hmcts/ARIAMigration-Databrick
May 2025 – Jun 2025
2 Months active
Languages Used
Python
Technical Skills
Data ValidationDatabricksPySparkPythonTest AutomationAutomation Testing
Avi Singh
PROFILE
Avi Singh
Avi Singh developed and enhanced automated data quality testing for the hmcts/ARIAMigration-Databrick repository, focusing on the Archive functionality within migration workflows. He implemented Python and PySpark-based test automation to validate Appeals and Bails data against evolving schemas and business rules, introducing automated test data generation and robust validation across JSON, A360, and HTML outputs. Avi improved HTML parsing performance, strengthened case-number extraction, and aligned data quality checks with current data structures, reducing test flakiness and manual effort. His work provided repeatable, auditable validation across data pipelines, accelerating regression testing and supporting more reliable, lower-risk migration releases in production.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
3
Lines of code
10,072
Activity Months2
Your Network
185 people
Work History
June 2025
3 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for hmcts/ARIAMigration-Databrick. Focused on automation test improvements and data quality alignment to accelerate feedback and reduce flaky tests in migration validation. Delivered two key feature clusters and aligned tests with current data structures, enabling more reliable releases.
3 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for hmcts/ARIAMigration-Databrick. Focused on automation test improvements and data quality alignment to accelerate feedback and reduce flaky tests in migration validation. Delivered two key feature clusters and aligned tests with current data structures, enabling more reliable releases.
June 2025
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for hmcts/ARIAMigration-Databrick. Focused on strengthening data quality assurance for Archive functionality within the migration workflow. Delivered comprehensive Archive Data Quality Test Automation to validate Appeals and Bails data against schemas and business rules, with automated test data generation and end-to-end validation across JSON, A360, and HTML outputs. This work reduces data quality risk in migration, accelerates regression testing, and provides repeatable, auditable checks across data pipelines.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for hmcts/ARIAMigration-Databrick. Focused on strengthening data quality assurance for Archive functionality within the migration workflow. Delivered comprehensive Archive Data Quality Test Automation to validate Appeals and Bails data against schemas and business rules, with automated test data generation and end-to-end validation across JSON, A360, and HTML outputs. This work reduces data quality risk in migration, accelerates regression testing, and provides repeatable, auditable checks across data pipelines.
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture65.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Automation TestingData EngineeringData Quality TestingData ValidationDatabricksPySparkPythonPython ScriptingSparkTest Automation
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