This is a collection of Python jobs that are supposed to transform data. These jobs are using PySpark to process larger volumes of data and are supposed to run on a Spark cluster (via spark-submit).

We use batect to dockerise the tasks in this exercise. batect is a lightweight wrapper around Docker that helps to ensure tasks run consistently (across linux, mac windows). With batect, the only dependencies that need to be installed are Docker and Java >=8. Every other dependency is managed inside Docker containers. If docker desktop can't be installed then Colima could be used on Mac and Linux.

For Windows, docker desktop is the only option for using container to run application otherwise local laptop should be set up.

Please make sure you have the following installed and can run them

• Docker Desktop or Colima
• Java (11)

You could use following instructions as guidelines to install Docker or Colima and Java.

# Install pre-requisites needed by batect 
# For mac users: 
./go.sh install-with-docker-desktop
OR
./go.sh install-with-colima

# For windows/linux users:
# Please ensure Docker and java >=8 is installed 
scripts\install_choco.ps1
scripts\install.bat

# For local laptop setup ensure that Java 11 with Spark 3.2.1 is available. More details in README-LOCAL.md

If you are using Colima, please ensure that you start Colima. For staring Colima, you could use following command:

./go.sh start-colima

**Please install poetry if you would like to use lint command. Instructions to install poetry in README-LOCAL **

General pattern apart from installation and starting of Colima is:

./go.sh run-<type>-<action>

type could be local, colima or docker-desktop

action could be unit-test, integration-test or job.

Full list of commands for Mac and Linux users is as follows:

Full list of commands for Windows users is as follows:

There are two applications in this repo: Word Count, and Citibike.

Currently, these exist as skeletons, and have some initial test cases which are defined but ignored. For each application, please un-ignore the tests and implement the missing logic.

A NLP model is dependent on a specific input file. This job is supposed to preprocess a given text file to produce this input file for the NLP model (feature engineering). This job will count the occurrences of a word within the given text file (corpus).

There is a dump of the datalake for this under resources/word_count/words.txt with a text file.

Simple *.txt file containing text.

A single *.csv file containing data similar to:

"word","count"
"a","3"
"an","5"
...

JOB=wordcount ./go.sh run-docker-desktop-job 

$env:JOB = wordcount 
.\go.ps1 run-docker-desktop-job 

JOB=wordcount ./go.sh run-colima-job 

This problem uses data made publicly available by Citibike, a New York based bike share company.

For analytics purposes, the BI department of a hypothetical bike share company would like to present dashboards, displaying the distance each bike was driven. There is a *.csv file that contains historical data of previous bike rides. This input file needs to be processed in multiple steps. There is a pipeline running these jobs.

There is a dump of the datalake for this under resources/citibike/citibike.csv with historical data.

Reads a *.csv file and transforms it to parquet format. The column names will be sanitized (whitespaces replaced).

Historical bike ride *.csv file:

"tripduration","starttime","stoptime","start station id","start station name","start station latitude",...
364,"2017-07-01 00:00:00","2017-07-01 00:06:05",539,"Metropolitan Ave & Bedford Ave",40.71534825,...
...

*.parquet files containing the same content

"tripduration","starttime","stoptime","start_station_id","start_station_name","start_station_latitude",...
364,"2017-07-01 00:00:00","2017-07-01 00:06:05",539,"Metropolitan Ave & Bedford Ave",40.71534825,...
...

JOB=citibike_ingest ./go.sh run-docker-desktop-job

$env:JOB = citibike_ingest
.\go.ps1 run-docker-desktop-job

JOB=citibike_ingest ./go.sh run-colima-job

This job takes bike trip information and calculates the "as the crow flies" distance traveled for each trip. It reads the previously ingested data parquet files.

Hint:

• For distance calculation, consider using Harvesine formula as an option.

Historical bike ride *.parquet files

"tripduration",...
364,...
...

*.parquet files containing historical data with distance column containing the calculated distance.

"tripduration",...,"distance"
364,...,1.34
...

JOB=citibike_distance_calculation ./go.sh run-docker-desktop-job

$env:JOB = citibike_distance_calculation 
.\go.ps1 run-docker-desktop-job

JOB=citibike_distance_calculation ./go.sh run-colima-job

If you would like to run the code in your laptop locally without containers then please follow instructions here.

Alternatively, you can setup the environment using

It's recommend that you setup ssh to Gitpod so that you can use VS Code from local to remote to Gitpod.

There's an initialize script setup that takes around 3 minutes to complete. Once you use paste this repository link in new Workspace, please wait until the packages are installed. After everything is setup, select Poetry's environment by clicking on thumbs up icon and navigate to Testing tab and hit refresh icon to discover tests.

If Testing tab complains about Python Interpreter, run poetry shell in terminal to get the bin path, replace activate with python3 to resolve the issue.

If poetry shell activate with this path

/workspace/.pyenv_mirror/poetry/virtualenvs/{project_name}-py{python_version}/bin/activate

Paste this into Python Interpreter prompt

/workspace/.pyenv_mirror/poetry/virtualenvs/{project_name}-py{python_version}/bin/python3