hmcts/ARIAMigration-Databrick
Oct 2025 – Dec 2025
3 Months active
Languages Used
BashPythonYAML
Technical Skills
Asynchronous ProgrammingAzure Blob StorageAzure CLIAzure FunctionsAzure PipelinesBlob Storage
NaveenSriramAddagarla
PROFILE
Naveensriramaddagarla
Naveen Sriram contributed to the hmcts/ARIAMigration-Databrick repository by engineering robust data migration pipelines and enhancing deployment reliability over a three-month period. He focused on optimizing Azure Functions for throughput and stability, tuning batch sizes, and implementing concurrency controls using Python and YAML. Naveen refactored the Appeals data processing pipeline to support both JSON and plain URL blob transfers, improving error handling and resource cleanup. He also addressed critical storage integration issues by aligning secret management with storage account configurations. His work demonstrated depth in asynchronous programming, CI/CD, and cloud infrastructure, resulting in more resilient and maintainable data workflows.
Overall Statistics
Feature vs Bugs
78%Features
Repository Contributions
25Total
Bugs
2
Commits
25
Features
7
Lines of code
2,729
Activity Months3
Your Network
227 peopleShared Repositories
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Work History
December 2025
1 Commits
Dec 1, 2025December 2025 (hmcts/ARIAMigration-Databrick): Stabilized storage integration for the Azure Function used in data migration. Delivered a critical bug fix by reverting to the TT storage account and aligning container secrets retrieval with the reverted storage configuration. This ensured correct storage credentials and reliable function operation, reducing the risk of credential-related failures and outages in the migration workflow. Demonstrated hands-on capability with Azure Functions, storage account configuration, and secret management, with clear change traceability and impact on production reliability.
1 Commits
Dec 1, 2025December 2025 (hmcts/ARIAMigration-Databrick): Stabilized storage integration for the Azure Function used in data migration. Delivered a critical bug fix by reverting to the TT storage account and aligning container secrets retrieval with the reverted storage configuration. This ensured correct storage credentials and reliable function operation, reducing the risk of credential-related failures and outages in the migration workflow. Demonstrated hands-on capability with Azure Functions, storage account configuration, and secret management, with clear change traceability and impact on production reliability.
December 2025
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.
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.
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.
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
Activity
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Quality Metrics
Correctness79.6%
Maintainability82.4%
Architecture74.4%
Performance68.8%
AI Usage20.8%
Skills & Technologies
Programming Languages
BashPythonYAML
Technical Skills
API IntegrationAsynchronous ProgrammingAzure Blob StorageAzure CLIAzure FunctionsAzure PipelinesBlob StorageBug FixingCI/CDCloud ComputingConcurrency ControlData EngineeringDevOpsError HandlingEvent Hubs
Repositories Contributed To
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