https://github.com/nareshk1290/udacity-data-engineering

Udacity Data Engineering Nano Degree (DEND)
https://github.com/nareshk1290/udacity-data-engineering

airflow aws cassandra etl postgresql redshift s3 spark star-schema udacity-dend

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Udacity Data Engineering Nano Degree (DEND)

• Host: GitHub
• URL: https://github.com/nareshk1290/udacity-data-engineering
• Owner: nareshk1290
• Created: 2019-04-16T03:05:07.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-01-20T22:24:18.000Z (over 4 years ago)
• Last Synced: 2024-09-24T21:55:19.051Z (about 12 hours ago)
• Topics: airflow, aws, cassandra, etl, postgresql, redshift, s3, spark, star-schema, udacity-dend
• Language: Jupyter Notebook
• Size: 1.73 MB
• Stars: 183
• Watchers: 6
• Forks: 168
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# Data Engineering Nanodegree

Projects and resources developed in the [DEND Nanodegree](https://www.udacity.com/course/data-engineer-nanodegree--nd027) from Udacity.

## Project 1: [Relational Databases - Data Modeling with PostgreSQL](https://github.com/nareshk1290/Udacity-Data-Engineering/tree/master/Data-Modeling/Project%201).

Developed a relational database using PostgreSQL to model user activity data for a music streaming app. Skills include:

* Created a relational database using PostgreSQL

* Developed a Star Schema database using optimized definitions of Fact and Dimension tables. Normalization of tables.

* Built out an ETL pipeline to optimize queries in order to understand what songs users listen to.

Proficiencies include: Python, PostgreSql, Star Schema, ETL pipelines, Normalization

## Project 2: [NoSQL Databases - Data Modeling with Apache Cassandra](https://github.com/nareshk1290/Udacity-Data-Engineering/tree/master/Data-Modeling/Project%202).

Designed a NoSQL database using Apache Cassandra based on the original schema outlined in project one. Skills include:

* Created a nosql database using Apache Cassandra (both locally and with docker containers)

* Developed denormalized tables optimized for a specific set queries and business needs

Proficiencies used: Python, Apache Cassandra, Denormalization

## Project 3: [Data Warehouse - Amazon Redshift](https://github.com/nareshk1290/Udacity-Data-Engineering/tree/master/Cloud%20Data%20Warehouse/Project%20Data%20Warehouse%20with%20AWS).

Created a database warehouse utilizing Amazon Redshift. Skills include:

* Creating a Redshift Cluster, IAM Roles, Security groups.

* Develop an ETL Pipeline that copies data from S3 buckets into staging tables to be processed into a star schema

* Developed a star schema with optimization to specific queries required by the data analytics team.

Proficiencies used: Python, Amazon Redshift, aws cli, Amazon SDK, SQL, PostgreSQL

## Project 4: [Data Lake - Spark](https://github.com/nareshk1290/Udacity-Data-Engineering/tree/master/Data%20Lakes%20with%20Spark/Project%20Data%20Lake%20with%20Spark)

Scaled up the current ETL pipeline by moving the data warehouse to a data lake. Skills include:

* Create an EMR Hadoop Cluster

* Further develop the ETL Pipeline copying datasets from S3 buckets, data processing using Spark and writing to S3 buckets using efficient partitioning and parquet formatting.

* Fast-tracking the data lake buildout using (serverless) AWS Lambda and cataloging tables with AWS Glue Crawler.

Technologies used: Spark, S3, EMR, Athena, Amazon Glue, Parquet.

## Project 5: [Data Pipelines - Airflow](https://github.com/nareshk1290/Udacity-Data-Engineering/tree/master/Data%20Pipeline%20with%20Airflow/Project%20Data%20Pipeline%20with%20Airflow)

Automate the ETL pipeline and creation of data warehouse using Apache Airflow. Skills include:

* Using Airflow to automate ETL pipelines using Airflow, Python, Amazon Redshift.

* Writing custom operators to perform tasks such as staging data, filling the data warehouse, and validation through data quality checks.

* Transforming data from various sources into a star schema optimized for the analytics team's use cases.

Technologies used: Apache Airflow, S3, Amazon Redshift, Python.