https://github.com/cschan1828/data-pipelines-with-airflow

https://github.com/cschan1828/data-pipelines-with-airflow

airflow

Last synced: 15 days ago
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• Host: GitHub
• URL: https://github.com/cschan1828/data-pipelines-with-airflow
• Owner: cschan1828
• Created: 2020-03-26T17:15:58.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-03-26T17:19:33.000Z (almost 5 years ago)
• Last Synced: 2024-11-29T02:40:57.321Z (about 2 months ago)
• Topics: airflow
• Language: Python
• Size: 21.5 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Data Pipelines with Airflow

## Introduction

A music streaming company, Sparkify, has decided that it is time to introduce more automation and monitoring to their data warehouse ETL pipelines and come to the conclusion that the best tool to achieve this is Apache Airflow.

The source data resides in S3 and needs to be processed in Sparkify's data warehouse in Amazon Redshift. The source datasets consist of JSON logs that tell about user activity in the application and JSON metadata about the songs the users listen to.

## Tasks

Build an Airflow dag to stage data, fill the data to the warehouse and run quality check. Such tasks dependencies need to be well-handled.

### Building the Operators

Need to build four different operators that will stage the data, transform the data, and run checks on data quality. All of the operators and task instances will run SQL statements against the Redshift database. However, using parameters wisely will allow you to build flexible, reusable, and configurable operators you can later apply to many kinds of data pipelines with Redshift and with other databases.

### Stage Operator

The stage operator is expected to be able to load any JSON and CSV formatted files from S3 to Amazon Redshift. The operator creates and runs a SQL COPY statement based on the parameters provided. The operator's parameters should specify where in S3 the file is loaded and what is the target table.

The parameters should be used to distinguish between JSON and CSV file. Another important requirement of the stage operator is containing a templated field that allows it to load timestamped files from S3 based on the execution time and run backfills.

### Fact and Dimension Operators

Provided SQL Helper class will help to run data transformations. Most of the logic is within the SQL transformations and the operator is expected to take as input a SQL statement and target database on which to run the query against. Dimension loads are often done with the truncate-insert pattern where the target table is emptied before the load. Fact tables are usually so massive that they should only allow append type functionality.

### Data Quality Operator

The final operator to create is the data quality operator, which is used to run checks on the data itself. The operator's main functionality is to receive one or more SQL based test cases along with the expected results and execute the tests. For each the test, the test result and expected result needs to be checked and if there is no match, the operator should raise an exception and the task should retry and fail eventually.

For example one test could be a SQL statement that checks if certain column contains NULL values by counting all the rows that have NULL in the column. We do not want to have any NULLs so expected result would be 0 and the test would compare the SQL statement's outcome to the expected result.