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rahul31agrawal/README.md

πŸ’» Data Engineer | Turning Data into Actionable Insights πŸš€

Hello there! πŸ‘‹
I’m a passionate Data Engineer with expertise in designing and implementing scalable data solutions. I work across multiple platforms and tools to transform raw data into actionable insights that drive decision-making and innovation.


πŸ› οΈ Tech Stack & Skills

Programming & Scripting

  • SQL: Proficient in writing complex queries, optimizing performance, and designing relational database structures.
    SQL
  • Python: Expertise in scripting, automation, and data manipulation using libraries like Pandas, NumPy, and Matplotlib.
    Python

Big Data & ETL Tools

  • Apache Spark: Skilled in distributed data processing, optimizing transformations, and handling massive datasets.
    Apache Spark
  • PySpark: Specialized in DataFrame operations, performance tuning, and advanced analytics on Spark.
    PySpark
  • Apache Airflow: Orchestrating complex workflows and ensuring robust ETL pipelines.
    Airflow

Cloud Platforms

  • Azure: Extensive experience in Azure Data Factory, Databricks, and cloud-based data engineering workflows.
    Azure
  • AWS: Building scalable and resilient architectures with AWS S3, Glue, and Lambda.
    AWS

Data Engineering & Storage

  • Databricks: Expertise in building and optimizing big data pipelines for real-time processing and analytics.
    Databricks
  • Data Warehouse: Proficient in designing and managing data warehouses for seamless querying and reporting.
    Data Warehouse

CI/CD & DevOps

  • Docker: Containerizing applications and ensuring consistency across environments.
    Docker
  • GitHub: Managing version control and collaborating effectively across teams.
    GitHub

Visualization & Analytics

  • Power BI: Designing interactive dashboards and reports to visualize complex datasets.
    Power BI
  • Microsoft Fabric: Exploring new frontiers in data visualization and analytics.
    Microsoft Fabric

🌟 What I Do

✨ End-to-End Data Engineering: Architecting and implementing robust data pipelines for ETL processes.
✨ Cloud Integration: Leveraging Azure and AWS for scalable and efficient data workflows.
✨ Big Data Solutions: Managing massive datasets and optimizing performance using Spark and PySpark.
✨ Data Visualization: Crafting insightful dashboards and reports with Power BI and Microsoft Fabric.


πŸš€ Current Focus Areas

  • πŸ—οΈ Mastering Microsoft Fabric and Data Mesh Architectures.
  • 🌐 Exploring multi-cloud integrations and hybrid architectures.
  • πŸ“ˆ Enhancing skills in Data Warehousing and BI Tools.

πŸ‘¨β€πŸ’» Let’s build the future of data together!

Pinned Loading

  1. llm_engineering llm_engineering Public

    Forked from ed-donner/llm_engineering

    Repo to accompany my mastering LLM engineering course

    Jupyter Notebook 1

  2. pyspark-zero-to-hero pyspark-zero-to-hero Public

    Forked from subhamkharwal/pyspark-zero-to-hero

    Learn PySpark from Basics to Advanced. Checkout the YouTube Series : [PySpark - Zero to Hero]

    Jupyter Notebook 1