In this blog, we will learn about Great Expectations, the benefits & limitations of Great Expectations, and its key features.
What is Great Expectations?
Great Expectations (GX) is a helpful tool that is used for documenting, validating, and profiling the data to maintain quality and improve communication between teams. It is an open-source tool built in Python. It provides Automation with proper logging and alerts on top of data quality solutions are essential parts of having a successful and reliable solution.
Benefits of Great Expectations:
There are several benefits to Great Expectations:
- It automates the data quality process, saving teams time and decreasing the possibility of human mistakes.
- To meet the specific needs of any organization, GX provides a flexible and powerful framework for data quality.
- It offers a centralized repository of data quality expectations and test results, allowing teams to efficiently track data quality over time.
Limitations of Great Expectations:
- Complexity: For people without technical expertise, setting up and implementing high expectations might be difficult.
- Integration: Although GX offers various built-in connectors, connecting it with specific systems or workflows might be challenging.
- Scalability: The library’s performance may suffer when dealing with big data sets, rapidly changing data, or slowly changing data.
Key Features of GX:
- Expectations: Expectations are one of the main features used for data assertions. These assertions are expressed in a declarative language in the form of simple, human-readable Python methods. The library provides a lot of highly expressive built-in Expectations and allows you to write custom Expectations. For example: In order to assert that you want the column “passenger_count” to be between 1 and 9, you can say:
expect_column_values_to_be_between( column="passenger_count", min_value=1, max_value=9 )
GX will use these statements to check whether the column passenger_count in a particular table is truly between 1 and 9, and it gives a success or failure result.
- Automated Data Profiling: Great Expectations jump-starts the process by providing automated data profiling. On the basis of observed data, the library profiles your data to obtain basic statistics and provides a set of Expectations. This allows you to quickly develop data tests without having to start from zero.
- Data Validation: Great Expectations can load any batch or several batches of data to validate with your suite of Expectations. Great Expectations shows you whether each Expectation in an Expectation Suite succeeds or fails, and it returns any unexpected numbers that failed a test, which may help you fix data issues much faster!
- Data Docs: GX creates clean, human-readable documentation, known as Data Docs. For each time validation, these docs contain both your data Validation results as well as your Expectation Suites.
- Data Contexts and Data Sources: Great expectation supports various data sources such as Pandas data frames, Spark data frames, and SQL databases via SQLAlchemy. Great Expectations is highly configurable. By establishing metadata Stores, you may store all essential metadata, such as Expectations and Validation Results, in file systems, database backends, and cloud storage services like as S3 and Google Cloud Storage.
Profiler in GX:
A profiler is a tool in Great Expectations (GX) that helps you to acquire insights into the qualities and quality of your data. Below are the points which will tell us how profiler works:
The profiler in Great Expectations is intended to assist users in better understanding their data by offering statistical summaries and visualisations.
2. Data Profiling:
It creates summary statistics for columns in your dataset, such as mean, standard deviation, minimum, maximum, and quantiles.
3. Data Visualization:
To highlight the distribution of values within columns, the profiler provides data distribution visualisations such as histograms and box plots.
4. Data Quality Insights:
It detects possible data quality concerns such missing values, unusual value distributions, and outliers.
5. Continuous Profiling:
Profiling can be used to aid users in detecting changes in data distribution and quality over time as part of a continuous monitoring process.
Using Great Expectations’ Data Docs, profiling findings and insights may be documented and shared, encouraging transparency and cooperation in data initiatives.
Below is the example of report that GX will generate.
Steps to install Great Expectations:
- Step 1 – To install Great Expectations, there are a few prerequisites:
a. A working Python install (3.7 to 3.10).
b. The ability to pip install for Python.
c. Working Installation of Git.
d. Web Browsers like Google Chrome, Firefox etc.
- Step 2 – GX requires Python 3 and We can install it using pip. Run this command:
pip install great_expectations
- Step 3 – You may verify that the installation was successful by executing the below command.
- Step 4 – By executing the above command, you will get the below output.
great_expectations, version 0.17.10
Great Expectations is a valuable tool for data teams looking to ensure the accuracy and consistency of their data. Great Expectations, with its adaptable structure and strong features, may assist organizations in preventing data quality concerns and making educated decisions based on reliable data. It may help you manage data quality successfully and efficiently, whether you’re dealing with large or small datasets.