prefeitura-rio/pipelines_rj_sms
Nov 2024 – Nov 2024
1 Month active
Languages Used
DockerfilePythonSQLShell
Technical Skills
Cloud StorageData EngineeringDatabase IntegrationDatabase ManagementDevOpsETL
Thiago Felipe
PROFILE
Thiago Felipe
Thiago Felipe overhauled the VitaCare data ingestion pipeline for the prefeitura-rio/pipelines_rj_sms repository, focusing on production-grade reliability and scalability. He engineered an end-to-end data flow that creates a temporary MSSQL database from backup, executes extraction queries, generates parquet files, and uploads them to a cloud data lake. Using Python, SQL, and Docker, Thiago strengthened backup and blob handling, automated CI/CD with GitHub Actions, and resolved race conditions in SQL execution. His work improved data quality, timeliness, and reproducibility, enabling faster data-driven decisions and easier maintenance, and established a robust foundation for ongoing data engineering and orchestration efforts.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
1
Lines of code
430
Activity Months1
Your Network
18 people
Work History
November 2024
8 Commits • 1 Features
Nov 1, 2024Monthly summary for 2024-11: On prefeitura-rio/pipelines_rj_sms, delivered a robust VitaCare data ingestion overhaul and prepared the pipeline for production-grade reliability and scale. Implemented end-to-end data flow: create temporary DB from backup, run extraction queries, generate parquet files, and upload to the data lake. Strengthened backup handling, blob processing, and CI/CD automation. Set up MSSQL environment in Docker for consistent local/CI runs and addressed race conditions in SQL execution. These efforts improved data timeliness, quality, and reproducibility, enabling faster decision-making and easier maintenance.
8 Commits • 1 Features
Nov 1, 2024Monthly summary for 2024-11: On prefeitura-rio/pipelines_rj_sms, delivered a robust VitaCare data ingestion overhaul and prepared the pipeline for production-grade reliability and scale. Implemented end-to-end data flow: create temporary DB from backup, run extraction queries, generate parquet files, and upload to the data lake. Strengthened backup handling, blob processing, and CI/CD automation. Set up MSSQL environment in Docker for consistent local/CI runs and addressed race conditions in SQL execution. These efforts improved data timeliness, quality, and reproducibility, enabling faster decision-making and easier maintenance.
November 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture70.0%
Performance67.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
DockerfilePythonSQLShell
Technical Skills
Cloud StorageData EngineeringDatabase IntegrationDatabase ManagementDevOpsETLError HandlingFile HandlingOrchestrationPrefectPython ScriptingSQL
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