• Allows curious individuals to easily access their sleep data and store them in a database for further statistical analysis.
• Simplifies and automate the ETL processes for sleep researchers to collect their subjects' sleep data into a database for further statistical analysis.

• Fully automated once the appropriate credentials are given.
• Can be used over multiple user accounts.

If you don't already have anaconda environment manager installed on your machine, install environment manager anaconda3 from https://www.anaconda.com/distribution/#download-section .

Or, if you already have Anaconda Environment Manager follow the steps below.

1. Create an environment that will be configured for running the ETL application.

conda create -n fitbitETL python=3.6

2. Activate the new environment.

conda activate fitbitETL

3. Download the application package.

git clone https://github.com/sehokim88/fitbit-etl.git 

4. Change directory into the package's root.

cd fitbit-etl 

5. Install the required python pacakges from anaconda distribution.

conda install --file requirements.txt

6. Fill in the credentials for your registered Fitbit Application and target database into var/config.yaml. If you don't already have one registered, register one from https://dev.fitbit.com/apps/new. If you don't already have AWS RDS instance established for your database, do so from https://console.aws.amazon.com/rds/home.

app:
   client_id: <your client id>
   client_secret: <your client secrete>
   
db:
   host: <your DB host DNS>
   port: <your DB host Port>
   database: <your DB name>
   user_id: <your DB user_id>
   password: <your DB password>
   

7. Fill in one or more Fitbit user information into var/users.yaml.

user1:
   name: <user's name>
   bod: <user's bod in "%Y-%m-%d">
   gender: <user's gender in string>
   country: <user's coutry of residence in string> 
   accountID: <user's Fitbit account ID>
   password: <user's Fitbit accound password>
   
user2:
   name: <user's name>
   bod: <user's bod in "%Y-%m-%d">
   gender: <user's gender in string>
   country: <user's coutry of residence in string> 
   accountID: <user's Fitbit account ID>
   password: <user's Fitbit accound password>
   
user3:
   ...

9. Automate login process over all the user accoutns by running login.py. Once authenticated, there is no need to run this every time you want to extract data.

python login.py 

10. Extract, transform, and load your sleep data into your AWS RDS database by executing sleep_ETL.py.

python FETL.py