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About me 👋

Google Developer Expert on Google Cloud.

Passionate about tech and Data, Craftsman at heart and functional programming lover, I work on long missions or specialized expertise with many customers.

Tech Lead Full-Stack in the past, I set up architectures (Hexagonal, Domain Driven Design) and helped teams to improve their code quality and propose best practices.

Today Lead and Architect Data on Google Cloud, I evangelize this platform while ensuring continuous improvement and expertise. I explore issues related to Data, Infra As Code, DevOps automation (CI/CD) and best practices.

I am also very active in the Google Cloud community:

Co-founder and Head of Data & Cloud at GroupBees. Groupbees is an IT freelancers group created to give freelancers access to a proven framework and a powerful community based on knowledge sharing in order to upskill them as well as give them better employability:

  • Promote the GroupBees label by helping to build a great community of freelancers: good mindset and very good technical skills of our members
  • Animation of the GroupBees Tech community with side projects and organization of monthly BShare sharing talks: https://www.youtube.com/channel/UCARW8PvH1T0yAkTiPbGLeiA
  • Delivering Customer Value with recognized expertise on Google Cloud Data ecosystem

🔗 Let's Connect!

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Mazlum Tosun's Projects

teams-league-airflow-elt icon teams-league-airflow-elt

The goal of this article is showing a real world use case for ELT batch pipeline, with Cloud Storage, BigQuery, Apache Airflow and Cloud Composer : The Extract part is managed in Cloud Storage The Load part is managed from Cloud Storage to BigQuery The Transform part is managed by a BigQuery SQL query Everything is orchestrated by Airflow

teams-league-airflow-spark-scala-etl icon teams-league-airflow-spark-scala-etl

This project shows a real world use case with ETL batch pipeline using Cloud Storage, Scala Spark with Dataproc Serverless and BigQuery orchestrated by Cloud Composer / Airflow

teams-league-cloudrun-service-fastapi icon teams-league-cloudrun-service-fastapi

Project showing a complete use case with a Cloud Run Service written with a Python module and multiple files. The deployment of service is done with FastApi and Uvicorn.

teams-league-java-standard-beam icon teams-league-java-standard-beam

This video present a real world use case developed with Apache Beam Java and launched with the serverless Dataflow runner in Google Cloud Platform. The job read a Json file from Cloud Storage, applies some transformations and write the result to a BigQuery table.

teams-league-python-standard-beam icon teams-league-python-standard-beam

This video present a real world use case developed with Apache Beam Python and launched with the serverless Dataflow runner in Google Cloud Platform. The job read a Json file from Cloud Storage, applies some transformations and write the result to a BigQuery table.

world-cup-qatar-team-stats-java icon world-cup-qatar-team-stats-java

This application shows a full Apache Beam pipeline with Java. The use case works on the last Qatar FIFA world cup data and calculate players statistics per team. This application will be presented at Beam Summit 2023 in New York. A Kotlin version of this use case is also used in another repo.

world-cup-qatar-team-stats-kotlin-midgard icon world-cup-qatar-team-stats-kotlin-midgard

This application shows a full Apache Beam pipeline with Kotlin and Midgard library. The use case works on the last Qatar FIFA world cup data and calculate players statistics per team. This application will be presented at Beam Summit 2023 in New York

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