February 2026 (2026-02) monthly summary for prefeitura-rio/prefect_rj_iplanrio. Focused on stabilizing and accelerating deployments, strengthening data processing pipelines, and improving code quality for business reliability. Key deliverables include deployment automation with force deploy capabilities across environments, Dockerfile and configuration modernization to align with master, and enhanced flow management with CVL monthly reporting. Also delivered transcription enhancements with parameterized input and robust error handling, plus new scheduling and CSV export capabilities. Notable bug fixes improved system correctness and resilience across the stack. Key achievements: - Implemented automated deployment workflow with force deploy across environments, improving release reliability and speed. - Modernized container/build configuration (Dockerfiles, Prefect config, pyproject) to align with master, reducing drift and build failures. - Introduced enhanced flow management and CVL monthly report flow with improved logging and page initialization for more reliable data pipelines. - Added transcription module enhancements with transcription as a parameter and robust handling of transcription failures in JSON data. - Implemented scheduling and data export workflows: CVL Pesquisa scheduler, Relatorio CVL cron-based scheduling, and CSV export/save functionality.

January 2026 — Key features delivered for prefeitura-rio/prefect_rj_iplanrio: - Scheduling Automation and Cleanup: Prefect-driven daily data processing workflows with automated schedules, activations, and cleanup of unused schedules to ensure reliable citizen service reminders and payment notifications. - Google Sheets-based Flow Control: Environment-driven control of flows via Google Sheets to manage dispatch templates and scheduling. - Discord-based Notifications for Dispatch/Flow Failures: Enriched alerts with context and environment awareness for failures such as missing recipients, no mobile numbers, inactive flows, and flow run failures. Major bugs fixed and cleanup: removal of unused schedules; deactivation of obsolete schedulers; improved error context and alerts for dispatch/flow failures, including missing mobile numbers and recipient validation.

December 2025 milestone: Delivered and stabilized mission-critical automation for debt reminders and PIC queries, expanded data accuracy and operational capacity, and strengthened deployment reliability across prefeitura-rio/prefect_rj_iplanrio and prefeitura-rio/queries-rj-iplanrio. Key investments focused on scheduling automation, data quality, geolocation performance, and end-to-end deployment readiness, driving tangible business value in timely notifications, reduced duplicate dispatches, and scalable processing under higher loads.

November 2025 — Delivered a focused set of features and fixes in prefeitura-rio/prefect_rj_iplanrio to improve deployment velocity, data quality, and scheduling reliability. Key outcomes include enhanced deployment automation, CRM data pipeline improvements, and template-driven flow scheduling with better observability. Key features delivered: - Deployment Automation: Force Deploy — Enabled urgent releases by bypassing standard flow checks in Prefect and CI/CD pipelines, accelerating hotfix delivery while preserving safety checks. - Days Ahead processing and configuration: Refactored days_ahead handling by removing it from prefect.yaml, adjusting scheduling processing, and introducing a days_ajead constant to standardize timing logic. - CRM and data enhancements: Expanded cost_center_id handling, added more query parameters, implemented CPF-based filtering and WhatsApp availability filtering, and introduced a filter on 1746 to restrict same-day dispatch results. Added mock Cadúnico queries for testing scenarios and improved debugging with explicit logs. - Scheduling and flows: Implemented template-driven scheduling for flows, added new flows, and created a Python script to push schedulers to staging and production environments. Included weekly and divida_ativa scheduling improvements and PIC dispatch scheduling. - Whitelist, templates and dispatch: Enhanced whitelist management with template integration and testing, updated template naming, and refined dispatch routing. - Observability and quality: Added debug prints, improved YAML handling and error visibility, and completed multiple housekeeping tasks (documentation updates, secret handling fixes, and removal of deprecated data_catalog usage). Major bugs fixed: - Days Ahead Type Fix: Resolved runtime issues by correcting days_ahead data types. - CRM: General bugfixes and dispatch whitelist fixes. - Weekend Flow Handling: Fixed query behavior when flows run on weekends. - Secret Handling Fixes: Hardened secret usage to prevent leakage. - CI/CD: Force Deploy capability enhancements and related build stability improvements. Overall impact and accomplishments: - Accelerated release cycles through force deploy, improved scheduling reliability, and enhanced data integrity in CRM workflows. - Reduced runtime errors and improved observability with debug logging and improved error reporting. - Simplified and modernized configuration management by removing deprecated days_ahead usage and data_catalog references, while standardizing constants. Technologies/skills demonstrated: - Prefect workflow orchestration, Python scripting, and CI/CD integration. - YAML and configuration management, SQL-like query enhancements, and robust filtering logic (CPF/WhatsApp). - Observability, debugging primitives, and template-driven automation for scalable scheduling.

October 2025 monthly summary for prefeitura-rio/prefect_rj_iplanrio: Delivered end-to-end CRM data-pipeline enhancements and infrastructure modernization, driving data quality, performance, and deployment consistency. No critical user-facing bugs were reported; maintenance tasks focused on log cleanup and observability to support stability. Key work spanned CRM data model upgrades, processing enhancements, and storage/deployment modernization to BigLake/BigQuery with environment-aligned configurations. These efforts culminated in a scalable, reliable CRM workflow with streamlined deployments across dev/stage/prod. Technologies demonstrated include Python, Prefect, BigQuery/BigLake, GCS, dbt, and token-based access to private repos.