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Add data quality part for parking_sensor_synapse using great expectations library #641

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Add data quality part for parking_sensor_synapse using great expectations library #641

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ff59e82
Adding credential: WorkspaceSystemIdentity
cchenshu Jun 20, 2023
e5232b1
Create publish_config.json
cchenshu Jun 20, 2023
ce4c456
Update publish_config.json
cchenshu Jun 20, 2023
52cd372
Updating notebook: 03_transform
cchenshu Jun 20, 2023
13b93c6
feat: add data validation part to notebooks
cchenshu Jun 20, 2023
c265182
Merge branch 'feature/dev' of https://github.com/cchenshu/modern-data…
cchenshu Jun 20, 2023
2c3a6db
remove unrelated files
cchenshu Jun 20, 2023
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remove unrelated files
cchenshu Jun 20, 2023
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3 changes: 2 additions & 1 deletion e2e_samples/parking_sensors_synapse/synapse/config/requirements.txt
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opencensus-ext-azure==1.1.7
opencensus-ext-azure==1.1.7
great_expectations
124 changes: 122 additions & 2 deletions e2e_samples/parking_sensors_synapse/synapse/notebook/02_standardize.ipynb
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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"nteract": {
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"nteract": {
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"nteract": {
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4. Data Quality\n",
"The following uses the [Great Expectations](https://greatexpectations.io/) library. See [Great Expectation Docs](https://docs.greatexpectations.io/docs/) for more info.\n",
"\n",
"**Note**: for simplication purposes, the [Expectation Suite](https://docs.greatexpectations.io/docs/terms/expectation_suite) is created inline. Generally this should be created prior to data pipeline execution, and only loaded during runtime and executed against a data [Batch](https://docs.greatexpectations.io/docs/terms/batch/) via [Checkpoint](https://docs.greatexpectations.io/docs/terms/checkpoint/)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"from ruamel import yaml\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 pyspark.sql import SparkSession, Row\n",
"\n",
"# 0. Create mount point path for spark job\n",
"job_id = mssparkutils.env.getJobId()\n",
"root_directory=f\"/synfs/{job_id}/great_expectations\"\n",
"\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\"},\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",
"\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=\"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",
"\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.add_or_update_expectation_suite(expectation_suite_name=expectation_suite_name)\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",
"\n",
"\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",
"my_checkpoint = context.test_yaml_config(yaml.dump(checkpoint_config))\n",
"context.add_or_update_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"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"nteract": {
Expand All @@ -174,7 +293,7 @@
}
},
"source": [
"# 4. Observability: Logging to Azure Application Insights using OpenCensus Library"
"# 5. Observability: Logging to Azure Application Insights using OpenCensus Library"
]
},
{
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"nteract": {
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}
},
"source": [
"# 5. Observability: Logging to Log Analytics workspace using log4j"
"# 6. Observability: Logging to Log Analytics workspace using log4j"
]
},
{
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124 changes: 122 additions & 2 deletions e2e_samples/parking_sensors_synapse/synapse/notebook/03_transform.ipynb
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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"nteract": {
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"nteract": {
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"nteract": {
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 4. Data Quality\n",
"The following uses the [Great Expectations](https://greatexpectations.io/) library. See [Great Expectation Docs](https://docs.greatexpectations.io/docs/) for more info.\n",
"\n",
"**Note**: for simplication purposes, the [Expectation Suite](https://docs.greatexpectations.io/docs/terms/expectation_suite) is created inline. Generally this should be created prior to data pipeline execution, and only loaded during runtime and executed against a data [Batch](https://docs.greatexpectations.io/docs/terms/batch/) via [Checkpoint](https://docs.greatexpectations.io/docs/terms/checkpoint/)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"# import os\n",
"from ruamel import yaml\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 pyspark.sql import SparkSession, Row\n",
"\n",
"# 0. Create mount point path for spark job\n",
"job_id = mssparkutils.env.getJobId()\n",
"root_directory=f\"/synfs/{job_id}/great_expectations\"\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\"},\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 new_fact_parking 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\": new_fact_parking}, # Your dataframe goes here\n",
")\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.add_or_update_expectation_suite(expectation_suite_name=expectation_suite_name)\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",
"\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",
"my_checkpoint = context.test_yaml_config(yaml.dump(checkpoint_config,default_flow_style=False))\n",
"context.add_or_update_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",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"nteract": {
Expand All @@ -158,7 +277,7 @@
}
},
"source": [
"# 4. Observability: Logging to Azure Application Insights using OpenCensus Library"
"# 5. Observability: Logging to Azure Application Insights using OpenCensus Library"
]
},
{
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {
"nteract": {
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}
},
"source": [
"# 5. Observability: Logging to Log Analytics workspace using log4j"
"# 6. Observability: Logging to Log Analytics workspace using log4j"
]
},
{
Expand Down