![]() Upload the file AWS-IAC-IAM-EC2-S3-Redshift.ipynb, and use it into your colab local env:.Go to -> Connect -> "Connect to local runtime" -> Paste the url copied from the last step and put it in Backend URL -> connect.After the execution of the last command, copy the localhost url, you will need it for colab.Install git-bash for windows, once installed, open git bash and download this repository, this will download the dags folder and the docker-compose.yaml file, and other files needed.Ĭ:\>jupyter notebook -NotebookApp.allow_origin='' -port=8888 -NotebookApp.port_retries=0.In addition to preparing the infrastructure, the file AWS-IAC-IAM-EC2-S3-Redshift.ipynb will help you to have an alternative staging zone in S3 as well.īelow we list the different steps and the things carried out in this file: The aim of this section is to create a Redshift cluster on AWS and keep it available for use by the airflow DAG. My proposed data model contains the expenses of both services separated in different fact tables, sharing dimensions between these facts, therefore, the proposed model will be a constellation scheme. Once the details for each type of receipt have been detected, it is easy to know what are the features, entities, and relations of the model. Take a look at the image below, both receipts belong to the details sent by Uber about Eats and Rides services, this will be our original data sources, In my case, I downloaded all those receipts from my email to my local computer. Keep reading this article, I will show you a quick and easy way to automate everything step by step.Įvery time an Ubers Eat or Uber Rides service has ended, you will receive a payment receipt to your email, this receipt contains the information about the details of the service, and is attached to the email with the extension. ![]() Have you ever thought about how much money you spend on these services? The goal of this project is to track the expenses of Uber Rides and Uber Eats through a data Engineering processes using technologies such as Apache Airflow, AWS Redshift and Power BI. ? Both phrases are very common in our daily lives, they represent the emblems of the two most important businesses with millionaire revenues from UBER. Have you heard phrases like Hungry? You're in the right place or Request a trip, hop in, and relax. Building an ETL pipeline with Apache Airflow and Visualizing AWS Redshift data using Microsoft Power BI Check the article here: Building an ETL data pipeline with Apache Airflow and Visualizing AWS Redshift data using Microsoft Power BI
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