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gborn/README.md

Hi there, I'm Glad Nayak๐Ÿ‘‹

  • ๐Ÿ“ˆ 5+ years of working experience in building scalable data-pipeline for ingesting and analyzing large scale datasets using Big Data framework.
  • ๐ŸŒฑ I'm on track for learning more about Data Engineering, Systems Design, Cloud Architecture and Machine Learning Pipelines.
  • ๐ŸŽฏ I am keen to develop products that impact on a large scale. ๐™„ ๐™–๐™ข ๐™˜๐™ช๐™ง๐™ง๐™š๐™ฃ๐™ฉ๐™ก๐™ฎ ๐™ก๐™ค๐™ค๐™ ๐™ž๐™ฃ๐™œ ๐™›๐™ค๐™ง ๐™๐™ช๐™ก๐™ก-๐™๐™ž๐™ข๐™š ๐™Ÿ๐™ค๐™— ๐™ค๐™ฅ๐™ฅ๐™ค๐™ง๐™ฉ๐™ช๐™ฃ๐™ž๐™ฉ๐™ž๐™š๐™จ ๐™–๐™ฃ๐™™ ๐™„ ๐™–๐™ข ๐™ž๐™ฃ๐™ฉ๐™š๐™ง๐™š๐™จ๐™ฉ๐™š๐™™ ๐™ž๐™ฃ ๐˜ฟ๐™–๐™ฉ๐™– ๐™€๐™ฃ๐™œ๐™ž๐™ฃ๐™š๐™š๐™ง๐™ž๐™ฃ๐™œ ๐™–๐™ฃ๐™™ ๐˜ฟ๐™–๐™ฉ๐™– ๐™Ž๐™˜๐™ž๐™š๐™ฃ๐™˜๐™š ๐™ง๐™ค๐™ก๐™š๐™จ
  • โœ๏ธ In my free time, I go out to explore new places or stay in to write journals.
  • ๐Ÿ’ฌ Feel free to reach out to me for pro bono consulting and volunteering, or just for some interesting discussion.
  • ๐Ÿ“ซ How to reach me: by mail, or LinkedIn
  • ๐Ÿ˜„ Pronouns: He/Him

๐Ÿ›  Tech Stack

  • โ–บ Programming Languages: Python, PySpark, SQL, shell-scripting.
  • โ–บ Databases: MySQL, PostgreSQL, MongoDB (NoSQL), Cassandra, Amazon Redshift.
  • โ–บ Machine Learning: Classification, Regression, Clustering, Neural Networks, Ensemble Learning, Forecasting, Dimension Reduction, Predictive modeling, CNN, GAN.
  • โ–บ Libraries: Asyncio, Pandas, Numpy, Scikit-learn, NLTK, Requests, Matplotlib, Tensorflow, Keras, Beautiful Soup, Selenium.
  • โ–บ Framework/Tools: Apache Spark, Spark Streaming, Hadoop, HBase, Hive, Nifi, Apache Kafka, Apache Airflow, Tableau, Git, GitHub, Bash, MS Excel, IntelliJ, Jupyter Notebooks, Flask, FASTAPI.
  • โ–บ DevOps Tools: Docker, Kubernetes.
  • โ–บ Cloud Technologies: Amazon Web Service (AWS), Google Cloud Platform (GCP).

Pinned Loading

  1. Real-Time-Streaming-Data-Pipeline Real-Time-Streaming-Data-Pipeline Public

    Real-Time Data streaming Pipeline to process and transform live events using Kafka, Spark Streaming, Hadoop, PostgresSQL on Docker. Live event metrics were monitored using a dashboard built with Djโ€ฆ

    Python 2 1

  2. Serverless-ETL-Pipeline-on-AWS Serverless-ETL-Pipeline-on-AWS Public

    Design of an ETL Pipeline to process and transform incrementally loaded data in datalake using AWS Lambda, Glue Jobs, EMR, and Athena.

    Python 3 1

  3. Semantic-Search-Engine-Using-ElasticSearch Semantic-Search-Engine-Using-ElasticSearch Public

    A semantic search engine to find questions semantically similar to given query using Elastic Search, Tensorflow, Universal Sentence Encoder, AWS Lambda, API Gateway

    Jupyter Notebook 5 1

  4. Generating-Multiple-Choice-Questions-From-Any-Text Generating-Multiple-Choice-Questions-From-Any-Text Public

    A webapp to generate multiple choice questions (MCQs) from any Text using Google T5 model fined tuned on SQUAD dataset to generate questions, and BERT to generate distractors.

    Jupyter Notebook 1

  5. Knowledge-Based-Recommendation-of-Food-Ingredients Knowledge-Based-Recommendation-of-Food-Ingredients Public

    Project to build a knowledge graph of all food ingredients and use it to recommend similar ingredients and recipes

    HTML 1

  6. ETL-Pipeline-From-Scratch ETL-Pipeline-From-Scratch Public

    An ETL pipeline to efficiently extract, transform and load data into PostgresSQL table

    Python 1