Maximizing Efficiency and Productivity with Great Expectations on Databricks ??

Data quality is crucial for organizations relying on data-driven insights to make informed decisions. Accurate, consistent, and reliable data is vital to the success of analytics and machine learning projects. Great Expectations, an open-source Python library, offers a robust framework for validating and documenting data quality expectations. When combined with the capabilities of Databricks, a cloud-based big data processing and analytics platform, organizations can unlock significant benefits in terms of efficiency and productivity. In this article, we will explore how to leverage Great Expectations on Databricks, providing concrete examples to establish robust data quality practices.

Setting Up Great Expectations on Databricks:

To begin, you need to set up Great Expectations and Databricks. Follow these steps:

Step 1: Install Great Expectations using pip:

pip install great_expectations         

Step 2: Create a new notebook in Databricks. ??

Step 3: Import the necessary libraries:

from great_expectations.dataset import PandasDataset         

Generating Mock Data:

Before we dive into the details, let's generate some mock data to work with. You can use online services like Mockaroo (https://www.mockaroo.com/) to generate realistic-looking mock data. For the purpose of this article, we will be using a mock dataset called "MOCK_DATA.csv". This csv has the following columns ['age', 'first_name', 'last_name', 'company_name', 'country', 'income', 'job_title']

Defining Expectations on Databricks:

Once you have Great Expectations installed and Databricks set up, you can define your data quality expectations. Let's consider an example where you have a Spark DataFrame containing customer information:

Step 1: Convert the Spark DataFrame to a Pandas DataFrame:

df = spark.read.format("csv").option("header", "true").load("/FileStore/tables/MOCK_DATA.csv")
pandas_df = df.toPandas()         

Step 2: Convert the 'age' and 'income' columns to numeric types:

pandas_df['age'] = pd.to_numeric(pandas_df['age'], errors='coerce') 
pandas_df['income'] = pd.to_numeric(pandas_df['income'], errors='coerce')         

Step 3: Create a Great Expectations dataset:

dataset = PandasDataset(pandas_df)         

Step 4: Define expectations using Great Expectations:

dataset.expect_column_values_to_be_between('age', min_value=35) # Expect age to be greater than 0 
dataset.expect_column_values_to_be_between('income', min_value=0) # Expect income to be greater than or equal to 0 
dataset.expect_column_values_to_not_be_null('first_name') # Expect first_name to not be null 
dataset.expect_column_values_to_not_be_null('last_name') # Expect last_name to not be null         

Validating Data Quality Expectations:

Once you have defined your expectations, it's time to validate them against your data:

Step 1: Validate the dataset:

results = dataset.validate()         

Step 2: Review the validation results:

results.head()         

Generating an HTML Report:

To generate a comprehensive HTML report of the validation results, follow these steps:

Step 1: Import the necessary libraries:

from great_expectations.render.renderer import ValidationResultsPageRenderer 
from great_expectations.render.view import DefaultJinjaPageView         

Step 2: Generate the HTML report:

renderer = ValidationResultsPageRenderer()
document = renderer.render(results)
view = DefaultJinjaPageView()
html = view.render(document)         

Step 3: Write the HTML report to a file:

with open('validation_report.html', 'w') as f: 
    f.write(html)         

Step 4: Display the HTML report in Databricks:

displayHTML(html)         

Automating Data Quality Checks:

To ensure ongoing data quality, you can automate the validation process using Databricks Jobs and Great Expectations:

Step 1: Create a new Databricks Job. ??

Step 2: In the Job, execute the validation code at regular intervals.

Step 3: Set up slack (full example) or email notifications or alerts for failed expectations. ????

# Validate the datase
results = dataset.validate()


# Check if any expectations have failed
if not results["success"]:
? ? # Prepare the email content
? ? recipient_email = '[email protected]'
? ? subject = 'Data Quality Alert: Failed Expectations'
? ? content = 'The validation of your dataset has failed. Please review the attached validation report for details.'


? ? # Send the email notification
? ? send_email_notification(recipient_email, subject, content)
        

By incorporating Great Expectations into your Databricks workflows, you can establish robust data quality practices, automate validation processes, and ensure ongoing data accuracy and consistency. Leveraging the power of Great Expectations within Databricks empowers organizations to make more confident and reliable data-driven decisions.

Remember, data quality is a continuous effort. Regularly review and update your expectations as your data evolves. With Great Expectations on Databricks, you can effectively manage data quality at scale, enabling more accurate and meaningful insights for your business.