IMAP-Science-Operations-Center/sds-data-manager
Oct 2025 – Oct 2025
1 Month active
Languages Used
Python
Technical Skills
API DevelopmentAWS LambdaBackend DevelopmentBoto3CDKData Engineering
Laura Sandoval
PROFILE
Laura Sandoval
Laura Sandoval developed data access configuration and coverage tooling for the IMAP-Science-Operations-Center/sds-data-manager repository, enabling external data access and improved monitoring for the pointing construct. She implemented SPICE kernel metadata ingestion into DynamoDB using Python and AWS Lambda, adding traceability through detailed logging. Laura enhanced local testing infrastructure, allowing tests to run reliably outside production, and broadened API data retrieval windows for more comprehensive access. Her work included performance instrumentation and memory optimization for Lambda functions, as well as efficient resource initialization. These contributions improved data workflow reliability, observability, and maintainability, demonstrating depth in backend development and data engineering.
Overall Statistics
Feature vs Bugs
71%Features
Repository Contributions
9Total
Bugs
2
Commits
9
Features
5
Lines of code
387
Activity Months1
Your Network
27 people
Work History
October 2025
9 Commits • 5 Features
Oct 1, 2025October 2025 monthly summary for IMAP-Science-Operations-Center/sds-data-manager: Key features delivered, major bugs fixed, overall impact, and technologies demonstrated. Key features delivered: - Data access configuration and coverage tooling for the pointing construct: introduced IMAP_DATA_ACCESS_URL and integrated ialirt_coverage.py into the pointing lambda deployment to support testing and monitoring. - Kernel metadata ingestion for SPICE kernels: implemented insertion into DynamoDB via insert_kernels; updated lambda_handler to trigger ingestion and added logs for traceability and audit. Major bugs fixed: - Testing infrastructure improvements for local execution: patched ialirt_coverage to enable tests locally and corrected test environment configuration. - API and data retrieval window improvement: broadened route restrictions and extended data query window from last 5 minutes to last hour to ensure more comprehensive data retrieval in production. Additional improvements: - Dependency updates for IMAP libraries: upgraded imap-processing to 1.0.1 and imap-data-access to >=0.37.0. - Performance instrumentation for Lambda and parsing: increased memory for two Lambda functions and added detailed timing logs to measure stages of request processing for bottleneck identification. - DynamoDB initialization efficiency: moved DynamoDB table resource initialization to the top level in IAlirtCode Lambda functions and updated timer summaries to reflect this change. Overall impact and accomplishments: These changes improve data accessibility, observability, auditability, and efficiency, enabling more reliable production data workflows, faster issue diagnosis, and smoother local testing. Technologies/skills demonstrated: DynamoDB, SPICE kernel ingestion, Lambda architecture, observability instrumentation, environment/config management, and test infrastructure improvements.
9 Commits • 5 Features
Oct 1, 2025October 2025 monthly summary for IMAP-Science-Operations-Center/sds-data-manager: Key features delivered, major bugs fixed, overall impact, and technologies demonstrated. Key features delivered: - Data access configuration and coverage tooling for the pointing construct: introduced IMAP_DATA_ACCESS_URL and integrated ialirt_coverage.py into the pointing lambda deployment to support testing and monitoring. - Kernel metadata ingestion for SPICE kernels: implemented insertion into DynamoDB via insert_kernels; updated lambda_handler to trigger ingestion and added logs for traceability and audit. Major bugs fixed: - Testing infrastructure improvements for local execution: patched ialirt_coverage to enable tests locally and corrected test environment configuration. - API and data retrieval window improvement: broadened route restrictions and extended data query window from last 5 minutes to last hour to ensure more comprehensive data retrieval in production. Additional improvements: - Dependency updates for IMAP libraries: upgraded imap-processing to 1.0.1 and imap-data-access to >=0.37.0. - Performance instrumentation for Lambda and parsing: increased memory for two Lambda functions and added detailed timing logs to measure stages of request processing for bottleneck identification. - DynamoDB initialization efficiency: moved DynamoDB table resource initialization to the top level in IAlirtCode Lambda functions and updated timer summaries to reflect this change. Overall impact and accomplishments: These changes improve data accessibility, observability, auditability, and efficiency, enabling more reliable production data workflows, faster issue diagnosis, and smoother local testing. Technologies/skills demonstrated: DynamoDB, SPICE kernel ingestion, Lambda architecture, observability instrumentation, environment/config management, and test infrastructure improvements.
October 2025
Activity
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Quality Metrics
Correctness86.6%
Maintainability89.0%
Architecture80.0%
Performance77.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
API DevelopmentAWS LambdaBackend DevelopmentBoto3CDKData EngineeringData ManagementDependency ManagementDynamoDBInfrastructure as CodeLambda FunctionsMetadata ManagementPerformance OptimizationPythonPython Packaging
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