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Achievement: YOLOAchievement: Pull Shark

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Achievement: YOLOAchievement: Pull Shark

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BeyzaNurSarikaya/README.md

Hi there, I'm Beyza 👋

I am an Industrial Engineer transitioning into the world of Data Engineering & AI. I combine my background in process optimization with modern data stack technologies to build scalable, automated, and efficient data solutions.

Currently, I am focused on building end-to-end ETL pipelines, cloud data warehousing, and AI-driven analytics.

🛠️ Tech Stack & Learning Path

Category Tools & Technologies
Data Engineering Apache Airflow, Docker, Microsoft Fabric, Snowflake, ETL Pipelines
Databases MySQL, PostgreSQL, MongoDB, OneLake
Data Science & AI Python (Pandas, NumPy), Machine Learning, Deep Learning, GenAI
Data Analysis SQL, Power BI, Streamlit, Data Modeling

🤖 Work Areas

DataScience MachineLearning DeepLearning DataEngineering

🚀 🌟 I'm always eager to learn new things and collaborate on exciting projects. Feel free to check out my repositories and connect with me!

📫 Let's Connect!

LinkedIn Kaggle

⚡ Fun Fact

As an Industrial Engineer, I don't just process data; I optimize the way data is processed! 🚀

Pinned Loading

  1. Smart_City_Microsoft_Fabric_Data_Engineering Smart_City_Microsoft_Fabric_Data_Engineering Public

    "Transforming raw urban datasets into actionable insights through Microsoft Fabric. This project showcases cloud-native data orchestration and modern storage strategies within the OneLake environme…

    Jupyter Notebook

  2. energyzero-etl energyzero-etl Public

    "An end-to-end ETL pipeline fetching real-time energy prices from EnergyZero API, processed with Pandas, and orchestrated using Dockerized Apache Airflow."

    Python

  3. MySQL_Snowflake_MangoDB_Data_Engineering MySQL_Snowflake_MangoDB_Data_Engineering Public

    Forked from werhereitacademy/DataEngineering_Module_Week_6

    "An end-to-end data engineering project migrating the Sakila dataset across MySQL (Relational), Snowflake (Cloud Data Warehouse), and MongoDB (NoSQL). Includes ERD/Star Schema design, ETL workflows…

  4. amazon_sales_analysis_spark_s3 amazon_sales_analysis_spark_s3 Public

    "Big Data Engineering project using PySpark and AWS S3 to process 128k+ Amazon sales records. Features include SQL analytics, Structured Streaming, Parquet optimization, and data visualization."

    Jupyter Notebook 2

  5. traffic-accident-analytics traffic-accident-analytics Public

    🚦 Real-time traffic accident analytics system using Apache Spark. Features end-to-end big data pipeline: data cleaning, Spark SQL, Parquet benchmarking, and Structured Streaming for live safety ins…

    Jupyter Notebook