alanchn31/Data-Engineering-Projects

Description

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• 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

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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

1. 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

1. 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.

1. 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.

1. 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.

1. 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

1. 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