Data Science and Engineering Teams Aim for High Standards: Implementing Automated Testing Methods
Great Expectations is a Python library designed to enhance data quality and communication in retail analytical solutions. This automated, rule-based framework offers a robust solution for validating, profiling, and documenting datasets.
Automated Data Profiling and Validation
Great Expectations can automatically generate data quality rules (expectations) based on the data, or users can manually define detailed validations tailored to retail data needs. For instance, it can check if daily sales figures, inventory counts, or customer transaction records are within expected ranges or formats.
Integration with Data Workflows
By integrating with feature stores or ETL pipelines (e.g., Feast), Great Expectations validates historical and on-demand features before consumption, preventing bad data from corrupting analytics.
Clear, Human-Readable Documentation
Validation results are presented as easy-to-understand reports, promoting transparency and improving communication among data engineers, analysts, and business stakeholders.
Early Detection and Prevention of Data Issues
Validation failures raise explicit exceptions that halt downstream processing, enabling proactive correction rather than reactive firefighting.
Key Features and Expectations
Great Expectations supports a variety of expectations, including:
- Expectation 2: checks if the maximum value of a column is between a specific range.
- Expectation 3: ensures all the values in a column are unique.
- Expectation 4: verifies if a particular column exists in the dataset.
- Expectation 5: checks if a value in a store column is in a given list.
The library has many pre-defined expectations in the core library, but it is not limited to or dependent on only these. The names of the expectations are self-explanatory, making it easy to understand what each expectation is checking.
Great Expectations also allows for asserting what is expected from the data, helping to catch data issues quickly and at an early stage. The main component of the library is Expectation, which is a declarative statement that can be evaluated by a computer.
Advantages for Retail Businesses
For retail businesses, Great Expectations offers several benefits:
- Reducing errors in transaction, inventory, and customer data.
- Ensuring consistent, reliable inputs for forecasting and analytics.
- Improving trust in dashboards and machine learning models.
- Enhancing collaboration and clarity across teams by linking data expectations directly to business logic.
Easy Implementation and Syntax
Great Expectations has an easy-to-implement and highly intuitive syntax. The dataset can be created from a Pandas DataFrame or a CSV file using the respective functions provided by Great Expectations. An example dataset for Great Expectations can be downloaded from the author's GitHub page.
Some expectations in Great Expectations require writing many lines of code if done individually using pure Python or other packages. However, with Great Expectations, these complex validations can be performed more efficiently.
Installation and Usage
Great Expectations can be installed via pip and imported for use. The outputs of Expectations include valuable insights such as the number of unexpected values and a partial unexpected value list. Expectations are essentially unit tests for data.
In conclusion, Great Expectations is a powerful tool for improving the accuracy, reliability, and clarity of data-driven retail solutions throughout their lifecycle. By automating data validation, profiling, and documentation, it enables retail businesses to make more informed decisions and enhance their overall analytical capabilities.
In the realm of home-and-garden projects, Great Expectations can be likened to a digital assistant that ensures data quality in a similar way, profiling and validating datasets automatically to maintain the robustness of home-garden analytical solutions.
Moreover, just as technology has revolutionized the way we live, Great Expectations also brings a technological edge to the lifestyles we lead by streamlining data-and-cloud computing processes with its automated data workflows and rule-based framework.
