Great Expectations on AWS

1. Introduction

Great Expectations is an open-source library that helps you define, document, and validate your data expectations. On AWS, it can be seamlessly integrated into your data workflow to ensure data quality and observability.

2. Key Concepts

• Data Expectations: Specifications about what your data should look like.
• Data Docs: Automatically generated documentation that provides insights into your data and the expectations defined.
• Validation: The process of checking if the data meets the defined expectations.
• Profiling: Understanding the data's properties and generating insights before validation.

3. Setup

Follow these steps to set up Great Expectations on AWS:

1. Install Great Expectations using pip:

pip install great_expectations

2. Initialize Great Expectations in your project:

great_expectations init

3. Configure your data source (e.g., S3, SQL database):

# In great_expectations.yml
datasources:
  my_s3_datasource:
    class_name: Datasource
    data_connectors:
      default_runtime_data_connector:
        class_name: RuntimeDataConnector

4. Code Examples

Here’s an example of how to create and validate expectations:

import great_expectations as ge

# Load your data
df = ge.read_csv("s3://your-bucket/data.csv")

# Create an Expectation Suite
suite = df.expectation_suite

# Add expectations
df.expect_column_values_to_be_in_set("column_name", ["value1", "value2"])

# Validate data
results = df.validate()
print(results)

5. Best Practices

• Define expectations early in the data pipeline.
• Use data profiling to understand data distributions before validation.
• Regularly monitor and update expectations as data evolves.
• Utilize Data Docs to communicate findings with stakeholders.

6. FAQ