2026-09-04 –, Auditorium
In this talk about Data Quality, I will explore its main concepts, technologies, and strategies, besides presenting a benchmark of Python solutions and a use case applying Airflow to execute Data Quality validations
Data Quality is growing in importance and visibility as companies understand the strategic value of it inside the Data Governance field. In this talk, I will explore the impact of implementing Data Quality routines in data pipelines and the available techniques and technologies, applying Python to do so. This lecture will be divided into four sessions, starting with the presentation of Data Quality's main concepts, then presenting three use cases, a benchmark of data quality tools, and, finally, a use case using Airflow to implement data quality validations.
Structure planned:
- Presenting myself (2 minutes)
- Why Data Quality is so relevant? (3 minutes)
- Data Quality main concepts (3 minutes)
- Three use cases of Data Quality (5 minutes)
- A benchmark for Python tools (7 minutes)
- A use case with Airflow (7 minutes)
- Questions (3 minutes)
Data Quality, Data Governance
I have more than 10 years of experience working on data projects, with prior experience in roles such as Data Engineer, Data Scientist, Data Architect, and Data Governance.
I had the honor of being a speaker at events such as GopherCon Brasil and Python Brasil, talking about topics related to data.
Currently, I work on a Data Governance project, implementing Data Quality routines.