Leveraging Python for Effective Data Profiling in Migration Projects

Introduction to Data Profiling

Data profiling is a critical step in any data project, especially during data migration. As organizations shift from legacy systems to modern platforms, ensuring data quality and integrity is essential. This helps avoid inconsistencies and errors in the new system.

Before setting out the business rules and transformations needed for data migration, conducting data profiling is crucial. This process provides deep insights into the data's structure, completeness, and overall health. It helps identify issues early and informs the migration strategy for a smooth transition.

In this blog, we will explore how Python can be used for data profiling. This powerful tool can help teams generate comprehensive reports that inform data mapping rules.

Understanding Data Profiling

Data profiling refers to the process of analyzing data to collect statistics and metadata. It helps uncover several aspects, including:

• Data types (numeric, text, date, etc.)

• Missing values and their distribution

• Duplicate records

• Outliers and anomalies

• Patterns and format inconsistencies

• Correlations between fields

  
  

Understanding these characteristics allows teams to define data mapping rules such as field transformations, validation checks, and cleansing steps before migration.

Automating Data Profiling with ydata-profiling

Python provides various libraries for data profiling. One of the most powerful and widely used tools is ydata-profiling (formerly pandas-profiling). This library automatically generates an HTML report summarizing key dataset statistics.

What the Report Contains

The report generated by ydata-profiling includes several critical elements:

• Overview: Details on dataset structure, number of rows and columns, memory usage.

• Variables: Summary statistics and information about unique and missing values.

• Interactions: Analysis of correlations between fields.

• Missing Values: Visual heatmaps illustrating the distribution of null data.

• Duplicate Rows: Identification of redundant records.

• Text Analysis: Insights into common patterns and inconsistencies.

  
  

Data Profiling Report: Overview of dataset quality, highlighting key issues and suggested transformations.

Enhancing Mapping Logic Using Profiling Insights

During data migration projects, teams often use a mapping sheet (typically in Excel or CSV format) to define how fields from the legacy system map to the new system. However, simply listing column names isn't enough. It’s necessary to understand each field's quality and nature to apply the right transformation logic.

Our Approach to Data Profiling

We automate the profiling-to-mapping process in three clear steps:

1. Input the Source

The source data can be from either:

• A CSV file (e.g., `legacy_data.csv`)

• A SQL query result (e.g., `SELECT * FROM legacy_table`)

  
  

2. Reference a Mapping Sheet

The mapping sheet should include the following columns:

• Source Field Name

• Target Field Name

• Data Type

• Transformation Rule (to be populated)

  
  

3. Profile Only the Mapped Columns

Using Python, we follow these steps:

• Subset the dataset to include only columns listed in the mapping sheet.

• Apply ydata-profiling on those specific columns.

• Update the mapping sheet with findings such as:

- Suggested Transformations: Useful methods like `.upper()`, `.strip()`, or pd.to_datetime().

- Null Handling: Strategies to fill with default values, drop rows, or flag for review.

- Formatting Needs: Regular expressions (regex) for inputs like phone numbers (^\+61\d{9}$), ABNs, and emails.

- Duplicate Checks: Identifying fields with repeated values that need deduplication.

- Inconsistencies: Addressing mixed types or inconsistent date formats.

Monitoring Data Quality with Power BI

Another effective application of data profiling is leveraging Power BI to track data quality over time. By establishing incremental refresh in Power BI, teams can consistently monitor trends and improvements in data quality.

As data profiling highlights issues like missing values, duplicates, and inconsistencies, these can be visualized in Power BI dashboards to monitor changes. This ongoing oversight ensures that data quality enhances with each migration phase. It helps teams address critical data integrity issues proactively.

Conclusion

Data profiling is a vital pre-migration step. It empowers teams to understand their data's current state, define suitable transformation rules, and reduce migration risks. By using Python and tools like ydata-profiling, you can automate this process and glean detailed insights with minimal effort.

Incorporating data profiling into your migration workflow significantly improves data quality. It lays a solid foundation for a successful transition to the new system. Don’t underestimate the importance of this step in ensuring a smooth and efficient migration.