Automated Testing Solutions for Data Science and Engineering Teams: Anticipating Success
Great Expectations is a Python library designed to streamline data validation, documentation, and profiling in data pipelines. This powerful tool is particularly useful in retail analytics, ensuring data integrity and reliability.
Data Validation with Great Expectations
Great Expectations allows you to define rules for your retail data, acting as automated tests that validate incoming data at various stages of the pipeline. These rules, or expectations, can range from ensuring no missing values in to checking for valid customer IDs.
By automating data validation checks during data ingestion, transformation, or before loading into analytics tables, you can catch anomalies early in the pipeline, helping to maintain data quality.
Data Profiling and Documentation
Great Expectations can generate data profiles by sampling your retail datasets to understand distributions, missing values, unique counts, patterns, and more. These profiles inform what expectations to set and provide ongoing monitoring of data quality trends.
Furthermore, GX auto-generates elegant, human-readable data documentation sites (Data Docs) that summarize expectation suites, validation results, and profiling insights. These docs act as living documentation of your data quality standards and validation history, useful for data stewards and analysts.
Implementing Great Expectations in a Retail Analytics Pipeline
To implement Great Expectations in your retail analytics pipeline, follow these steps:
- Installation & Setup: Install Great Expectations in your Python environment and initialize a GX project. Configure datasources for your retail data storage, such as databases, files, or Snowflake tables.
- Creating Expectation Suites: Use the GX CLI or Python APIs to create expectation suites tailored to your retail data's schema and business logic.
- Integrate Validation Calls in Pipeline: Embed GX validation steps at critical pipeline points to programmatically validate data against expectations.
- Automated Documentation & Monitoring: Use GX Data Docs to publish validation results and data quality dashboards to a web endpoint or internal network.
- Profiling to Discover Expectations: Run GX profiling on new or evolving datasets to auto-generate initial expectation suites reflecting actual data characteristics.
Benefits of Using Great Expectations
In a retail analytics pipeline that handles orders, inventory, customers, and sales data, Great Expectations ensures data completeness and correctness before analytics, consistency across raw and transformed layers, early detection of issues, and comprehensive documentation of data quality.
By integrating Great Expectations with Python ETL scripts, Snowflake processing, and data observability tools, you build a robust framework for maintaining high data integrity, enabling scalable and trustworthy retail analytics.
In summary, Great Expectations serves as the backbone of automated data quality assurance, documentation, and profiling within your retail analytics pipeline when embedded systematically at extraction, transformation, and loading stages alongside observability infrastructure. This leads to trusted, documented data ready for analytical consumption.
You can download the example dataset from the datasets repository on the author's GitHub page. Great Expectations is easy to implement and has a standard, highly intuitive syntax. The library has many Expectations defined in the core library but is not limited to them. The output of Expectations is in a dictionary format for easy use in pipelines.
With Great Expectations, you can assert what is expected from your data to catch issues quickly. Some expectations, such as expect_column_values_to_be_between, check if the values in a column are between specified minimum and maximum values. The names of the expectations are self-explanatory, making it easy to understand what they do.
Great Expectations can check if the maximum value of a column is within a specific range and allows for asserting the uniqueness of values for a column, such as an id column. If a value fails an expectation, the output of the Expectation contains information such as observed values, number of values, and missing values in the column, helping you quickly identify and resolve issues.
Great Expectations can be installed via pip and imported into a Python environment. It's a valuable tool for any data professional looking to maintain high-quality data in their retail analytics pipelines.
- Incorporating Great Expectations into a home-and-garden e-commerce platform's lifestyle data, one could automate checking for valid customer information and maintaining data integrity in the sustainable-living merchandise section, improving the overall data quality.
- To optimize home-and-garden data pipelines, consider implementing technology such as data-and-cloud-computing solutions combined with Great Expectations for efficient data validation, documentation, and profiling across various home improvements and gardening categories, ensuring seamless operations and reliable consumer experience.
