
Geraldo Maia developed and enhanced data pipelines and analytics infrastructure for prefeitura-rio/queries-rj-iplanrio and prefeitura-rio/prefect_rj_iplanrio, focusing on data integrity, scalability, and maintainability. He implemented end-to-end ETL workflows using Python, SQL, and DBT, introducing robust data modeling and configuration management with YAML. Geraldo improved data ingestion and transformation processes, optimized SQL queries for urban licensing and HR analytics, and ensured consistent data types across YAML configurations. His work included Dockerized deployments, Prefect-based orchestration, and the creation of new data models and views, resulting in more reliable reporting, reduced manual intervention, and improved data quality for downstream analytics.
February 2026 monthly summary for Prefeitura Rio's data platform: Delivered a Data Pipeline and Views Overhaul for prefeitura-rio/prefect_rj_iplanrio, introducing new view vw_auditoria and renaming vw_RespostaOcorrencia_detalhada to vw_RespostaOcorrencia, with updated data pipeline configuration and refined Prefect SQL query execution to improve data retrieval and reliability. This work enhances data accessibility for reporting and analytics and lays groundwork for future data model evolution.
February 2026 monthly summary for Prefeitura Rio's data platform: Delivered a Data Pipeline and Views Overhaul for prefeitura-rio/prefect_rj_iplanrio, introducing new view vw_auditoria and renaming vw_RespostaOcorrencia_detalhada to vw_RespostaOcorrencia, with updated data pipeline configuration and refined Prefect SQL query execution to improve data retrieval and reliability. This work enhances data accessibility for reporting and analytics and lays groundwork for future data model evolution.
In 2026-01, delivered end-to-end data integration enhancements across prefeitura-rio/prefect_rj_iplanrio and prefeitura-rio/queries-rj-iplanrio, focusing on scalable data ingestion, robust modeling, and reliable deployment. The work enabled richer analytics for Osinfo RH and improved data quality, timeliness, and consistency for BigQuery analytics and DBT transformations. Highlights include a Prefect-based BigQuery data-dump pipeline with Dockerized deployment and new data sources; a DBT project initialized and tuned for osinfo_rh with materialization and naming conventions; category and municipality data models; process and date handling refinements; provisioning, column types, date/time adjustments, and FNOS-related refinements. These efforts improve data availability, accuracy, and governance, reducing manual intervention and speeding insights.
In 2026-01, delivered end-to-end data integration enhancements across prefeitura-rio/prefect_rj_iplanrio and prefeitura-rio/queries-rj-iplanrio, focusing on scalable data ingestion, robust modeling, and reliable deployment. The work enabled richer analytics for Osinfo RH and improved data quality, timeliness, and consistency for BigQuery analytics and DBT transformations. Highlights include a Prefect-based BigQuery data-dump pipeline with Dockerized deployment and new data sources; a DBT project initialized and tuned for osinfo_rh with materialization and naming conventions; category and municipality data models; process and date handling refinements; provisioning, column types, date/time adjustments, and FNOS-related refinements. These efforts improve data availability, accuracy, and governance, reducing manual intervention and speeding insights.
2025-11 Monthly Summary for prefeitura-rio/queries-rj-iplanrio: Focused on data integrity enhancements for urban licensing data; improved SQL queries and data tests; ensured data accuracy and reliability for downstream analytics and compliance reporting. All work logged with commit 3d9a8e1b243bcc50bf625d34a83d3a944b73a142 (ajuste fino do Projeto). No major bugs fixed this month in this repo. The month delivered solid business value by increasing data trust and maintainability of the licensing data pipeline. Technologies demonstrated include SQL optimization, data validation/testing, and version control best practices.
2025-11 Monthly Summary for prefeitura-rio/queries-rj-iplanrio: Focused on data integrity enhancements for urban licensing data; improved SQL queries and data tests; ensured data accuracy and reliability for downstream analytics and compliance reporting. All work logged with commit 3d9a8e1b243bcc50bf625d34a83d3a944b73a142 (ajuste fino do Projeto). No major bugs fixed this month in this repo. The month delivered solid business value by increasing data trust and maintainability of the licensing data pipeline. Technologies demonstrated include SQL optimization, data validation/testing, and version control best practices.
Concise monthly summary focusing on key accomplishments for 2025-10. Highlights delivered a data type consistency fix in YAML configuration for the SMFP Sigma System within prefeitura-rio/queries-rj-iplanrio, improving data integrity across dates, quantities, prices, and codes in both config files and models.
Concise monthly summary focusing on key accomplishments for 2025-10. Highlights delivered a data type consistency fix in YAML configuration for the SMFP Sigma System within prefeitura-rio/queries-rj-iplanrio, improving data integrity across dates, quantities, prices, and codes in both config files and models.

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