
Daniel Johnstone contributed to the ministryofjustice/analytical-platform-airflow repository by designing and implementing workflow automation solutions for data extraction and processing, with a focus on divorce case analytics. He leveraged AWS, Airflow, and YAML to build configurable, maintainable pipelines that enabled SQL-based access to Fabric system data and automated divorce data ingestion. His work included developing multi-task DAGs, refining workflow configurations, and enhancing deployment reliability through CI/CD best practices. Daniel also addressed data coverage issues by updating extraction ranges and releasing targeted patches, demonstrating a methodical approach to workflow management and ensuring robust, auditable data pipelines for downstream analytics teams.
March 2026 focused on stabilizing data extraction pipelines for the divorce dataset within the ministryofjustice/analytical-platform-airflow repository. Delivered a critical data range fix in the Divorce Extraction Workflow by updating the cutoff date from 2026-01-20 to 2023-01-01 and released a patch (v3.0.10). This change ensures complete data coverage and aligns historical extractions with updated requirements, reducing data gaps in downstream reporting.
March 2026 focused on stabilizing data extraction pipelines for the divorce dataset within the ministryofjustice/analytical-platform-airflow repository. Delivered a critical data range fix in the Divorce Extraction Workflow by updating the cutoff date from 2026-01-20 to 2023-01-01 and released a patch (v3.0.10). This change ensures complete data coverage and aligns historical extractions with updated requirements, reducing data gaps in downstream reporting.
February 2026: Delivered two priority features in ministryofjustice/analytical-platform-airflow to strengthen data ingestion and workflow reliability for divorce-case analytics.
February 2026: Delivered two priority features in ministryofjustice/analytical-platform-airflow to strengthen data ingestion and workflow reliability for divorce-case analytics.
Month: 2026-01 — Delivered a new divorce data extraction workflow configuration in ministryofjustice/analytical-platform-airflow, introducing a multi-task DAG with environment-variable driven tasks for divorce records; improved maintainability and deployment reliability through YAML workflow definitions and lint fixes; updated the divorce extract cutoff date to reflect current data window. These changes, implemented via commits cc60a14da8cf0c1889c41320f3c4939e2b5f7c09 and d6764714821c946d1bae590a4743258e7ae64b33, advance automated data collection, reduce manual intervention, and support downstream analytics.
Month: 2026-01 — Delivered a new divorce data extraction workflow configuration in ministryofjustice/analytical-platform-airflow, introducing a multi-task DAG with environment-variable driven tasks for divorce records; improved maintainability and deployment reliability through YAML workflow definitions and lint fixes; updated the divorce extract cutoff date to reflect current data window. These changes, implemented via commits cc60a14da8cf0c1889c41320f3c4939e2b5f7c09 and d6764714821c946d1bae590a4743258e7ae64b33, advance automated data collection, reduce manual intervention, and support downstream analytics.
December 2025 performance summary for ministryofjustice/analytical-platform-airflow. Key feature delivery centered on integrating Athena into the Fabric system workflow to enhance data processing and analytics capabilities. The work enables SQL-based access to Fabric data within the existing Airflow-driven pipeline, improving time-to-insight and data accessibility for analytics teams. Code quality and maintainability were prioritized via YAML-driven configuration, formatting, linting, and IAM-related adjustments to ensure production-readiness.
December 2025 performance summary for ministryofjustice/analytical-platform-airflow. Key feature delivery centered on integrating Athena into the Fabric system workflow to enhance data processing and analytics capabilities. The work enables SQL-based access to Fabric data within the existing Airflow-driven pipeline, improving time-to-insight and data accessibility for analytics teams. Code quality and maintainability were prioritized via YAML-driven configuration, formatting, linting, and IAM-related adjustments to ensure production-readiness.

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