Feature remove mandatory args

CONTRIBUTORS.md

@@ -6,6 +6,8 @@
 Thanks to the contributors who helped on this project apart from the authors
 * [Teja Dogiparthi](https://github.com/Tejadogiparthi)
 * [Phani Kumar Vemuri](https://www.linkedin.com/in/vemuriphani/)
+* [Sarath Chandra Bandaru](https://www.linkedin.com/in/sarath-chandra-bandaru/)
+* [Holden Karau](https://www.linkedin.com/in/holdenkarau/)
 
 # Honorary Mentions
 Thanks to the team below for invaluable insights and support throughout the initial release of this project
@@ -15,4 +17,3 @@ Thanks to the team below for invaluable insights and support throughout the init
 * [Aditya Chaturvedi](https://www.linkedin.com/in/chaturvediaditya/)
 * [Scott Haines](https://www.linkedin.com/in/scotthaines/)
 * [Arijit Banerjee](https://www.linkedin.com/in/massborn/)
-* [Sarath Chandra Bandaru](https://www.linkedin.com/in/sarath-chandra-bandaru/)

README.md

@@ -7,7 +7,8 @@
 [![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)
 [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 ![PYPI version](https://img.shields.io/pypi/v/spark-expectations.svg)
-
+![PYPI - Downloads](https://static.pepy.tech/badge/spark-expectations)
+![PYPI - Python Version](https://img.shields.io/pypi/pyversions/spark-expectations.svg)
 
 <p align="center">
 Spark Expectations is a specialized tool designed with the primary goal of maintaining data integrity within your processing pipeline.
@@ -70,7 +71,7 @@ is provided in the appropriate fields.
 ```python
 from spark_expectations.config.user_config import *
 
-se_global_spark_Conf = {
+se_user_conf = {
     se_notifications_enable_email: False,
     se_notifications_email_smtp_host: "mailhost.nike.com",
     se_notifications_email_smtp_port: 25,
@@ -91,66 +92,49 @@ se_global_spark_Conf = {
 
 For all the below examples the below import and SparkExpectations class instantiation is mandatory
 
-```python
-from spark_expectations.core.expectations import SparkExpectations
+1. Instantiate `SparkExpectations` class which has all the required functions for running data quality rules
 
+```python
+from spark_expectations.core.expectations import SparkExpectations, WrappedDataFrameWriter
+from pyspark.sql import SparkSession
 
+spark: SparkSession = SparkSession.builder.getOrCreate()
+writer = WrappedDataFrameWriter().mode("append").format("delta")
+# writer = WrappedDataFrameWriter().mode("append").format("iceberg")
 # product_id should match with the "product_id" in the rules table
-se: SparkExpectations = SparkExpectations(product_id="your-products-id")
+se: SparkExpectations = SparkExpectations(
+    product_id="your_product",
+    rules_df=spark.table("dq_spark_local.dq_rules"),
+    stats_table="dq_spark_local.dq_stats",
+    stats_table_writer=writer,
+    target_and_error_table_writer=writer,
+    debugger=False,
+    # stats_streaming_options={user_config.se_enable_streaming: False},
+)
 ```
 
-1. Instantiate `SparkExpectations` class which has all the required functions for running data quality rules
-
+2. Decorate the function with `@se.with_expectations` decorator
 
 ```python
-from spark_expectations.config.user_config import * 
-
-
-@se.with_expectations( 
-    se.reader.get_rules_from_table(
-        product_rules_table="pilot_nonpub.dq.dq_rules",
-        table_name="pilot_nonpub.dq_employee.employee",  
-        dq_stats_table_name="pilot_nonpub.dq.dq_stats" 
-    ),
-    write_to_table=True, 
-    write_to_temp_table=True,
-    row_dq=True, 
-    agg_dq={
-        se_agg_dq: True,  
-        se_source_agg_dq: True,  
-        se_final_agg_dq: True, 
-    },
-    query_dq={ 
-        se_query_dq: True,  
-        se_source_query_dq: True, 
-        se_final_query_dq: True, 
-        se_target_table_view: "order", 
-    },
-    spark_conf=se_global_spark_Conf,
+from spark_expectations.config.user_config import *
+from pyspark.sql import DataFrame
+import os
+
 
+@se.with_expectations(
+    target_table="dq_spark_local.customer_order",
+    write_to_table=True,
+    user_conf=se_user_conf,
+    target_table_view="order",
 )
 def build_new() -> DataFrame:
+    # Return the dataframe on which Spark-Expectations needs to be run
     _df_order: DataFrame = (
         spark.read.option("header", "true")
         .option("inferSchema", "true")
         .csv(os.path.join(os.path.dirname(__file__), "resources/order.csv"))
     )
-    _df_order.createOrReplaceTempView("order")  
-
-    _df_product: DataFrame = (
-        spark.read.option("header", "true")
-        .option("inferSchema", "true")
-        .csv(os.path.join(os.path.dirname(__file__), "resources/product.csv"))
-    )
-    _df_product.createOrReplaceTempView("product") 
-
-    _df_customer: DataFrame = (
-        spark.read.option("header", "true")
-        .option("inferSchema", "true")
-        .csv(os.path.join(os.path.dirname(__file__), "resources/customer.csv"))
-    )
-
-    _df_customer.createOrReplaceTempView("customer") 
+    _df_order.createOrReplaceTempView("order")
 
     return _df_order 
 ```

docs/api/sample_dq.md → docs/api/sample_dq_bigquery.md

@@ -1,9 +1,5 @@
----
-search:
-  exclude: true
----
 
-::: spark_expectations.examples.sample_dq
+::: spark_expectations.examples.sample_dq_bigquery
     handler: python
     options:
         filters:

docs/api/sample_dq_delta.md

@@ -0,0 +1,8 @@
+
+::: spark_expectations.examples.sample_dq_delta
+    handler: python
+    options:
+        filters:
+            - "!^_[^_]"
+            - "!^__[^__]"
+

docs/api/sample_dq_iceberg.md

@@ -0,0 +1,8 @@
+
+::: spark_expectations.examples.sample_dq_iceberg
+    handler: python
+    options:
+        filters:
+            - "!^_[^_]"
+            - "!^__[^__]"
+

docs/bigquery.md

@@ -0,0 +1,92 @@
+### Example - Write to Delta
+
+Setup SparkSession for bigquery to test in your local environment. Configure accordingly for higher environments.
+Refer to Examples in [base_setup.py](../spark_expectations/examples/base_setup.py) and
+[delta.py](../spark_expectations/examples/sample_dq_bigquery.py)
+
+```python title="spark_session"
+from pyspark.sql import SparkSession
+
+builder = (
+    SparkSession.builder.config(
+        "spark.jars.packages",
+        "com.google.cloud.spark:spark-bigquery-with-dependencies_2.12:0.30.0",
+    )
+)
+spark = builder.getOrCreate()
+
+spark._jsc.hadoopConfiguration().set(
+    "fs.gs.impl", "com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem"
+)
+spark.conf.set("viewsEnabled", "true")
+spark.conf.set("materializationDataset", "<temp_dataset>")
+```
+
+Below is the configuration that can be used to run SparkExpectations and write to DeltaLake
+
+```python title="iceberg_write"
+import os
+from pyspark.sql import DataFrame
+from spark_expectations.core.expectations import (
+    SparkExpectations,
+    WrappedDataFrameWriter,
+)
+from spark_expectations.config.user_config import Constants as user_config
+
+os.environ[
+    "GOOGLE_APPLICATION_CREDENTIALS"
+] = "path_to_your_json_credential_file"  # This is needed for spark write to bigquery
+writer = (
+    WrappedDataFrameWriter.mode("overwrite")
+    .format("bigquery")
+    .option("createDisposition", "CREATE_IF_NEEDED")
+    .option("writeMethod", "direct")
+)
+
+se: SparkExpectations = SparkExpectations(
+    product_id="your_product",
+    rules_df=spark.read.format("bigquery").load(
+        "<project_id>.<dataset_id>.<rules_table>"
+    ),
+    stats_table="<project_id>.<dataset_id>.<stats_table>",
+    stats_table_writer=writer,
+    target_and_error_table_writer=writer,
+    debugger=False,
+    stats_streaming_options={user_config.se_enable_streaming: False}
+)
+
+
+# Commented fields are optional or required when notifications are enabled
+user_conf = {
+    user_config.se_notifications_enable_email: False,
+    # user_config.se_notifications_email_smtp_host: "mailhost.com",
+    # user_config.se_notifications_email_smtp_port: 25,
+    # user_config.se_notifications_email_from: "",
+    # user_config.se_notifications_email_to_other_mail_id: "",
+    # user_config.se_notifications_email_subject: "spark expectations - data quality - notifications",
+    user_config.se_notifications_enable_slack: False,
+    # user_config.se_notifications_slack_webhook_url: "",
+    # user_config.se_notifications_on_start: True,
+    # user_config.se_notifications_on_completion: True,
+    # user_config.se_notifications_on_fail: True,
+    # user_config.se_notifications_on_error_drop_exceeds_threshold_breach: True,
+    # user_config.se_notifications_on_error_drop_threshold: 15,
+}
+
+
+@se.with_expectations(
+    target_table="<project_id>.<dataset_id>.<target_table_name>",
+    write_to_table=True,
+    user_conf=user_conf,
+    target_table_view="<project_id>.<dataset_id>.<target_table_view_name>",
+)
+def build_new() -> DataFrame:
+    _df_order: DataFrame = (
+        spark.read.option("header", "true")
+        .option("inferSchema", "true")
+        .csv(os.path.join(os.path.dirname(__file__), "resources/order.csv"))
+    )
+    _df_order.createOrReplaceTempView("order")
+
+    return _df_order
+```

docs/configurations/adoption_versions_comparsion.md

@@ -4,16 +4,17 @@ Please find the difference in the changes with different version, latest three v
 
 
 
-| stage | 0.6.0 |  0.7.0 | 0.8.0 |   
-| :------------------| :----------- | :----- | ------------------ |                      
-| rules table schema changes | refer rule table creation [here](https://glowing-umbrella-j8jnolr.pages.github.io/0.7.0/getting-started/setup/) | added three additional column <br> 1.`enable_for_source_dq_validation(boolean)`  <br> 2.`enable_for_target_dq_validation(boolean)` <br> 3.`is_active(boolean)` <br> <br> documentation found [here](https://glowing-umbrella-j8jnolr.pages.github.io/0.7.0/getting-started/setup/) | added additional two column <br> 1.`enable_error_drop_alert(boolean)` <br> 2.`error_drop_thresholdt(int)` <br><br> documentation found [here](https://glowing-umbrella-j8jnolr.pages.github.io/0.8.0/getting-started/setup/)|                                               
-| rule table creation required | yes | yes - creation not required if you're upgrading from old version but schema changes required | yes - creation not required if you're upgrading from old version but schema changes required |       
-| stats table schema changes | refer rule table creation [here](https://glowing-umbrella-j8jnolr.pages.github.io/0.7.0/getting-started/setup/) | added additional columns <br> 1. `source_query_dq_results`  <br> 2. `final_query_dq_results` <br> 3. `row_dq_res_summary` <br> 4. `dq_run_time` <br> 5. `dq_rules` <br><br> renamed columns <br> 1. `runtime` to `meta_dq_run_time`  <br> 2. `run_date` to `meta_dq_run_date` <br> 3. `run_id` to `meta_dq_run_id` <br><br> documentation found [here](https://glowing-umbrella-j8jnolr.pages.github.io/0.7.0/getting-started/setup/)| remains same |                               
-| stats table creation required | yes | yes - creation not required if you're upgrading from old version but schema changes required  | automated |
-| notification config setting | define global notification param, register as env variable and place in the `__init__.py` file for multiple usage, [example](https://glowing-umbrella-j8jnolr.pages.github.io/0.7.0/examples/) | Define a global notification parameter in the `__init__.py` file to be used in multiple instances where the spark_conf parameter needs to be passed within the with_expectations function. [example](https://glowing-umbrella-j8jnolr.pages.github.io/0.7.0/examples/) | remains same |
-| secret store and kafka authentication details | not applicable | not applicable | Create a dictionary that contains your secret configuration values and register in `__init__.py` for multiple usage, [example](https://glowing-umbrella-j8jnolr.pages.github.io/0.8.0/examples/) |
-| spark expectations initialisation | create SparkExpectations class object using the `SparkExpectations` library and by passing the `product_id` | create spark expectations class object using `SpakrExpectations` by passing `product_id` and optional parameter `debugger` [example](https://glowing-umbrella-j8jnolr.pages.github.io/0.7.0/examples/)  | create spark expectations class object using `SpakrExpectations` by passing `product_id` and additional optional parameter `debugger`, `stats_streaming_options`  [example](https://glowing-umbrella-j8jnolr.pages.github.io/0.8.0/examples/) |
-| spark expectations decorator | The decorator allows for configuration by passing individual parameters to each decorator. However, registering a DataFrame view within a decorated function is not supported for implementations of query_dq [example](https://glowing-umbrella-j8jnolr.pages.github.io/0.7.0/examples/) | The decorator allows configurations to be logically grouped through a dictionary passed as a parameter to the decorator. Additionally, registering a DataFrame view within a decorated function is supported for implementations of query_dq. [example](https://glowing-umbrella-j8jnolr.pages.github.io/0.7.0/examples/) | remains same |
+| stage                                         | 0.8.0                                                                                                                                                                                                                                        | 1.0.0                                                                                                                    |   
+|:----------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------|
+| rules table schema changes                    | added additional two column <br> 1.`enable_error_drop_alert(boolean)` <br> 2.`error_drop_thresholdt(int)` <br><br> documentation found [here](https://engineering.nike.com/spark-expectations/0.8.1/getting-started/setup/)                  | Remains same                                                                                                             |                                               
+| rule table creation required                  | yes - creation not required if you're upgrading from old version but schema changes required                                                                                                                                                 | yes - creation not required if you're upgrading from old version but schema changes required                             |       
+| stats table schema changes                    | remains same                                                                                                                                                                                                                                 | Remains Same                                                                                                             |                               
+| stats table creation required                 | automated                                                                                                                                                                                                                                    | Remains Same                                                                                                             |
+| notification config setting                   | remains same                                                                                                                                                                                                                                 | Remains Same                                                                                                             |
+| secret store and kafka authentication details | Create a dictionary that contains your secret configuration values and register in `__init__.py` for multiple usage, [example](https://engineering.nike.com/spark-expectations/0.8.1/examples/)                                              | Remains Same. You can disable streaming if needed, in SparkExpectations class                                            |
+| spark expectations initialisation             | create spark expectations class object using `SpakrExpectations` by passing `product_id` and additional optional parameter `debugger`, `stats_streaming_options`  [example](https://engineering.nike.com/spark-expectations/0.8.1/examples/) | New arguments are added. Please follow this - [example](https://engineering.nike.com/spark-expectations/1.0.0/examples/) |
+| with_expectations decorator                   | remains same                                                                                                                                                                                                                                 | New arguments are added. Please follow this - [example](https://engineering.nike.com/spark-expectations/1.0.0/examples/) |
+| WrappedDataFrameWriter                        | Doesn't exist                                                                                                                                                                                                                                | This is new and users need to provider the writer object to record the spark conf that need to be used while writing - [example](https://engineering.nike.com/spark-expectations/1.0.0/examples/)  |