Enhancing Data Quality in Snowflake Using a Streamlit Framework

Introduction

In today's data-driven landscape, organizations rely heavily on accurate and reliable data to make informed decisions. Snowflake, as a leading cloud-based data platform, offers robust solutions for managing vast amounts of data. However, ensuring the quality of this data remains a critical challenge.

To address this, I have developed the Snowflake Data Quality Assessment Framework, a Streamlit application designed to assess and enhance data quality within Snowflake databases. This article provides a comprehensive guide on how to use this framework to evaluate your data's quality and improve your data governance practices.

Demo Video: Watch the Framework in Action

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Background on Data Quality

Data quality refers to the condition of a set of values of qualitative or quantitative variables. High-quality data is crucial for:

• Accurate Analytics: Ensuring business intelligence reports and dashboards reflect true insights.

• Regulatory Compliance: Meeting legal and industry standards for data handling.

• Operational Efficiency: Reducing errors and inefficiencies in business processes.

• Customer Satisfaction: Providing reliable services and products to clients.

Key Dimensions of Data Quality:

1. Accuracy: The degree to which data correctly describes the real-world object or event.

2. Completeness: The extent to which all required data is known.

3. Consistency: The absence of differences when comparing two or more representations of a thing.

4. Timeliness: The degree to which data is up-to-date and available within a useful time frame.

5. Uniqueness: No data record is duplicated.

6. Validity: Data conforms to the syntax (format, type, range) of its definition.

These dimensions are based on the "Dimensions of Data Quality" research paper, which outlines essential criteria for evaluating data quality.

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The Snowflake Data Quality Assessment Framework

The framework is an interactive Streamlit application that connects to your Snowflake database to perform data quality assessments based on the key dimensions mentioned above.

Features:

• Data Maturity Score: Provides an overall score representing your data's quality level.

• Detailed Analysis: Breaks down the assessment by each data quality dimension.

• User-Friendly Interface: Easy navigation and interpretation of results.

• Real-Time Assessment: Connects directly to your Snowflake database for up-to-date evaluations.

• Extensibility: Ability to add more data quality checks as per organizational needs.

Technologies Used:

• Python: The core programming language for the application.

• Streamlit: For building the interactive web application.

• Pandas: For data manipulation and analysis.

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Installation and Setup

Prerequisites

• Access to a Snowflake database with appropriate permissions.

Steps

1. Clone the Repository

    bashCopy codegit clone https://github.com/TheMohammedZeeshan/snowflake-data-quality-framework.git
    cd snowflake-data-quality-framework
   

2. Paste the code in Snowflake Streamlit section

3. 

   And Thats it!!!

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Using the Framework

Step 1: Connect to Snowflake

• Upon launching the application, you'll be prompted to connect to your Snowflake database.

• Navigate to Streamlit section under Projects

Step 2: Select Schema and Table

• Choose the schema and table you wish to assess from dropdown menus.

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Step 3: Run Data Quality Assessment

• Upon Schema’s and Tables selection, your assessment automatically runs

• The application will execute various data quality checks on the selected table.

Step 4: View Results

• Once the assessment is complete, the application will display:

  • Data Maturity Score: Overall score based on all dimensions.

  • Detailed Reports: Individual scores and findings for each data quality dimension.

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Understanding the Results

Data Maturity Score

• Definition: An aggregate score representing the overall quality of your data.

• Interpretation:

  • 90-100: Excellent data quality.

  • 70-89: Good data quality with some areas for improvement.

  • 50-69: Fair data quality; significant improvements needed.

  • Below 50: Poor data quality; immediate action required.

Detailed Dimension Reports

1. Accuracy

   • What It Checks: Correctness of data values.

   • How to Improve: Validate data against reliable sources; implement data validation rules.

2. Completeness

   • What It Checks: Presence of all required data fields.

   • How to Improve: Ensure mandatory fields are not left blank; set default values where appropriate.

3. Consistency

   • What It Checks: Uniformity of data across records and tables.

   • How to Improve: Standardize data formats; enforce data type consistency.

4. Timeliness

   • What It Checks: Data is up-to-date and reflects the current state.

   • How to Improve: Implement regular data refresh schedules; monitor data latency.

5. Uniqueness

   • What It Checks: Absence of duplicate records.

   • How to Improve: Use primary keys and unique constraints; perform de-duplication processes.

6. Validity

   • What It Checks: Data conforms to defined formats and rules.

   • How to Improve: Define and enforce data schemas; use validation checks during data entry.

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Use Cases and Benefits

Data Governance

• Benefit: Enhances data governance by providing insights into data quality issues.

• Application: Helps in setting data standards and policies within the organization.

Regulatory Compliance

• Benefit: Assists in meeting compliance requirements by ensuring data accuracy and completeness.

• Application: Useful for industries with strict data regulations, such as finance and healthcare.

Business Intelligence

• Benefit: Improves the reliability of analytics and reporting.

• Application: Enables better decision-making based on high-quality data.

Operational Efficiency

• Benefit: Reduces errors and inefficiencies in business processes.

• Application: Streamlines operations by identifying and correcting data quality issues.

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Conclusion

Ensuring high data quality is essential for leveraging the full potential of your data assets. The Snowflake Data Quality Assessment Framework provides a practical and efficient way to evaluate and enhance the quality of your data stored in Snowflake.

By regularly using this framework, organizations can:

• Improve Data Trustworthiness: Build confidence in data-driven decisions.

• Enhance Data Governance: Establish robust data management practices.

• Increase Operational Efficiency: Reduce costs associated with poor data quality.

Call to Action:

• Try the Framework: GitHub Repository

• Watch the Demo: YouTube Video

• Contribute: Feel free to contribute to the project by submitting issues or pull requests.

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References

1. Dimensions of Data Quality
   dama-uk.org

2. Snowflake Documentation
   https://docs.snowflake.com/

3. Streamlit Documentation
   https://docs.streamlit.io/

4. Pandas Documentation
   https://pandas.pydata.org/docs/

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Contact Information:

• Author: Mohammed Zeeshan

• Email: reach2zeeshan@gmail.com

• GitHub: TheMohammedZeeshan