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Great Expectations is a powerful open-source library that can be used to build an Expectation suite using Rule-based profilers. Here are the steps to do it:

1. Define the data source: First, define the data source, which could be a database, a CSV file, or any other data source.

2. Create a data context: Next, create a data context using Great Expectations. A data context provides the context for your data and allows you to define expectations for your data.

3. Define a suite: Once you have created a data context, define an expectation suite. An expectation suite is a collection of expectations that you want to apply to your data.

4. Define expectations: Define expectations using rule-based profilers. For example, you could define an expectation that a particular column should contain only integers, or that a particular column should have no missing values.

5. Apply the suite: Apply the expectation suite to your data source. Great Expectations will evaluate your data based on the defined expectations and provide you with a report on the quality of your data.

By following these steps, you can use Great Expectations to build an Expectation suite using Rule-based profilers. This will help you to ensure the quality and integrity of your data and make better decisions based on your analysis.