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)
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
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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
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7. The reported CSV file is ready to use.
Jupyter vs Excel for Data Analysis
Jupyter | Excel |
Using jupyter will be command-based. Will take some time getting used to it. | Ease of Use with the Graphical User Interface (GUI). Learning formulas is fairly easier. |
Jupyter requires python language for data analysis hence a steeper learning curve. | Negligible previous knowledge is required. |
Equipped to handle lots of data really quickly. With the bonus of ease of accessibility to databases like Postgres and Mysql where actual data is stored. | Excel can only handle so much data. Scalability becomes difficult and messy. More Data = Slower Results |
Summary: Python is harder to learn because you have to download many packages and set the correct development environment on your computer. However, it provides a big leg up when working with big data and creating repeatable, automatable analyses, and in-depth visualizations. | Summary: Excel is best when doing small and one-time analyses or creating basic visualizations quickly. It is easy to become an intermediate user relatively without too much experience dueo its GUI. |
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)
Install Python and check to see if it installed correctly
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| Code Block |
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pip3 install notebook |
(Whenever you want to run Jupyter lab)
To run jupyter lab
| Code Block |
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jupyter notebook |
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Select that notebook (Ex: sample.pynb)
Opening an existing notebook
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After being opened
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Table 1.1 - File Names for each Module
Module Name | Script File Name (With Links) | Datamart CSV File Name | Datamart CSV File Name |
PT | ptDatamart.csv | ||
W&S | waterDatamart.csv | sewerageDatamart.csv | |
PGR | pgrDatamart.csv | ||
mCollect | mcollectDatamart.csv | ||
TL | tlDatamart.csv | tlrenewDatamart.csv | |
Fire Noc | fnDatamart.csv | ||
OBPS (Bpa) | bpaDatamart.csv |
Table 1.2 - Pod Names for each Module
Module Name | Pod Name | Description |
PT | playground-865db67c64-tfdrk | Punjab Prod Data in UAT Environment |
W&S | playground-584d866dcc-cr5zf | QA Data |
PGR | Local Data | Data Dump |
mCollect | playground-584d866dcc-cr5zf | QA Data |
TL | playground-584d866dcc-cr5zf | QA Data |
Fire Noc | playground-584d866dcc-cr5zf | QA Data |
OBPS (Bpa) | playground-584d866dcc-cr5zf | QA Data |
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