https://github.com/alanchn31/data-engineering-projects

Personal Data Engineering Projects
https://github.com/alanchn31/data-engineering-projects

airflow aws-redshift cassandra data-engineering data-engineering-nanodegree data-lake data-modeling data-warehouse ingest-data mongodb postgres scrapy spark star-schema

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Personal Data Engineering Projects

• Host: GitHub
• URL: https://github.com/alanchn31/data-engineering-projects
• Owner: alanchn31
• Created: 2020-04-20T10:47:33.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-02-08T00:44:31.000Z (almost 2 years ago)
• Last Synced: 2024-10-19T23:27:25.629Z (24 days ago)
• Topics: airflow, aws-redshift, cassandra, data-engineering, data-engineering-nanodegree, data-lake, data-modeling, data-warehouse, ingest-data, mongodb, postgres, scrapy, spark, star-schema
• Language: Jupyter Notebook
• Homepage:
• Size: 2.92 MB
• Stars: 843
• Watchers: 9
• Forks: 184
• Open Issues: 5
• Metadata Files:
  • Readme: README.md

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README

        
## Description

---

* This repo contains projects done which applies principles in data engineering. 

* Notes taken during the course can be found in folder `0. Back to Basics`

## Projects

---

1.  Postgres ETL  :heavy_check_mark:

* This project looks at data modelling for a fictitious music startup Sparkify, applying STAR schema to ingest data to simplify queries that answers business questions the product owner may have

2.  Cassandra ETL  :heavy_check_mark:

* Looking at the realm of big data, Cassandra helps to ingest large amounts of data in a NoSQL context. This project adopts a query centric approach in ingesting data into data tables in Cassandra, to answer business questions about a music app

3.  Web Scrapying using Scrapy, MongoDB ETL  :heavy_check_mark:

* In storing semi-structured data, one form to store it in, is in the form of documents. MongoDB makes this possible, with a specific collection containing related documents. Each document contains fields of data which can be queried. 

* In this project, data is scraped from a books listing website using Scrapy. The fields of each book, such as price of a book, ratings, whether it is available is stored in a document in the books collection in MongoDB.

4.  Data Warehousing with AWS Redshift  :heavy_check_mark:

* This project creates a data warehouse, in AWS Redshift. A data warehouse provides a reliable and consistent foundation for users to query and answer some business questions based on requirements.

5.  Data Lake with Spark & AWS S3  :heavy_check_mark:

* This project creates a data lake, in AWS S3 using Spark. 

* Why create a data lake? A data lake provides a reliable store for large amounts of data, from unstructured to semi-structured and even structured data. In this project, we ingest json files, denormalize them into fact and dimension tables and upload them into a AWS S3 data lake, in the form of parquet files.

6.  Data Pipelining with Airflow  :heavy_check_mark:

* This project schedules data pipelines, to perform ETL from json files in S3 to Redshift using Airflow. 

* Why use Airflow? Airflow allows workflows to be defined as code, they become more maintainable, versionable, testable, and collaborative

7.  Capstone Project  :heavy_check_mark:  

* This project is the finale to Udacity's data engineering nanodegree. Udacity provides a default dataset however I chose to embark on my own project. 

* My project is on building a movies data warehouse, which can be used to build a movies recommendation system, as well as predicting box-office earnings. View the project here: [Movies Data Warehouse](https://github.com/alanchn31/Udacity-Data-Engineering-Capstone)