Winsure Data Loading Process Update

Winsure Data Loading Process Update

Tools & Technologies

• Data Warehouse: Snowflake

• Languages: Python, SQL

Situation

A critical system for managing insurance-related data, Winsure stored and processed vital data used in analytical dashboards and reporting. The existing nightly data-loading process from Winsure to Snowflake experienced frequent errors and failures, compromising data quality and reliability. This posed risks for accurate reporting and analysis. To address these issues, a project was initiated to update and enhance the data-loading process, ensuring the integrity of the data being transferred to Snowflake.

Task

The key objectives of the project were to:

• Improve the efficiency and reliability of the data-loading process from Winsure to Snowflake.

• Implement measures to prevent data duplication and ensure data integrity during the loading process.

• Maintain a high standard of data quality to support accurate reporting and analysis.

• Reduce the frequency of errors and failures in the nightly data load, thus enhancing system performance.

Action

The project was completed through a series of structured steps:

1. Creation of Temporary Raw Tables:
   A task was created to generate temporary raw tables using data from the staging environment. These tables were truncated before new data was inserted, enabling historical data retention and allowing for time travel to ensure no previous data was lost in the process.

2. Loading Raw Tables:
   Specific procedures were executed for each dataset to load the data into raw tables. This process was crucial for organizing the data before additional transformations.

3. Creation of Temporary Unpacked Raw Tables:
   Temporary unpacked tables were created for each dataset, and row counts were conducted to ensure data integrity before proceeding.

4. Loading to Master Tables:
   Data was then loaded into master tables, ensuring clean and accurate data was processed. For tables without primary keys, these were added to an exception list, and a procedure was implemented to check for duplicates. Any identified duplicates were removed, preventing erroneous data from entering the master tables.

5. Quality Control:
   A final quality check compared row counts across schemas with a 0.01% discrepancy threshold. Any discrepancy beyond this threshold triggered an immediate alert to address potential data inconsistencies.

6. Clean-up:
   After each load, data from the staging area was cleared to avoid future duplication. This ensured each nightly load began with fresh data, minimizing duplication risk.

Each step was monitored carefully, with error-handling mechanisms in place, such as try-except functionality. Alerts were emailed to notify the team of any issues, allowing quick resolution.

Result

The updated Winsure data-loading process resulted in significant improvements in both efficiency and reliability. Key outcomes included:

• Enhanced Data Quality: The risk of duplicate data entries was minimized, ensuring accurate data for reporting and analysis.

• Improved Reliability: The frequency of failures during the nightly data loads was significantly reduced.

• Operational Stability: The new process provided a stable and trustworthy data foundation, leading to more accurate and timely dashboards and reports for analysts.

Challenges & Solutions

• Challenge: Some tables lacked primary keys, making identifying and eliminating duplicates difficult.

• Solution: These tables were added to an exception list to exclude them from duplicate control processes. Although not critical, this highlighted the need for a more robust primary key implementation, which the development team communicated to address in future updates.

Impact & Contributions

The updated data-loading process significantly improved overall data quality and workflow efficiency. Reduced errors and improved data integrity increased the satisfaction of both data engineers and analysts. The enhanced process allowed for quicker and more accurate report generation, benefiting stakeholders and supporting better decision-making across the company.

Conclusion:

This project showcases strong skills in identifying inefficiencies, designing effective solutions, and implementing processes that ensure high data quality and system reliability. The focus on data integrity, quality control, and ETL processes highlights critical competencies in data engineering.

Visuals & Samples & Link to Full Project

Visuals and links are unavailable due to confidentiality and the internal nature of the data.