DIGIT: Internal Datamart deployment steps

Steps for setting up the environment and running the script file to get a fresh copy of the required Datamart CSV file.

Steps for setting up the environment for running the script 

(One Time Setup)

1. Install Kubectl
   Step 1: Go through the Kubernetes documentation page to install and configure the kubectl. Following are useful links:
   Kubernetes Installation Doc
   Kubernetes Ubuntu Installation

After installing type the below command to check the version install in your system

 kubectl version

Step 2: Install aws-iam-authenticator
https://docs.aws.amazon.com/eks/latest/userguide/install-aws-iam-authenticator.html

Step 3: After installing, you need access to a particular environment cluster.

• Go to $HOME/.Kube folder 

cd
cd .kube

Open the config file and replace the content with the environment cluster config file. (Config file will be attached)

gedit config

• Copy-paste the content from the config file provided to this config file opened and save the file.

2. Exec into the pod

kubectl exec --stdin --tty playground-584d866dcc-cr5zf -n playground -- /bin/bash

(Replace the pod name depending on what data you want.

Refer to Table 1.2 for more information)

3. Install Python and check to see if it installed correctly

apt install python3.8
python --version

4. Install pip and check to see if it installed correctly

apt install python3-pip
pip3 --version

5. Install psycopg2 and Pandas

pip3 install psycopg2-binary pandas

Note: If this doesn’t work then try this command

pip3 install --upgrade pip 

and running the #5 command again

Steps for setting up the environment for running the script 

(Every time you want a datamart with the latest data available in the pods)

1. Sending the python script to the pod

tar cf - /home/priyanka/Desktop/mcollect.py | kubectl exec -i -n playground playground-584d866dcc-cr5zf -- tar xf - -C /tmp

Note: Replace the file path (/home/priyanka/Desktop/mcollect.py) with your own file path (/home/user_name/Desktop/script_name.py)

Note: Replace the pod name depending on what data you want.  

(Refer to Table 1.2 for more information on pod names)

2. Exec into the pod

kubectl exec --stdin --tty playground-584d866dcc-cr5zf -n playground -- /bin/bash

(Note: Replace the pod name depending on what data you want.

kubectl exec --stdin --tty <your_pod_name> -n playground -- /bin/bash

 Refer to Table 1.2 for more information)

3. Move into tmp directory and then move into the directory your script was in

cd tmp
cd home/priyanka/Desktop 

for example :

cd home/<your_username>/Desktop 

4. List the files there

ls

(Python script file should be present here)

(Refer Table 1.1 for the list of script file names for each module)

5. Run the python script file

python3 ws.py

(name of the python script file will change depending on the module)

(Refer Table 1.1 for the list of script file names for each module)

6. Outside the pod shell, In your home directory run this command to copy the CSV file/files to your desired location

kubectl cp playground/playground-584d866dcc-cr5zf:/tmp/mcollectDatamart.csv /home/priyanka/Desktop/mcollectDatamart.csv

(The list of CSV file names for each module will be mentioned below)

7. The reported CSV file is ready to use.

Jupyter vs Excel for Data Analysis

How to install and configure jupyter to analyze the datamart

Watch this video

https://www.youtube.com/watch?v=Yg9AkozItTU

OR

Follow these steps ->

(One Time Setup)

1. Install Python and check to see if it installed correctly

apt install python3.8
python --version

2. Install pip and check to see if it installed correctly

apt install python3-pip
pip3 --version

3. Install jupyter

pip3 install notebook

(Whenever you want to run Jupyter lab)

1. To run jupyter lab

jupyter notebook

2. To open a new notebook

New -> Python3 notebook

3. To open an existing notebook

Select File -> Open

Go to the directory where your sample notebook is.

Select that notebook (Ex: sample.pynb)

Opening an existing notebook

After being opened

Table 1.1 - File Names for each Module

Table 1.2 - Pod Names for each Module