hmcts/ARIAMigration-Databrick
Oct 2025 – Nov 2025
2 Months active
Languages Used
BashPythonYAML
Technical Skills
Asynchronous ProgrammingAzure Blob StorageAzure CLIAzure FunctionsAzure PipelinesBlob Storage
NaveenSriramAddagarla
PROFILE
Naveensriramaddagarla
Naveen Sriram engineered and optimized data migration pipelines for the hmcts/ARIAMigration-Databrick repository, focusing on Azure Functions and Blob Storage integration. He enhanced throughput and reliability by tuning batch sizes, implementing chunking strategies, and introducing concurrency controls using Python and YAML. Naveen refactored the Appeals data processing pipeline to support both JSON payloads and plain URLs, improving error handling and resource cleanup. His work included expanding test coverage for critical workflows and rolling back changes when stability required, demonstrating a thoughtful, iterative approach. These efforts improved deployment resilience, reduced failure rates, and laid the foundation for scalable, maintainable data integration.
Overall Statistics
Feature vs Bugs
88%Features
Repository Contributions
24Total
Bugs
1
Commits
24
Features
7
Lines of code
2,721
Activity Months2
Your Network
183 people
Work History
November 2025
3 Commits • 2 Features
Nov 1, 2025November 2025 milestones for hmcts/ARIAMigration-Databrick focused on performance tuning of Azure Functions and robust data processing pipelines. Delivered initial throughput optimization via a concurrency limiter and smaller batch size to boost checkpointing speed and processing throughput (order-agnostic processing), with a rollback to preserve stability after testing. Refactored the Appeals data processing pipeline to download content from source blob URLs (supporting JSON payloads and plain URLs) and upload to target blob storage, enhancing error handling and resource cleanup. These efforts improved end-to-end data migration reliability, throughput, and maintainability, laying groundwork for scalable, resilient data integration.
3 Commits • 2 Features
Nov 1, 2025November 2025 milestones for hmcts/ARIAMigration-Databrick focused on performance tuning of Azure Functions and robust data processing pipelines. Delivered initial throughput optimization via a concurrency limiter and smaller batch size to boost checkpointing speed and processing throughput (order-agnostic processing), with a rollback to preserve stability after testing. Refactored the Appeals data processing pipeline to download content from source blob URLs (supporting JSON payloads and plain URLs) and upload to target blob storage, enhancing error handling and resource cleanup. These efforts improved end-to-end data migration reliability, throughput, and maintainability, laying groundwork for scalable, resilient data integration.
November 2025
October 2025
21 Commits • 5 Features
Oct 1, 2025October 2025 monthly summary: Drove stability and throughput improvements for ARIAMigration-Databrick by tuning TD batch size and chunking in the Azure Functions pipeline, delivering more reliable processing of small files; expanded and hardened deployment lifecycle for reference data, active deployments for TD/FTA, and HTTPS path support; extended testing coverage for FTA/UTA workflows, including BlobURL and curated storage account scenarios; and fixed critical regressions in the TD Function App to restore reliability. These efforts delivered measurable business value through faster data migrations, reduced retry/failure rates, and improved deployment resilience.
October 2025
21 Commits • 5 Features
Oct 1, 2025October 2025 monthly summary: Drove stability and throughput improvements for ARIAMigration-Databrick by tuning TD batch size and chunking in the Azure Functions pipeline, delivering more reliable processing of small files; expanded and hardened deployment lifecycle for reference data, active deployments for TD/FTA, and HTTPS path support; extended testing coverage for FTA/UTA workflows, including BlobURL and curated storage account scenarios; and fixed critical regressions in the TD Function App to restore reliability. These efforts delivered measurable business value through faster data migrations, reduced retry/failure rates, and improved deployment resilience.
Activity
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Quality Metrics
Correctness79.6%
Maintainability82.4%
Architecture74.2%
Performance68.2%
AI Usage20.8%
Skills & Technologies
Programming Languages
BashPythonYAML
Technical Skills
API IntegrationAsynchronous ProgrammingAzure Blob StorageAzure CLIAzure FunctionsAzure PipelinesBlob StorageBug FixingCI/CDConcurrency ControlData EngineeringDevOpsError HandlingEvent HubsHTTP Requests
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