
Laura Sandoval enhanced the IMAP-Science-Operations-Center/sds-data-manager repository by developing data access configuration and coverage tooling for the pointing construct, integrating Python-based Lambda functions with DynamoDB for SPICE kernel metadata ingestion. She improved local testing infrastructure and broadened API data retrieval windows to increase reliability in production workflows. Using AWS Lambda, Boto3, and Infrastructure as Code via CDK, Laura optimized performance by increasing memory allocation and adding detailed timing instrumentation for request processing. Her work focused on improving data accessibility, observability, and efficiency, resulting in more robust backend data management and streamlined development and testing processes for the project.
October 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 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.

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