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.
For this project, you'll be working with two datasets. Here are the s3 links for each:
Log data: s3://udacity-dend/log_data
Song data: s3://udacity-dend/song_data
The stage operator is expected to be able to load any JSON 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 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.
With dimension and fact operators, you can utilize the provided SQL helper class 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. You can also define a target table that will contain the results of the transformation.
Dimension loads are often done with the truncate-insert pattern where the target table is emptied before the load. Thus, you could also have a parameter that allows switching between insert modes when loading dimensions. Fact tables are usually so massive that they should only allow append type functionality.
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.
Start Airflow in Docker Compose, see more info here.
Before starting Airflow for the first time, You need to prepare your environment, i.e. create the necessary files, directories and initialize the database.
Run:
docker-compose up airflow-init
Now you can start all services:
docker-compose up
The webserver available at: http://localhost:8080. The default account has the login airflow and the password airflow.
To stop and delete containers, delete volumes with database data and download images, run:
docker-compose down --volumes --rmi alls