From 7fbe40739c10aef2badb55c987bfb8c78a64b5ed Mon Sep 17 00:00:00 2001 From: Christeena Date: Fri, 10 Jan 2025 09:27:58 +0900 Subject: [PATCH] [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 Co-authored-by: yunasugimoto --- .../config/application.cfg.template | 1 + .../ddo_transform_standardize.py | 95 + .../ddo_transform_transform.py | 343 + .../config/fabric_environment/environment.yml | 1 + .../config/lakehouse_ddls.yaml | 4 +- .../scripts/setup_fabric_environment.py | 18 + .../src/notebooks/00_setup.ipynb | 51 +- .../src/notebooks/02_standardize.ipynb | 290 +- .../src/notebooks/03_transform.ipynb | 261 +- .../src/pipelines/00-main.json | 58 +- .../data/MelbParkingBayData.json | 38771 +++++++++++++ .../data/MelbParkingSensorData.json | 19001 ++++++ .../ddo_transform/data/dim_location.json | 27029 +++++++++ .../ddo_transform/data/dim_parking_bay.json | 39457 +++++++++++++ .../ddo_transform/data/dim_st_marker.json | 11013 ++++ .../data/interim_parking_bay.json | 40642 +++++++++++++ .../ddo_transform/data/interim_sensor.json | 48002 ++++++++++++++++ .../tests/ddo_transform/test_standardize.py | 126 + .../src/tests/ddo_transform/test_transform.py | 116 + 19 files changed, 225207 insertions(+), 72 deletions(-) create mode 100644 e2e_samples/fabric_dataops_sample/config/fabric_environment/ddo_transform_standardize.py create mode 100644 e2e_samples/fabric_dataops_sample/config/fabric_environment/ddo_transform_transform.py create mode 100644 e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/data/MelbParkingBayData.json create mode 100644 e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/data/MelbParkingSensorData.json create mode 100644 e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/data/dim_location.json create mode 100644 e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/data/dim_parking_bay.json create mode 100644 e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/data/dim_st_marker.json create mode 100644 e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/data/interim_parking_bay.json create mode 100644 e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/data/interim_sensor.json create mode 100644 e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/test_standardize.py create mode 100644 e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/test_transform.py diff --git a/e2e_samples/fabric_dataops_sample/config/application.cfg.template b/e2e_samples/fabric_dataops_sample/config/application.cfg.template index 1977d2815..4b123faf5 100644 --- a/e2e_samples/fabric_dataops_sample/config/application.cfg.template +++ b/e2e_samples/fabric_dataops_sample/config/application.cfg.template @@ -25,6 +25,7 @@ workspace_name = workspace_id = lakehouse_name = lakehouse_id = +landing_directory = ${setup:adls_shortcut_name}/data/lnd/ [keyvault] name = diff --git a/e2e_samples/fabric_dataops_sample/config/fabric_environment/ddo_transform_standardize.py b/e2e_samples/fabric_dataops_sample/config/fabric_environment/ddo_transform_standardize.py new file mode 100644 index 000000000..944231b1e --- /dev/null +++ b/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 diff --git a/e2e_samples/fabric_dataops_sample/config/fabric_environment/ddo_transform_transform.py b/e2e_samples/fabric_dataops_sample/config/fabric_environment/ddo_transform_transform.py new file mode 100644 index 000000000..ca3186772 --- /dev/null +++ b/e2e_samples/fabric_dataops_sample/config/fabric_environment/ddo_transform_transform.py @@ -0,0 +1,343 @@ +# -*- coding: utf-8 -*- + +"""Main module.""" + + +import uuid + +from pyspark.sql import DataFrame +from pyspark.sql.functions import col, lit, udf, when +from pyspark.sql.types import ( # noqa: E501 + ArrayType, + DoubleType, + FloatType, + IntegerType, + StringType, + StructField, + StructType, + TimestampType, +) + +uuidUdf = udf(lambda: str(uuid.uuid4()), StringType()) +EMPTY_UUID = "00000000-0000-0000-0000-000000000000" + + +def get_schema(schema_name: StringType) -> StructType: + schema = None + if schema_name == "interim_parkingbay_schema": + schema = StructType( + [ + StructField("bay_id", IntegerType(), False), + StructField("last_edit", StringType()), + StructField("marker_id", StringType()), + StructField("meter_id", StringType()), + StructField("rd_seg_id", IntegerType()), + StructField("rd_seg_dsc", StringType()), + StructField( + "the_geom", + StructType( + [ + StructField("coordinates", ArrayType(ArrayType(ArrayType(ArrayType(DoubleType()))))), + StructField("type", StringType()), + ] + ), + ), + StructField("load_id", StringType()), + StructField("loaded_on", TimestampType()), + ] + ) + elif schema_name == "interim_sensor": + schema = StructType( + [ + StructField("bay_id", IntegerType(), False), + StructField("st_marker_id", StringType()), + StructField("lat", FloatType()), + StructField("lon", FloatType()), + StructField( + "location", + StructType( + [StructField("coordinates", ArrayType(DoubleType())), StructField("type", StringType())] + ), + False, + ), + StructField("status", StringType()), + StructField("load_id", StringType()), + StructField("loaded_on", TimestampType()), + ] + ) + elif schema_name == "dw_dim_parking_bay": + schema = StructType( + [ + StructField("dim_parking_bay_id", StringType(), False), + StructField("bay_id", IntegerType(), False), + StructField("marker_id", StringType()), + StructField("meter_id", StringType()), + StructField("rd_seg_id", IntegerType()), + StructField("rd_seg_dsc", StringType()), + StructField( + "the_geom", + StructType( + [ + StructField("coordinates", ArrayType(ArrayType(ArrayType(ArrayType(DoubleType()))))), + StructField("type", StringType()), + ] + ), + ), + StructField("load_id", StringType()), + StructField("loaded_on", TimestampType()), + ] + ) + elif schema_name == "dw_dim_location": + schema = StructType( + [ + StructField("dim_location_id", StringType(), False), + StructField( + "location", + StructType( + [StructField("coordinates", ArrayType(DoubleType())), StructField("type", StringType())] + ), + False, + ), + StructField("lat", FloatType()), + StructField("lon", FloatType()), + StructField("load_id", StringType()), + StructField("loaded_on", TimestampType()), + ] + ) + elif schema_name == "dw_dim_st_marker": + schema = StructType( + [ + StructField("dim_st_marker_id", StringType(), False), + StructField("st_marker_id", StringType()), + StructField("load_id", StringType()), + StructField("loaded_on", TimestampType()), + ] + ) + return schema + + +def process_dim_parking_bay( + parkingbay_sdf: DataFrame, dim_parkingbay_sdf: DataFrame, load_id: StringType, loaded_on: TimestampType +) -> DataFrame: + """Transform incoming parkingbay_sdf data and existing dim_parking_bay + into the latest version of records of dim_parking_bay data. + """ + # Get landing data distinct rows + parkingbay_sdf = parkingbay_sdf.select(["bay_id", "marker_id", "meter_id", "rd_seg_dsc", "rd_seg_id"]).distinct() + + # Using a left_outer join on the business key (bay_id), + # identify rows that do NOT EXIST in landing data that EXISTS in existing Dimension table + oldrows_parkingbay_sdf = ( + dim_parkingbay_sdf.alias("dim") + .join(parkingbay_sdf, "bay_id", "left_outer") + .where(parkingbay_sdf["bay_id"].isNull()) + .select(col("dim.*")) + ) + + # Using a left_outer join on the business key (bay_id), + # Identify rows that EXISTS in incoming landing data that does also EXISTS in existing Dimension table + # and take the values of the incoming landing data. That is, we update existing table values. + existingrows_parkingbay_sdf = ( + parkingbay_sdf.alias("pb") + .join(dim_parkingbay_sdf.alias("dim"), "bay_id", "left_outer") + .where(dim_parkingbay_sdf["bay_id"].isNotNull()) + .select( + col("dim.dim_parking_bay_id"), + col("pb.bay_id"), + col("pb.marker_id"), + col("pb.meter_id"), + col("pb.rd_seg_dsc"), + col("pb.rd_seg_id"), + ) + ) + + # Using a left_outer join on the business key (bay_id), + # Identify rows that EXISTS in landing data that does NOT EXISTS in existing Dimension table + newrows_parkingbay_sdf = ( + parkingbay_sdf.alias("pb") + .join(dim_parkingbay_sdf, "bay_id", "left_outer") + .where(dim_parkingbay_sdf["bay_id"].isNull()) + .select(col("pb.*")) + ) + + # Add load_id, loaded_at and dim_parking_bay_id + existingrows_parkingbay_sdf = existingrows_parkingbay_sdf.withColumn("load_id", lit(load_id)).withColumn( + "loaded_on", lit(loaded_on.isoformat()).cast("timestamp") + ) + newrows_parkingbay_sdf = ( + newrows_parkingbay_sdf.withColumn("load_id", lit(load_id)) + .withColumn("loaded_on", lit(loaded_on.isoformat()).cast("timestamp")) + .withColumn("dim_parking_bay_id", uuidUdf()) + ) + + # Select relevant columns + relevant_cols = [ + "dim_parking_bay_id", + "bay_id", + "marker_id", + "meter_id", + "rd_seg_dsc", + "rd_seg_id", + "load_id", + "loaded_on", + ] + oldrows_parkingbay_sdf = oldrows_parkingbay_sdf.select(relevant_cols) + existingrows_parkingbay_sdf = existingrows_parkingbay_sdf.select(relevant_cols) + newrows_parkingbay_sdf = newrows_parkingbay_sdf.select(relevant_cols) + + allrows_parkingbay_sdf = oldrows_parkingbay_sdf.union(existingrows_parkingbay_sdf).union(newrows_parkingbay_sdf) + + return allrows_parkingbay_sdf + + +def process_dim_location( + sensordata_sdf: DataFrame, dim_location: DataFrame, load_id: StringType, loaded_on: TimestampType +) -> DataFrame: + """Transform sensordata into dim_location""" + + # Get landing data distint rows + sensordata_sdf = sensordata_sdf.select(["lat", "lon"]).distinct() + + # Using a left_outer join + # identify rows that do NOT EXIST in landing data that EXISTS in existing Dimension table + oldrows_sdf = ( + dim_location.alias("dim") + .join(sensordata_sdf, ["lat", "lon"], "left_outer") + .where(sensordata_sdf["lat"].isNull() & sensordata_sdf["lon"].isNull()) + .select(col("dim.*")) + ) + + # Using a left_outer join + # Identify rows that EXISTS in incoming landing data that does also EXISTS in existing Dimension table + # and take the values of the incoming landing data. That is, we update existing table values. + existingrows_sdf = ( + sensordata_sdf.alias("in") + .join(dim_location.alias("dim"), ["lat", "lon"], "left_outer") + .where(dim_location["lat"].isNotNull() & dim_location["lon"].isNotNull()) + .select(col("dim.dim_location_id"), col("in.lat"), col("in.lon")) + ) + + # Using a left_outer join + # Identify rows that EXISTS in landing data that does NOT EXISTS in existing Dimension table + newrows_sdf = ( + sensordata_sdf.alias("in") + .join(dim_location, ["lat", "lon"], "left_outer") + .where(dim_location["lat"].isNull() & dim_location["lon"].isNull()) + .select(col("in.*")) + ) + + # Add load_id, loaded_at and dim_parking_bay_id + existingrows_sdf = existingrows_sdf.withColumn("load_id", lit(load_id)).withColumn( + "loaded_on", lit(loaded_on.isoformat()).cast("timestamp") + ) + newrows_sdf = ( + newrows_sdf.withColumn("load_id", lit(load_id)) + .withColumn("loaded_on", lit(loaded_on.isoformat()).cast("timestamp")) + .withColumn("dim_location_id", uuidUdf()) + ) + + # Select relevant columns + relevant_cols = ["dim_location_id", "lat", "lon", "load_id", "loaded_on"] + oldrows_sdf = oldrows_sdf.select(relevant_cols) + existingrows_sdf = existingrows_sdf.select(relevant_cols) + newrows_sdf = newrows_sdf.select(relevant_cols) + + allrows_sdf = oldrows_sdf.union(existingrows_sdf).union(newrows_sdf) + + return allrows_sdf + + +def process_dim_st_marker( + sensordata_sdf: DataFrame, dim_st_marker: DataFrame, load_id: StringType, loaded_on: TimestampType +) -> DataFrame: + """Transform sensordata into dim_st_marker""" + + # Get landing data distint rows + sensordata_sdf = sensordata_sdf.select(["st_marker_id"]).distinct() + + # Using a left_outer join + # identify rows that do NOT EXIST in landing data that EXISTS in existing Dimension table + oldrows_sdf = ( + dim_st_marker.alias("dim") + .join(sensordata_sdf, ["st_marker_id"], "left_outer") + .where(sensordata_sdf["st_marker_id"].isNull()) + .select(col("dim.*")) + ) + + # Using a left_outer join + # Identify rows that EXISTS in incoming landing data that does also EXISTS in existing Dimension table + # and take the values of the incoming landing data. That is, we update existing table values. + existingrows_sdf = ( + sensordata_sdf.alias("in") + .join(dim_st_marker.alias("dim"), ["st_marker_id"], "left_outer") + .where(dim_st_marker["st_marker_id"].isNotNull()) + .select(col("dim.dim_st_marker_id"), col("in.st_marker_id")) + ) + + # Using a left_outer join + # Identify rows that EXISTS in landing data that does NOT EXISTS in existing Dimension table + newrows_sdf = ( + sensordata_sdf.alias("in") + .join(dim_st_marker, ["st_marker_id"], "left_outer") + .where(dim_st_marker["st_marker_id"].isNull()) + .select(col("in.*")) + ) + + # Add load_id, loaded_at and dim_parking_bay_id + existingrows_sdf = existingrows_sdf.withColumn("load_id", lit(load_id)).withColumn( + "loaded_on", lit(loaded_on.isoformat()).cast("timestamp") + ) + newrows_sdf = ( + newrows_sdf.withColumn("load_id", lit(load_id)) + .withColumn("loaded_on", lit(loaded_on.isoformat()).cast("timestamp")) + .withColumn("dim_st_marker_id", uuidUdf()) + ) + + # Select relevant columns + relevant_cols = ["dim_st_marker_id", "st_marker_id", "load_id", "loaded_on"] + oldrows_sdf = oldrows_sdf.select(relevant_cols) + existingrows_sdf = existingrows_sdf.select(relevant_cols) + newrows_sdf = newrows_sdf.select(relevant_cols) + + allrows_sdf = oldrows_sdf.union(existingrows_sdf).union(newrows_sdf) + + return allrows_sdf + + +def process_fact_parking( + sensordata_sdf: DataFrame, + dim_parkingbay_sdf: DataFrame, + dim_location_sdf: DataFrame, + dim_st_marker_sdf: DataFrame, + load_id: StringType, + loaded_on: TimestampType, +) -> DataFrame: + """Transform sensordata into fact_parking""" + + dim_date_id = loaded_on.strftime("%Y%m%d") + midnight = loaded_on.replace(hour=0, minute=0, second=0, microsecond=0) + dim_time_id = (midnight - loaded_on).seconds + + # Build fact + fact_parking = ( + sensordata_sdf.join(dim_parkingbay_sdf.alias("pb"), "bay_id", "left_outer") + .join(dim_location_sdf.alias("l"), ["lat", "lon"], "left_outer") + .join(dim_st_marker_sdf.alias("st"), "st_marker_id", "left_outer") + .select( + lit(dim_date_id).alias("dim_date_id"), + lit(dim_time_id).alias("dim_time_id"), + when(col("pb.dim_parking_bay_id").isNull(), lit(EMPTY_UUID)) + .otherwise(col("pb.dim_parking_bay_id")) + .alias("dim_parking_bay_id"), + when(col("l.dim_location_id").isNull(), lit(EMPTY_UUID)) + .otherwise(col("l.dim_location_id")) + .alias("dim_location_id"), + when(col("st.dim_st_marker_id").isNull(), lit(EMPTY_UUID)) + .otherwise(col("st.dim_st_marker_id")) + .alias("dim_st_marker_id"), + "status", + lit(load_id).alias("load_id"), + lit(loaded_on.isoformat()).cast("timestamp").alias("loaded_on"), + ) + ) + return fact_parking diff --git a/e2e_samples/fabric_dataops_sample/config/fabric_environment/environment.yml b/e2e_samples/fabric_dataops_sample/config/fabric_environment/environment.yml index 1e38c7522..94fd8b362 100644 --- a/e2e_samples/fabric_dataops_sample/config/fabric_environment/environment.yml +++ b/e2e_samples/fabric_dataops_sample/config/fabric_environment/environment.yml @@ -4,3 +4,4 @@ dependencies: - pytest==8.3.3 - ipytest==0.14.2 - azure-monitor-opentelemetry-exporter==1.0.0b32 + - great-expectations==0.14.12 diff --git a/e2e_samples/fabric_dataops_sample/config/lakehouse_ddls.yaml b/e2e_samples/fabric_dataops_sample/config/lakehouse_ddls.yaml index 9ba55ce57..3907f7706 100644 --- a/e2e_samples/fabric_dataops_sample/config/lakehouse_ddls.yaml +++ b/e2e_samples/fabric_dataops_sample/config/lakehouse_ddls.yaml @@ -101,7 +101,7 @@ table_types: marker_id STRING, meter_id STRING, rd_seg_dsc STRING, - rd_seg_id STRING, + rd_seg_id INT, the_geom STRUCT>>>, type: STRING>, load_id STRING, loaded_on TIMESTAMP @@ -134,7 +134,7 @@ table_types: marker_id STRING, meter_id STRING, rd_seg_dsc STRING, - rd_seg_id STRING, + rd_seg_id INT, the_geom STRUCT>>>, type: STRING>, load_id STRING, loaded_on TIMESTAMP diff --git a/e2e_samples/fabric_dataops_sample/scripts/setup_fabric_environment.py b/e2e_samples/fabric_dataops_sample/scripts/setup_fabric_environment.py index e0ac89753..f43c8676f 100755 --- a/e2e_samples/fabric_dataops_sample/scripts/setup_fabric_environment.py +++ b/e2e_samples/fabric_dataops_sample/scripts/setup_fabric_environment.py @@ -239,6 +239,24 @@ def get_libraries(workspace_id: str, environment_id: str, status: str) -> None: "application/x-python-wheel", ) + # Uploading python library file to staging + upload_staging_libraries( + workspace_id, + environment_id, + "./../config/fabric_environment", + "ddo_transform_standardize.py", + "application/x-python-wheel", + ) + + # Uploading python library file to staging + upload_staging_libraries( + workspace_id, + environment_id, + "./../config/fabric_environment", + "ddo_transform_transform.py", + "application/x-python-wheel", + ) + # Publishing environment (publishing the staged changes) publish_environment(workspace_id, environment_id) diff --git a/e2e_samples/fabric_dataops_sample/src/notebooks/00_setup.ipynb b/e2e_samples/fabric_dataops_sample/src/notebooks/00_setup.ipynb index fd2baabe7..ce43fd513 100644 --- a/e2e_samples/fabric_dataops_sample/src/notebooks/00_setup.ipynb +++ b/e2e_samples/fabric_dataops_sample/src/notebooks/00_setup.ipynb @@ -147,9 +147,11 @@ "# testing using notebooks or functions from this notebook need to be called from another notebook.\n", "import configparser\n", "import json\n", + "import os\n", "from datetime import datetime\n", "\n", "import otel_monitor_invoker as otel # custom module part of env\n", + "import requests # type: ignore\n", "import yaml # type: ignore\n", "from opentelemetry.trace import SpanKind\n", "from opentelemetry.trace.status import StatusCode\n", @@ -162,7 +164,11 @@ "# Helpful to derive any stage based globals\n", "env_stage = \"dev\"\n", "# Common config file path hosted on attached lakehouse - path relative to Files/\n", - "config_file_path = \"sc-adls-main/config/application.cfg\"" + "config_file_path = \"sc-adls-main/config/application.cfg\"\n", + "\n", + "# Parameters from the pipeline\n", + "infilefolder = \"2024_12_17_11_46_14\"\n", + "load_id = \"e8099e5c-16f6-4104-9009-814b75587d99\"" ] }, { @@ -252,6 +258,24 @@ "local_data_mount_path = f'{notebookutils.fs.getMountPath(\"/local_data\")}'" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "892240da", + "metadata": {}, + "outputs": [], + "source": [ + "## Temporary workaround. Remove when issue with OneLake authentication in Notebook is resolved.\n", + "file_path = f\"{local_data_mount_path}/{config_file_path}\"\n", + "os.listdir(f\"{local_data_mount_path}/\")\n", + "os.listdir(f\"{local_data_mount_path}/sc-adls-main\")\n", + "os.listdir(f\"{local_data_mount_path}/sc-adls-main/config\")\n", + "with open(file_path, \"r\") as file:\n", + " file_content = file.read()\n", + "\n", + "print(file_content)" + ] + }, { "cell_type": "markdown", "id": "de3fea57-3e3f-40cc-9fb5-59e595a11957", @@ -323,7 +347,8 @@ "parking_ws = config.get(config_section_name, \"workspace_name\")\n", "parking_ws_id = config.get(config_section_name, \"workspace_id\")\n", "parking_lakehouse = config.get(config_section_name, \"lakehouse_name\")\n", - "ddl_config_file = config.get(config_section_name, \"ddl_file\") # relative path to Files/" + "ddl_config_file = config.get(config_section_name, \"ddl_file\") # relative path to Files/\n", + "landing_directory = config.get(config_section_name, \"landing_directory\")" ] }, { @@ -639,7 +664,21 @@ " raise ValueError(\n", " f\"Encountered errors while creating tables. Errors are\\n{json.dumps(errors, indent=2, default=str)}\"\n", " )\n", - " return None" + " return None\n", + "\n", + "\n", + "def download_data(repo_url: str, destination: str, file_name: str) -> None:\n", + "\n", + " # Create the directory if it doesn't exist\n", + " os.makedirs(destination, exist_ok=True)\n", + "\n", + " response = requests.get(f\"{repo_url}/{file_name}\")\n", + " if response.status_code == 200:\n", + " with open(f\"{destination}/{file_name}\", \"wb\") as file:\n", + " file.write(response.content)\n", + " logger.info(f\"\\t\\t- Downloaded file {repo_url}/{file_name} to {destination}\")\n", + " else:\n", + " logger.exception(f\"\\t\\t- Failed to download file {repo_url}/{file_name} to {destination}\")" ] }, { @@ -687,6 +726,12 @@ " root_span.add_event(name=\"040-table-creation\", attributes={})\n", " create_tables(ddl_dict, lh_table_path, lh_file_path)\n", "\n", + " root_span.add_event(name=\"050-download-data\", attributes={})\n", + " repo_url = \"https://raw.githubusercontent.com/Azure-Samples/modern-data-warehouse-dataops/refs/heads/main/e2e_samples/parking_sensors/src/ddo_transform/data\"\n", + " destination = f\"{local_data_mount_path}/{landing_directory}/{infilefolder}\"\n", + " download_data(repo_url, destination, \"MelbParkingBayData.json\")\n", + " download_data(repo_url, destination, \"MelbParkingSensorData.json\")\n", + "\n", " except Exception as e:\n", " error_message = f\"{process_name} process failed with error {e}\"\n", " logger.exception(error_message)\n", diff --git a/e2e_samples/fabric_dataops_sample/src/notebooks/02_standardize.ipynb b/e2e_samples/fabric_dataops_sample/src/notebooks/02_standardize.ipynb index 1711b44db..edbf3c26d 100644 --- a/e2e_samples/fabric_dataops_sample/src/notebooks/02_standardize.ipynb +++ b/e2e_samples/fabric_dataops_sample/src/notebooks/02_standardize.ipynb @@ -18,7 +18,6 @@ "# Standardize data for Parking Sensors\n", "\n", "TO DO: \n", - "- Update the code logic using [02_standardize.py](https://github.com/Azure-Samples/modern-data-warehouse-dataops/blob/feat/e2e-fabric-dataops-sample/e2e_samples/parking_sensors/databricks/notebooks/02_standardize.py) as the reference.\n", "- Need to update the landing page references.\n", "- Add unit test cases in a separate notebook.\n", "\n", @@ -140,11 +139,25 @@ "# this notebook like a module (main execution will be skipped). Useful when performing\n", "# testing using notebooks or funcitons from this notebook need to be called from another notebook.\n", "import configparser\n", + "import logging\n", + "import os\n", "from datetime import datetime\n", + "from typing import Any, Tuple\n", "\n", + "import ddo_transform_standardize as s\n", "import otel_monitor_invoker as otel # custom module part of env\n", + "from great_expectations.core.batch import RuntimeBatchRequest\n", + "from great_expectations.data_context import BaseDataContext\n", + "from great_expectations.data_context.types.base import (\n", + " DataContextConfig,\n", + " DatasourceConfig,\n", + " FilesystemStoreBackendDefaults,\n", + ")\n", "from opentelemetry.trace import SpanKind\n", "from opentelemetry.trace.status import StatusCode\n", + "from pyspark.sql import DataFrame\n", + "from pyspark.sql.types import StructType\n", + "from ruamel import yaml\n", "\n", "# execution_mode = \"module\" will skip the execution of the main fucntion. Use it for module like treatment\n", "# \"all\" perform execution as well.\n", @@ -154,7 +167,11 @@ "# Helpful to derive any stage based globals\n", "env_stage = \"dev\"\n", "# Common config file path hosted on attached lakehouse - path relative to Files/\n", - "config_file_path = \"sc-adls-main/config/application.cfg\"" + "config_file_path = \"sc-adls-main/config/application.cfg\"\n", + "\n", + "# Parameters from the pipeline\n", + "infilefolder = \"2024_12_17_11_46_14\"\n", + "load_id = \"e8099e5c-16f6-4104-9009-814b75587d99\"" ] }, { @@ -265,6 +282,24 @@ "local_data_mount_path = f'{notebookutils.fs.getMountPath(\"/local_data\")}'" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "881f5d6b", + "metadata": {}, + "outputs": [], + "source": [ + "## Temporary workaround. Remove when issue with OneLake authentication in Notebook is resolved.\n", + "file_path = f\"{local_data_mount_path}/{config_file_path}\"\n", + "os.listdir(f\"{local_data_mount_path}/\")\n", + "os.listdir(f\"{local_data_mount_path}/sc-adls-main\")\n", + "os.listdir(f\"{local_data_mount_path}/sc-adls-main/config\")\n", + "with open(file_path, \"r\") as file:\n", + " file_content = file.read()\n", + "\n", + "print(file_content)" + ] + }, { "cell_type": "markdown", "id": "de3fea57-3e3f-40cc-9fb5-59e595a11957", @@ -337,7 +372,8 @@ "parking_ws_id = config.get(config_section_name, \"workspace_id\")\n", "parking_lakehouse = config.get(config_section_name, \"lakehouse_name\")\n", "\n", - "# Add any other parameters that need to be read" + "# Add any other parameters that need to be read\n", + "landing_directory = config.get(config_section_name, \"landing_directory\")" ] }, { @@ -384,7 +420,8 @@ "job_exec_instance = job_exec_instance if job_exec_instance else f\"{process_name}#{current_ts}\"\n", "execution_user_name = runtime_context[\"userName\"]\n", "\n", - "# Add any other parameters needed by the process" + "# Add any other parameters needed by the process\n", + "loaded_on = datetime.now()" ] }, { @@ -594,17 +631,193 @@ " except Exception:\n", " logger.exception(f\"Specified lakehouse - {lakehouse_name} doesn't exist. Aborting..\")\n", " raise\n", - " return details\n", + " return details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6b7cb26a", + "metadata": {}, + "outputs": [], + "source": [ + "def read_dataframe(schema: StructType, file_path: str, bad_records_file_path: str) -> DataFrame:\n", "\n", + " df = (\n", + " spark.read.schema(schema)\n", + " .option(\"badRecordsPath\", bad_records_file_path)\n", + " .option(\"multiLine\", True)\n", + " .json(file_path)\n", + " )\n", "\n", - "# create functions as needed for standardization process\n", - "# (https://github.com/Azure-Samples/modern-data-warehouse-dataops/blob/feat/e2e-fabric-dataops-sample/e2e_samples/parking_sensors/databricks/notebooks/02_standardize.py)\n", - "def dummy_function() -> None:\n", + " return df\n", "\n", - " # your code goes here\n", - " pass\n", "\n", - " return None" + "def standardize_parking_bay(\n", + " file_path: str, bad_records_file_path: str, load_id: str, loaded_on: datetime\n", + ") -> Tuple[DataFrame, DataFrame]:\n", + "\n", + " schema = s.get_schema(\"in_parkingbay_schema\")\n", + "\n", + " df = read_dataframe(schema, file_path, bad_records_file_path)\n", + "\n", + " # Standardize\n", + " sdf, malformed_sdf = s.standardize_parking_bay(df, load_id, loaded_on)\n", + "\n", + " # Insert new rows\n", + " sdf.write.format(\"delta\").mode(\"append\").saveAsTable(\"interim.parking_bay\")\n", + "\n", + " # Insert bad rows\n", + " malformed_sdf.write.format(\"delta\").mode(\"append\").saveAsTable(\"malformed.parking_bay\")\n", + "\n", + " return sdf, malformed_sdf\n", + "\n", + "\n", + "def standardize_sensordata(\n", + " file_path: str, bad_records_file_path: str, load_id: str, loaded_on: datetime\n", + ") -> Tuple[DataFrame, DataFrame]:\n", + " schema = s.get_schema(\"in_sensordata_schema\")\n", + "\n", + " df = read_dataframe(schema, file_path, bad_records_file_path)\n", + "\n", + " # Standardize\n", + " sdf, malformed_sdf = s.standardize_sensordata(df, load_id, loaded_on)\n", + "\n", + " # Insert new rows\n", + " sdf.write.format(\"delta\").mode(\"append\").saveAsTable(\"interim.sensor\")\n", + "\n", + " # Insert bad rows\n", + " malformed_sdf.write.format(\"delta\").mode(\"append\").saveAsTable(\"malformed.sensor\")\n", + "\n", + " return sdf, malformed_sdf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b3457200", + "metadata": {}, + "outputs": [], + "source": [ + "def validate_parking_bay(parkingbay_sdf: DataFrame) -> Any:\n", + "\n", + " root_directory = \"/lakehouse/default/Files/standardize_validation\"\n", + "\n", + " # 1. Configure DataContext\n", + " # https://docs.greatexpectations.io/docs/terms/data_context\n", + " data_context_config = DataContextConfig(\n", + " datasources={\n", + " \"parkingbay_data_source\": DatasourceConfig(\n", + " class_name=\"Datasource\",\n", + " execution_engine={\"class_name\": \"SparkDFExecutionEngine\", \"force_reuse_spark_context\": True},\n", + " data_connectors={\n", + " \"parkingbay_data_connector\": {\n", + " \"module_name\": \"great_expectations.datasource.data_connector\",\n", + " \"class_name\": \"RuntimeDataConnector\",\n", + " \"batch_identifiers\": [\n", + " \"environment\",\n", + " \"pipeline_run_id\",\n", + " ],\n", + " }\n", + " },\n", + " )\n", + " },\n", + " store_backend_defaults=FilesystemStoreBackendDefaults(root_directory=root_directory),\n", + " )\n", + " context = BaseDataContext(project_config=data_context_config)\n", + " # 2. Create a BatchRequest based on parkingbay_sdf dataframe.\n", + " # https://docs.greatexpectations.io/docs/terms/batch\n", + " batch_request = RuntimeBatchRequest(\n", + " datasource_name=\"parkingbay_data_source\",\n", + " data_connector_name=\"parkingbay_data_connector\",\n", + " data_asset_name=\"paringbaydataaset\", # This can be anything that identifies this data_asset for you\n", + " batch_identifiers={\n", + " \"environment\": \"stage\",\n", + " \"pipeline_run_id\": \"pipeline_run_id\",\n", + " },\n", + " runtime_parameters={\"batch_data\": parkingbay_sdf}, # Your dataframe goes here\n", + " )\n", + " # 3. Define Expecation Suite and corresponding Data Expectations\n", + " # https://docs.greatexpectations.io/docs/terms/expectation_suite\n", + " expectation_suite_name = \"parkingbay_data_exception_suite_basic\"\n", + " context.create_expectation_suite(expectation_suite_name=expectation_suite_name, overwrite_existing=True)\n", + " validator = context.get_validator(\n", + " batch_request=batch_request,\n", + " expectation_suite_name=expectation_suite_name,\n", + " )\n", + " # Add Validatons to suite\n", + " # Check available expectations: validator.list_available_expectation_types()\n", + " # https://legacy.docs.greatexpectations.io/en/latest/autoapi/great_expectations/expectations/index.html\n", + " # https://legacy.docs.greatexpectations.io/en/latest/reference/core_concepts/expectations/standard_arguments.html#meta\n", + " validator.expect_column_values_to_not_be_null(column=\"meter_id\")\n", + " validator.expect_column_values_to_not_be_null(column=\"marker_id\")\n", + " validator.expect_column_values_to_be_of_type(column=\"rd_seg_dsc\", type_=\"StringType\")\n", + " validator.expect_column_values_to_be_of_type(column=\"rd_seg_id\", type_=\"IntegerType\")\n", + " # validator.validate() # To run run validations without checkpoint\n", + " validator.save_expectation_suite(discard_failed_expectations=False)\n", + " # 4. Configure a checkpoint and run Expectation suite using checkpoint\n", + " # https://docs.greatexpectations.io/docs/terms/checkpoint\n", + " my_checkpoint_name = \"Parkingbay Data DQ\"\n", + " checkpoint_config = {\n", + " \"name\": my_checkpoint_name,\n", + " \"config_version\": 1.0,\n", + " \"class_name\": \"SimpleCheckpoint\",\n", + " \"run_name_template\": \"%Y%m%d-%H%M%S-my-run-name-template\",\n", + " }\n", + " context.test_yaml_config(yaml.dump(checkpoint_config))\n", + " context.add_checkpoint(**checkpoint_config)\n", + " # Run Checkpoint passing in expectation suite.\n", + " checkpoint_result = context.run_checkpoint(\n", + " checkpoint_name=my_checkpoint_name,\n", + " validations=[\n", + " {\n", + " \"batch_request\": batch_request,\n", + " \"expectation_suite_name\": expectation_suite_name,\n", + " }\n", + " ],\n", + " )\n", + "\n", + " return checkpoint_result" + ] + }, + { + "cell_type": "markdown", + "id": "76a83fa1", + "metadata": {}, + "source": [ + "### Data Quality Metric Reporting\n", + "This parses the results of the checkpoint and sends it to AppInsights / Azure Monitor for reporting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8cf1cc11", + "metadata": {}, + "outputs": [], + "source": [ + "def data_quality_metric_reporting(checkpoint_result: Any, load_id: str) -> None:\n", + " result_dic = checkpoint_result.to_json_dict()\n", + " key_name = [key for key in result_dic[\"run_results\"].keys()][0]\n", + " results = result_dic[\"run_results\"][key_name][\"validation_result\"][\"results\"]\n", + "\n", + " checks = {\"check_name\": checkpoint_result[\"checkpoint_config\"][\"name\"], \"pipelinerunid\": load_id}\n", + " for i in range(len(results)):\n", + " validation_name = (\n", + " results[i][\"expectation_config\"][\"expectation_type\"]\n", + " + \"_on_\"\n", + " + results[i][\"expectation_config\"][\"kwargs\"][\"column\"]\n", + " )\n", + " checks[validation_name] = results[i][\"success\"]\n", + "\n", + " properties = {\"custom_dimensions\": str(checks)}\n", + "\n", + " if checkpoint_result.success is True:\n", + " logger.setLevel(logging.INFO)\n", + " logger.info(\"verifychecks\", extra=properties)\n", + " else:\n", + " logger.setLevel(logging.ERROR)\n", + " logger.error(\"verifychecks\", extra=properties)" ] }, { @@ -628,10 +841,6 @@ }, "outputs": [], "source": [ - "# Template for main function which calls all other functions\n", - "# Note that, there is a root span using OTEL library\n", - "\n", - "\n", "def main() -> None:\n", " root_span_name = f\"root#{process_name}#{current_ts}\"\n", "\n", @@ -647,15 +856,39 @@ " lh_file_path = f'{lh_details[\"properties\"][\"abfsPath\"]}/Files'\n", " print(lh_table_path, lh_file_path)\n", "\n", - " # root_span.add_event(\n", - " # name=\"<>\", attributes={attributes in dictonary format for second event}\n", - " # )\n", - " # code function for second event\n", + " landing_directory_path = f'{lh_details[\"properties\"][\"abfsPath\"]}/Files/{landing_directory}/{infilefolder}'\n", + "\n", + " # Standardize Parking Bay Data\n", + " parking_bay_file_path = f\"{landing_directory_path}/MelbParkingBayData.json\"\n", + " parking_bay_bad_records_file_path = f\"{landing_directory_path}/__corrupt/MelbParkingBayData\"\n", + " root_span.add_event(\n", + " name=\"020-standardize-parking-bay\",\n", + " attributes={\n", + " \"parking_bay_file_path\": parking_bay_file_path,\n", + " \"parking_bay_bad_records_file_path\": parking_bay_bad_records_file_path,\n", + " },\n", + " )\n", + " parking_bay_df, _ = standardize_parking_bay(\n", + " parking_bay_file_path, parking_bay_bad_records_file_path, load_id, loaded_on\n", + " )\n", + "\n", + " # Standardize Sensor Data\n", + " sensor_file_path = f\"{landing_directory_path}/MelbParkingSensorData.json\"\n", + " sensor_bad_records_file_path = f\"{landing_directory_path}/__corrupt/MelbParkingSensorData\"\n", + " root_span.add_event(\n", + " name=\"030-standardize-sensor\",\n", + " attributes={\n", + " \"sensor_file_path\": sensor_file_path,\n", + " \"sensor_bad_records_file_path\": sensor_bad_records_file_path,\n", + " },\n", + " )\n", + " standardize_sensordata(sensor_file_path, sensor_bad_records_file_path, load_id, loaded_on)\n", + "\n", + " # Validate parking Bay Data\n", + " checkpoint_result = validate_parking_bay(parking_bay_df)\n", "\n", - " # root_span.add_event(\n", - " # name=\"<>\", attributes={attributes in dictonary format for nth event}\n", - " # )\n", - " # code for nth event\n", + " # Data Quality Report\n", + " data_quality_metric_reporting(checkpoint_result, load_id)\n", "\n", " except Exception as e:\n", " error_message = f\"{process_name} process failed with error {e}\"\n", @@ -711,17 +944,6 @@ }, "outputs": [], "source": [ - "# Apply logic here incase this notebook need to be used as a library\n", - "\n", - "# Log into AppInsights and verify the following for telemetry events produced by OpenTelemetry.\n", - "# dependencies\n", - "# | where name hasprefix \"root#nb-020\"\n", - "# //\n", - "# exceptions\n", - "# //\n", - "# traces\n", - "\n", - "\n", "if execution_mode == \"all\":\n", " print(f\"{execution_mode = }. Proceeding with the code execution.\")\n", " main()\n", diff --git a/e2e_samples/fabric_dataops_sample/src/notebooks/03_transform.ipynb b/e2e_samples/fabric_dataops_sample/src/notebooks/03_transform.ipynb index e54f35e79..88ad9b969 100644 --- a/e2e_samples/fabric_dataops_sample/src/notebooks/03_transform.ipynb +++ b/e2e_samples/fabric_dataops_sample/src/notebooks/03_transform.ipynb @@ -18,7 +18,6 @@ "# Transform data for Parking Sensors\n", "\n", "TO DO: \n", - "- Update the code logic using [03_transform.py](https://github.com/Azure-Samples/modern-data-warehouse-dataops/blob/feat/e2e-fabric-dataops-sample/e2e_samples/parking_sensors/databricks/notebooks/03_transform.py) as the reference.\n", "- Need to update the landing page references.\n", "- Add unit test cases in a separate notebook.\n", "\n", @@ -141,11 +140,25 @@ "# this notebook like a module (main execution will be skipped). Useful when performing\n", "# testing using notebooks or funcitons from this notebook need to be called from another notebook.\n", "import configparser\n", + "import logging\n", + "import os\n", "from datetime import datetime\n", + "from typing import Any\n", "\n", + "import ddo_transform_transform as t\n", "import otel_monitor_invoker as otel # custom module part of env\n", + "from great_expectations.core.batch import RuntimeBatchRequest\n", + "from great_expectations.data_context import BaseDataContext\n", + "from great_expectations.data_context.types.base import (\n", + " DataContextConfig,\n", + " DatasourceConfig,\n", + " FilesystemStoreBackendDefaults,\n", + ")\n", "from opentelemetry.trace import SpanKind\n", "from opentelemetry.trace.status import StatusCode\n", + "from pyspark.sql import DataFrame\n", + "from pyspark.sql.functions import col, lit\n", + "from ruamel import yaml\n", "\n", "# execution_mode = \"module\" will skip the execution of the main fucntion. Use it for module like treatment\n", "# \"all\" perform execution as well.\n", @@ -155,7 +168,11 @@ "# Helpful to derive any stage based globals\n", "env_stage = \"dev\"\n", "# Common config file path hosted on attached lakehouse - path relative to Files/\n", - "config_file_path = \"sc-adls-main/config/application.cfg\"" + "config_file_path = \"sc-adls-main/config/application.cfg\"\n", + "\n", + "# Parameters from the pipeline\n", + "infilefolder = \"2024_12_17_11_46_14\"\n", + "load_id = \"e8099e5c-16f6-4104-9009-814b75587d99\"" ] }, { @@ -266,6 +283,24 @@ "local_data_mount_path = f'{notebookutils.fs.getMountPath(\"/local_data\")}'" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "f677288d", + "metadata": {}, + "outputs": [], + "source": [ + "## Temporary workaround. Remove when issue with OneLake authentication in Notebook is resolved.\n", + "file_path = f\"{local_data_mount_path}/{config_file_path}\"\n", + "os.listdir(f\"{local_data_mount_path}/\")\n", + "os.listdir(f\"{local_data_mount_path}/sc-adls-main\")\n", + "os.listdir(f\"{local_data_mount_path}/sc-adls-main/config\")\n", + "with open(file_path, \"r\") as file:\n", + " file_content = file.read()\n", + "\n", + "print(file_content)" + ] + }, { "cell_type": "markdown", "id": "de3fea57-3e3f-40cc-9fb5-59e595a11957", @@ -385,7 +420,8 @@ "job_exec_instance = job_exec_instance if job_exec_instance else f\"{process_name}#{current_ts}\"\n", "execution_user_name = runtime_context[\"userName\"]\n", "\n", - "# Add any other parameters needed by the process" + "# Add any other parameters needed by the process\n", + "loaded_on = datetime.now()" ] }, { @@ -595,17 +631,186 @@ " except Exception:\n", " logger.exception(f\"Specified lakehouse - {lakehouse_name} doesn't exist. Aborting..\")\n", " raise\n", - " return details\n", - "\n", - "\n", - "# create functions as needed for standardization process\n", - "# (https://github.com/Azure-Samples/modern-data-warehouse-dataops/blob/feat/e2e-fabric-dataops-sample/e2e_samples/parking_sensors/databricks/notebooks/03_transform.py)\n", - "def dummy_function() -> None:\n", - "\n", - " # your code goes here\n", - " pass\n", + " return details" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "385b2890", + "metadata": {}, + "outputs": [], + "source": [ + "### Transform and load Dimension tables\n", + "def transform_load_dimension(\n", + " parkingbay_sdf: DataFrame, sensordata_sdf: DataFrame, load_id: str, loaded_on: datetime\n", + ") -> None:\n", + " # Read existing Dimensions\n", + " dim_parkingbay_sdf = spark.read.table(\"dw.dim_parking_bay\")\n", + " dim_location_sdf = spark.read.table(\"dw.dim_location\")\n", + " dim_st_marker = spark.read.table(\"dw.dim_st_marker\")\n", + "\n", + " # Transform\n", + " new_dim_parkingbay_sdf = t.process_dim_parking_bay(parkingbay_sdf, dim_parkingbay_sdf, load_id, loaded_on).cache()\n", + " new_dim_location_sdf = t.process_dim_location(sensordata_sdf, dim_location_sdf, load_id, loaded_on).cache()\n", + " new_dim_st_marker_sdf = t.process_dim_st_marker(sensordata_sdf, dim_st_marker, load_id, loaded_on).cache()\n", + "\n", + " # Insert new rows into Dimension tables\n", + " new_dim_parkingbay_sdf.write.insertInto(\"dw.dim_parking_bay\", overwrite=True)\n", + " new_dim_location_sdf.write.insertInto(\"dw.dim_location\", overwrite=True)\n", + " new_dim_st_marker_sdf.write.insertInto(\"dw.dim_st_marker\", overwrite=True)\n", + "\n", + "\n", + "### Transform and load Fact tables\n", + "def transform_load_fact_table(sensordata_sdf: DataFrame, load_id: str, loaded_on: datetime) -> DataFrame:\n", + " # Read existing Dimensions\n", + " dim_parkingbay_sdf = spark.read.table(\"dw.dim_parking_bay\")\n", + " dim_location_sdf = spark.read.table(\"dw.dim_location\")\n", + " dim_st_marker = spark.read.table(\"dw.dim_st_marker\")\n", + "\n", + " # Process\n", + " nr_fact_parking = t.process_fact_parking(\n", + " sensordata_sdf, dim_parkingbay_sdf, dim_location_sdf, dim_st_marker, load_id, loaded_on\n", + " )\n", + "\n", + " # Insert new rows\n", + " nr_fact_parking.write.mode(\"append\").insertInto(\"dw.fact_parking\")\n", + "\n", + " return nr_fact_parking" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "caf058a0", + "metadata": {}, + "outputs": [], + "source": [ + "def validate_fact_parking(fact_parking_df: DataFrame) -> Any:\n", + "\n", + " root_directory = \"/lakehouse/default/Files/transform_validation\"\n", + "\n", + " # 1. Configure DataContext\n", + " # https://docs.greatexpectations.io/docs/terms/data_context\n", + " data_context_config = DataContextConfig(\n", + " datasources={\n", + " \"transformed_data_source\": DatasourceConfig(\n", + " class_name=\"Datasource\",\n", + " execution_engine={\"class_name\": \"SparkDFExecutionEngine\", \"force_reuse_spark_context\": True},\n", + " data_connectors={\n", + " \"transformed_data_connector\": {\n", + " \"module_name\": \"great_expectations.datasource.data_connector\",\n", + " \"class_name\": \"RuntimeDataConnector\",\n", + " \"batch_identifiers\": [\n", + " \"environment\",\n", + " \"pipeline_run_id\",\n", + " ],\n", + " }\n", + " },\n", + " )\n", + " },\n", + " store_backend_defaults=FilesystemStoreBackendDefaults(root_directory=root_directory),\n", + " )\n", + " context = BaseDataContext(project_config=data_context_config)\n", + "\n", + " # 2. Create a BatchRequest based on parkingbay_sdf dataframe.\n", + " # https://docs.greatexpectations.io/docs/terms/batch\n", + " batch_request = RuntimeBatchRequest(\n", + " datasource_name=\"transformed_data_source\",\n", + " data_connector_name=\"transformed_data_connector\",\n", + " data_asset_name=\"paringbaydataaset\", # This can be anything that identifies this data_asset for you\n", + " batch_identifiers={\n", + " \"environment\": \"stage\",\n", + " \"pipeline_run_id\": \"pipeline_run_id\",\n", + " },\n", + " runtime_parameters={\"batch_data\": fact_parking_df}, # Your dataframe goes here\n", + " )\n", + "\n", + " # 3. Define Expecation Suite and corresponding Data Expectations\n", + " # https://docs.greatexpectations.io/docs/terms/expectation_suite\n", + " expectation_suite_name = \"Transfomed_data_exception_suite_basic\"\n", + " context.create_expectation_suite(expectation_suite_name=expectation_suite_name, overwrite_existing=True)\n", + " validator = context.get_validator(\n", + " batch_request=batch_request,\n", + " expectation_suite_name=expectation_suite_name,\n", + " )\n", + " # Add Validatons to suite\n", + " # Check available expectations: validator.list_available_expectation_types()\n", + " # https://legacy.docs.greatexpectations.io/en/latest/autoapi/great_expectations/expectations/index.html\n", + " # https://legacy.docs.greatexpectations.io/en/latest/reference/core_concepts/expectations/standard_arguments.html#meta\n", + " validator.expect_column_values_to_not_be_null(column=\"status\")\n", + " validator.expect_column_values_to_be_of_type(column=\"status\", type_=\"StringType\")\n", + " validator.expect_column_values_to_not_be_null(column=\"dim_time_id\")\n", + " validator.expect_column_values_to_be_of_type(column=\"dim_time_id\", type_=\"IntegerType\")\n", + " validator.expect_column_values_to_not_be_null(column=\"dim_parking_bay_id\")\n", + " validator.expect_column_values_to_be_of_type(column=\"dim_parking_bay_id\", type_=\"StringType\")\n", + " # validator.validate() # To run run validations without checkpoint\n", + " validator.save_expectation_suite(discard_failed_expectations=False)\n", + "\n", + " # 4. Configure a checkpoint and run Expectation suite using checkpoint\n", + " # https://docs.greatexpectations.io/docs/terms/checkpoint\n", + " my_checkpoint_name = \"Transformed Data\"\n", + " checkpoint_config = {\n", + " \"name\": my_checkpoint_name,\n", + " \"config_version\": 1.0,\n", + " \"class_name\": \"SimpleCheckpoint\",\n", + " \"run_name_template\": \"%Y%m%d-%H%M%S-my-run-name-template\",\n", + " }\n", + " context.test_yaml_config(yaml.dump(checkpoint_config, default_flow_style=False))\n", + " context.add_checkpoint(**checkpoint_config)\n", + " # Run Checkpoint passing in expectation suite\n", + " checkpoint_result = context.run_checkpoint(\n", + " checkpoint_name=my_checkpoint_name,\n", + " validations=[\n", + " {\n", + " \"batch_request\": batch_request,\n", + " \"expectation_suite_name\": expectation_suite_name,\n", + " }\n", + " ],\n", + " )\n", "\n", - " return None" + " return checkpoint_result" + ] + }, + { + "cell_type": "markdown", + "id": "93c63bcb", + "metadata": {}, + "source": [ + "### Data Quality Metric Reporting\n", + "This parses the results of the checkpoint and sends it to AppInsights / Azure Monitor for reporting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "01d20302", + "metadata": {}, + "outputs": [], + "source": [ + "def data_quality_metric_reporting(checkpoint_result: Any, load_id: str) -> None:\n", + " ## Report Data Quality Metrics to Azure Monitor using python Azure Monitor open-census exporter\n", + " result_dic = checkpoint_result.to_json_dict()\n", + " key_name = [key for key in result_dic[\"run_results\"].keys()][0]\n", + " results = result_dic[\"run_results\"][key_name][\"validation_result\"][\"results\"]\n", + "\n", + " checks = {\"check_name\": checkpoint_result[\"checkpoint_config\"][\"name\"], \"pipelinerunid\": load_id}\n", + " for i in range(len(results)):\n", + " validation_name = (\n", + " results[i][\"expectation_config\"][\"expectation_type\"]\n", + " + \"_on_\"\n", + " + results[i][\"expectation_config\"][\"kwargs\"][\"column\"]\n", + " )\n", + " checks[validation_name] = results[i][\"success\"]\n", + "\n", + " properties = {\"custom_dimensions\": str(checks)}\n", + "\n", + " if checkpoint_result.success is True:\n", + " logger.setLevel(logging.INFO)\n", + " logger.info(\"verifychecks\", extra=properties)\n", + " else:\n", + " logger.setLevel(logging.ERROR)\n", + " logger.error(\"verifychecks\", extra=properties)" ] }, { @@ -629,10 +834,6 @@ }, "outputs": [], "source": [ - "# Template for main function which calls all other functions\n", - "# Note that, there is a root span using OTEL library\n", - "\n", - "\n", "def main() -> None:\n", " root_span_name = f\"root#{process_name}#{current_ts}\"\n", "\n", @@ -642,21 +843,17 @@ " name=\"010-verify-lakehouse\",\n", " attributes={\"lakehouse_name\": parking_lakehouse},\n", " )\n", - " lh_details = get_lakehouse_details(parking_lakehouse)\n", - " # Always use absolute paths when referring to Onelake locations\n", - " lh_table_path = f'{lh_details[\"properties\"][\"abfsPath\"]}/Tables'\n", - " lh_file_path = f'{lh_details[\"properties\"][\"abfsPath\"]}/Files'\n", - " print(lh_table_path, lh_file_path)\n", - "\n", - " # root_span.add_event(\n", - " # name=\"<>\", attributes={attributes in dictonary format for second event}\n", - " # )\n", - " # code function for second event\n", - "\n", - " # root_span.add_event(\n", - " # name=\"<>\", attributes={attributes in dictonary format for nth event}\n", - " # )\n", - " # code for nth event\n", + " # Read interim cleansed data\n", + " parkingbay_sdf = spark.read.table(\"interim.parking_bay\").filter(col(\"load_id\") == lit(load_id))\n", + " sensordata_sdf = spark.read.table(\"interim.sensor\").filter(col(\"load_id\") == lit(load_id))\n", + "\n", + " transform_load_dimension(parkingbay_sdf, sensordata_sdf, load_id, loaded_on)\n", + " nr_fact_parking = transform_load_fact_table(sensordata_sdf, load_id, loaded_on)\n", + "\n", + " # validate the parking fact dataframe\n", + " checkpoint_result = validate_fact_parking(nr_fact_parking)\n", + " # data quality report\n", + " data_quality_metric_reporting(checkpoint_result, load_id)\n", "\n", " except Exception as e:\n", " error_message = f\"{process_name} process failed with error {e}\"\n", diff --git a/e2e_samples/fabric_dataops_sample/src/pipelines/00-main.json b/e2e_samples/fabric_dataops_sample/src/pipelines/00-main.json index f0deaaea3..ce639878e 100644 --- a/e2e_samples/fabric_dataops_sample/src/pipelines/00-main.json +++ b/e2e_samples/fabric_dataops_sample/src/pipelines/00-main.json @@ -1,6 +1,22 @@ { "properties": { "activities": [ + { + "type": "SetVariable", + "typeProperties": { + "variableName": "infilefolder", + "value": { + "value": "@utcnow('yyyy_MM_dd_hh_mm_ss')", + "type": "Expression" + } + }, + "policy": { + "secureOutput": false, + "secureInput": false + }, + "name": "set_infilefolder", + "dependsOn": [] + }, { "type": "TridentNotebook", "typeProperties": { @@ -50,6 +66,13 @@ "config_file_path": { "value": "sc-adls-main/config/application.cfg", "type": "string" + }, + "infilefolder": { + "value": { + "value": "@variables('infilefolder')", + "type": "Expression" + }, + "type": "string" } }, "sessionTag": { @@ -65,7 +88,14 @@ "secureOutput": false }, "name": "nb-setup", - "dependsOn": [] + "dependsOn": [ + { + "activity": "set_infilefolder", + "dependencyConditions": [ + "Succeeded" + ] + } + ] }, { "type": "TridentNotebook", @@ -116,6 +146,20 @@ "config_file_path": { "value": "sc-adls-main/config/application.cfg", "type": "string" + }, + "infilefolder": { + "value": { + "value": "@variables('infilefolder')", + "type": "Expression" + }, + "type": "string" + }, + "load_id": { + "value": { + "value": "@pipeline().RunId", + "type": "Expression" + }, + "type": "string" } }, "sessionTag": { @@ -189,6 +233,13 @@ "config_file_path": { "value": "sc-adls-main/config/application.cfg", "type": "string" + }, + "load_id": { + "value": { + "value": "@pipeline().RunId", + "type": "Expression" + }, + "type": "string" } }, "sessionTag": { @@ -231,6 +282,11 @@ "type": "string", "defaultValue": "{{ .lakehouse_name }}" } + }, + "variables": { + "infilefolder": { + "type": "String" + } } } } diff --git a/e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/data/MelbParkingBayData.json b/e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/data/MelbParkingBayData.json new file mode 100644 index 000000000..19557dc47 --- /dev/null +++ b/e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/data/MelbParkingBayData.json @@ -0,0 +1,38771 @@ +[ + { + "the_geom": { + "type": "MultiPolygon", + "coordinates": [ + [ + [ + [ + 144.98359695184072, + -37.810750006600763 + ], + [ + 144.98360614176616, + -37.810694624298392 + ], + [ + 144.98363208398439, + -37.810697398979222 + ], + [ + 144.9836228249919, + -37.810752773792863 + ], + [ + 144.98359695184072, + -37.810750006600763 + ] + ] + ] + ] + }, + "bay_id": "3787", + "last_edit": 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"00000000-0000-0000-0000-000000000000", + "loaded_on": "2019-10-09T12:58:49.754+09:00" + }, + { + "bay_id": 2510, + "st_marker_id": "C2954", + "lat": -37.81275, + "lon": 144.9607, + "location": { + "coordinates": [ + 144.96069414799933, + -37.812751159078395 + ], + "type": "Point" + }, + "status": "Unoccupied", + "load_id": "00000000-0000-0000-0000-000000000000", + "loaded_on": "2019-10-09T12:58:49.754+09:00" + }, + { + "bay_id": 5185, + "st_marker_id": "C8022", + "lat": -37.81053, + "lon": 144.95226, + "location": { + "coordinates": [ + 144.95226223951698, + -37.81053163250514 + ], + "type": "Point" + }, + "status": "Present", + "load_id": "00000000-0000-0000-0000-000000000000", + "loaded_on": "2019-10-09T12:58:49.754+09:00" + } +] diff --git a/e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/test_standardize.py b/e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/test_standardize.py new file mode 100644 index 000000000..274280320 --- /dev/null +++ b/e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/test_standardize.py @@ -0,0 +1,126 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +"""Tests for `ddo_transform` package.""" +import datetime +import os +import sys + +import pytest +from pyspark.sql import SparkSession +from pyspark.sql.functions import isnull +from pyspark.sql.types import ( + ArrayType, + DoubleType, + FloatType, + IntegerType, + StringType, + StructField, + StructType, + TimestampType, +) + +sys.path.append(os.path.join(os.path.dirname(__file__), "../../../config/fabric_environment/")) +import ddo_transform_standardize as standardize # isort: skip + +load_id = "00000000-0000-0000-0000-000000000000" +loaded_on = datetime.datetime.now() + + +@pytest.fixture +def spark() -> SparkSession: + """Spark Session fixture""" + spark = SparkSession.builder.master("local[2]").appName("Unit Testing").getOrCreate() + spark.sparkContext.setLogLevel("ERROR") + return spark + + +def expected_parkingbay_schema(bay_id_nullable: bool = False) -> StructType: + """Create the expected schema for the parking bay data""" + expected_schema_parkingbay = StructType( + [ + StructField("bay_id", IntegerType(), bay_id_nullable), + StructField("last_edit", TimestampType()), + StructField("marker_id", StringType()), + StructField("meter_id", StringType()), + StructField("rd_seg_dsc", StringType()), + StructField("rd_seg_id", IntegerType()), + StructField( + "the_geom", + StructType( + [ + StructField("coordinates", ArrayType(ArrayType(ArrayType(ArrayType(DoubleType()))))), + StructField("type", StringType()), + ] + ), + ), + StructField("load_id", StringType(), False), + StructField("loaded_on", TimestampType()), + ] + ) + return expected_schema_parkingbay + + +def expected_sensordata_schema(bay_id_nullable: bool = False) -> StructType: + """Create the expected schema for the sensor data""" + expected_schema_sensordata = StructType( + [ + StructField("bay_id", IntegerType(), bay_id_nullable), + StructField("st_marker_id", StringType()), + StructField("lat", FloatType()), + StructField("lon", FloatType()), + StructField( + "location", + StructType([StructField("coordinates", ArrayType(DoubleType())), StructField("type", StringType())]), + ), + StructField("status", StringType()), + StructField("load_id", StringType(), False), + StructField("loaded_on", TimestampType()), + ] + ) + return expected_schema_sensordata + + +def test_standardize_parking_bay(spark: SparkSession) -> None: + """Test data standardization""" + # Arrange + schema = standardize.get_schema("in_parkingbay_schema") + parkingbay_sdf = spark.read.json("./data/MelbParkingBayData.json", multiLine=True, schema=schema) + + # Act + t_parkingbay_sdf, t_parkingbay_malformed_sdf = standardize.standardize_parking_bay( + parkingbay_sdf, load_id, loaded_on + ) # noqa: E501 + + # Assert + assert t_parkingbay_sdf.count() != 0 + assert t_parkingbay_malformed_sdf.count() == 1 + assert t_parkingbay_sdf.filter(isnull("bay_id")).count() == 0 + + # Ensure that each bay_id occurs only once + assert t_parkingbay_sdf.groupBy("bay_id").count().filter("count > 1").count() == 0 + + # Ensure that the schema is as expected + assert t_parkingbay_sdf.schema.simpleString() == expected_parkingbay_schema().simpleString() + assert t_parkingbay_malformed_sdf.schema.simpleString() == expected_parkingbay_schema(True).simpleString() + + +def test_standardize_sensordata(spark: SparkSession) -> None: + """Test data standardization""" + # Arrange + schema = standardize.get_schema("in_sensordata_schema") + sensordata_sdf = spark.read.json("./data/MelbParkingSensorData.json", multiLine=True, schema=schema) + + # Act + t_sensordata_sdf, t_sensordata_malformed_sdf = standardize.standardize_sensordata( + sensordata_sdf, load_id, loaded_on + ) # noqa: E501 + + # Assert + assert t_sensordata_sdf.count() != 0 + assert t_sensordata_malformed_sdf.count() == 1 + assert t_sensordata_sdf.filter(isnull("bay_id")).count() == 0 + + # Ensure that the schema is as expected + assert t_sensordata_sdf.schema.simpleString() == expected_sensordata_schema().simpleString() + assert t_sensordata_malformed_sdf.schema.simpleString() == expected_sensordata_schema(True).simpleString() diff --git a/e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/test_transform.py b/e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/test_transform.py new file mode 100644 index 000000000..204816f9f --- /dev/null +++ b/e2e_samples/fabric_dataops_sample/src/tests/ddo_transform/test_transform.py @@ -0,0 +1,116 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +"""Tests for `ddo_transform` package.""" +import datetime +import os +import sys + +import pytest +from pyspark.sql import SparkSession +from pyspark.sql.functions import isnull +from pyspark.sql.types import IntegerType, StringType, StructField, StructType, TimestampType + +sys.path.append(os.path.join(os.path.dirname(__file__), "../../../config/fabric_environment/")) +import ddo_transform_transform as transform # isort: skip + +load_id = "00000000-0000-0000-0000-000000000000" +loaded_on = datetime.datetime.now() + + +@pytest.fixture +def spark() -> SparkSession: + """Spark Session fixture""" + spark = SparkSession.builder.master("local[2]").appName("Unit Testing").getOrCreate() + spark.sparkContext.setLogLevel("ERROR") + return spark + + +def expected_process_dim_parking_bay_schema() -> StructType: + """Create the expected schema for the dim parking bay data""" + expected_schema_process_dim_parking_bay = StructType( + [ + StructField("dim_parking_bay_id", StringType(), False), + StructField("bay_id", IntegerType(), False), + StructField("marker_id", StringType()), + StructField("meter_id", StringType()), + StructField("rd_seg_dsc", StringType()), + StructField("rd_seg_id", IntegerType()), + StructField("load_id", StringType(), False), + StructField("loaded_on", TimestampType()), + ] + ) + return expected_schema_process_dim_parking_bay + + +def expected_process_fact_parking_schema() -> StructType: + """Create the expected schema for the fact parking data""" + expected_process_fact_parking = StructType( + [ + StructField("dim_date_id", StringType()), + StructField("dim_time_id", IntegerType()), + StructField("dim_parking_bay_id", StringType(), False), + StructField("dim_location_id", StringType(), False), + StructField("dim_st_marker_id", StringType(), False), + StructField("status", StringType()), + StructField("load_id", StringType(), False), + StructField("loaded_on", TimestampType()), + ] + ) + return expected_process_fact_parking + + +def test_process_dim_parking_bay(spark: SparkSession) -> None: + """Test data transform""" + # Arrange + parkingbay_sdf = spark.read.json( + "./data/interim_parking_bay.json", multiLine=True, schema=transform.get_schema("interim_parkingbay_schema") + ) + dim_parkingbay_sdf = spark.read.json( + "./data/dim_parking_bay.json", multiLine=True, schema=transform.get_schema("dw_dim_parking_bay") + ) + ## Filter out the data from each DataFrame to ensure that the act function is working as expected + parkingbay_sdf = parkingbay_sdf.filter((parkingbay_sdf.bay_id != 3787) & (parkingbay_sdf.bay_id != 4318)) + dim_parkingbay_sdf = dim_parkingbay_sdf.filter(dim_parkingbay_sdf.bay_id != 21016) + + # Act + results_df = transform.process_dim_parking_bay(parkingbay_sdf, dim_parkingbay_sdf, load_id, loaded_on) + + # Assert + assert results_df.count() == 998 + assert results_df.filter(results_df.bay_id == 3787).count() == 1 + assert results_df.filter(results_df.bay_id == 4318).count() == 1 + assert results_df.filter(results_df.bay_id == 21016).count() == 1 + assert results_df.filter(isnull("dim_parking_bay_id")).count() == 0 + + # Ensure that the schema is as expected + assert results_df.schema.simpleString() == expected_process_dim_parking_bay_schema().simpleString() + + +def test_process_fact_parking(spark: SparkSession) -> None: + """Test data transform""" + # Arrange + sensor_sdf = spark.read.json( + "./data/interim_sensor.json", multiLine=True, schema=transform.get_schema("interim_sensor") + ) + dim_parking_bay_sdf = spark.read.json( + "./data/dim_parking_bay.json", multiLine=True, schema=transform.get_schema("dw_dim_parking_bay") + ) + dim_location_sdf = spark.read.json( + "./data/dim_location.json", multiLine=True, schema=transform.get_schema("dw_dim_location") + ) + dim_st_marker_sdf = spark.read.json( + "./data/dim_st_marker.json", multiLine=True, schema=transform.get_schema("dw_dim_st_marker") + ) + + # Act + results_df = transform.process_fact_parking( + sensor_sdf, dim_parking_bay_sdf, dim_location_sdf, dim_st_marker_sdf, load_id, loaded_on + ) + + # Assert + assert results_df.count() != 0 + assert results_df.filter(isnull("dim_parking_bay_id")).count() == 0 + + # Ensure that the schema is as expected + assert results_df.schema.simpleString() == expected_process_fact_parking_schema().simpleString()