded_on.isoformat()).cast("timestamp").alias("loaded_on"),
+        )
+    ).cache()
+    # Data Validation
+    good_records = t_sensordata_sdf.filter(col("bay_id").isNotNull())
+    bad_records = t_sensordata_sdf.filter(col("bay_id").isNull())
+    return good_records, bad_records