Run from database folder:

mysql --host=localhost --user=xxx --password=xxx 

and in mysql run

source employees.sql

confirm the employee database creation in MyQSL.

Besides this one, there should be a database called dwh (Run from dwh folder):

mysql --host=localhost --user=xxx --password=xxx 

and in mysql run

source salaries.sql

Install (git clone) this repo

• https://github.com/nunobbras/airflow_for_bi

Create containers building the images at the same time (see Dockerfile)

docker-compose -f docker-compose-SequentialExecutor.yml up -d

and manage them (restart and delete them) if needed

docker-compose -f docker-compose-SequentialExecutor.yml restart

docker-compose -f docker-compose-SequentialExecutor.yml down

Run and close a container bash (good to interact once with the airflow)

docker-compose -f docker-compose-SequentialExecutor.yml run --rm webserver bash

Check existent containers

docker container ls

Run in a specific container bash

docker exec -it airflow_for_bi_webserver_1 bash

Note: check the name of the generated components: they could be different than airflow_for_bi_XXX

Test a specific airflow function in docker

docker exec -it airflow_for_bi_webserver_1 airflow list_dags

Execute through bash interactively a test (this will actually work!)

docker exec -it airflow_for_bi_webserver_1 bash
cd dags
airflow test init_configuration first_task 2015-06-01

execute remotely the same test

docker exec -it airflow_for_bi_webserver_1 airflow test init_configuration first_task 2015-06-01

Create a Run manually (it could be done in the DAG webPage) This is the same DAG as before (init_configuration) but now we are running al the tasks, which includes second_task, that makes a airflow configuration (see the code of init_configuration.py)

docker exec -it airflow_for_bi_webserver_1 airflow trigger_dag init_configuration

Now you can see it ready to work to be fired in web server. We now have to run airflow scheduler to run available

docker exec -it airflow_for_bi_webserver_1 airflow scheduler

Go to Data Profiling and use Ad Hoc Query. Make a query you know it returns values.

testing version

docker exec -it airflow_for_bi_webserver_1 airflow test get_salaries extract_salaries 1999-12-30

docker cp ./airflow/config/airflow.cfg airflow_for_bi_webserver_1:/usr/local/airflow

Use the dag in file process_salaries and don't forget to create a similar table employees in dwh database;

docker exec -it airflow_for_bi_webserver_1 airflow test process_salaries extract_salaries 1999-1-30

Let's use the DAG inject_salaries to generate data automatically in salaries table;

For that to work you can start by testing it with

docker exec -it airflow_for_bi_webserver_1 airflow test inject_salaries inject_salaries_operator 2015-01-01

This should generate an extra set of 10 rows of salaries for random employees for the month 2015-01.

You should now be able to start the scheduler and activate this task. Make it work 2 times per day; Also, run previous dates (1 year) using airflow backfill, starting at 2017, using

docker exec -it airflow_for_bi_webserver_1 airflow backfill inject_salaries -s 2017-05-01 -e 2018-05-01

Given this we should now import from OLTP to dwh from time to time. For that we have to build a DAG that import data from one place to another. For now we are building a simple scheduled DAG that will push data to the same dwh.

For that you should run twice per day the DAG process_salaries. Start by testing it with.

docker exec -it airflow_for_bi_webserver_1 airflow test process_salaries process_salaries_operator 1/5/2001

You can see now you have new data in the dwh.salaries database;

(included in the docker-compose file)

make

pip install superset
pip install flask-appbuilder
pip install ipython
pip install mysqlclient
pip install cryptography

docker exec -it airflow_for_bi_superset_1 superset-init

if not using docker container make

fabmanager create-admin --app superset

superset db upgrade

superset load_examples

superset init

To start a development web server on port 8088, use -p to bind to another port

superset runserver -d

Create a connection to dwh in Sources > Databases

To connect to your local mysql use in SQLAlchemy URI field the following path

mysql://[mysqluser]:[mysqlpassword]@host.docker.internal:3306/dwh

OK! You can now test the connection and move on