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

1. Get a working environment
   Either local (local, or using gitpod)

You can setup the environment using

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.

Note that you can use gitpod's web interface or setup ssh to Gitpod so that you can use VS Code from local to remote to Gitpod

All of the following commands should be running successfully

poetry run pytest tests/unit

poetry run pytest tests/integration

poetry run mypy --ignore-missing-imports --disallow-untyped-calls --disallow-untyped-defs --disallow-incomplete-defs \
            data_transformations tests

poetry run pylint data_transformations tests

All commands are passing?
You are good to go!

You can customize your environment (having the test in vscode directly for example): feel free to spend the time making this comfortable for you.

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

Currently, these exist as skeletons, and have some initial test cases which are defined but some are skipped.

The following section provides context over them.

/
├─ /data_transformations # Contains the main python library
│ # with the code to the transformations
│
├─ /jobs # Contains the entry points to the jobs
│ # performs argument parsing, and are
│ # passed to `spark-submit`
│
├─ /resources # Contains the raw datasets for the jobs
│
├─ /tests
│ ├─ /units # contains basic unit tests for the code
│ └─ /integration # contains integrations tests for the jobs
│ # and the setup
│
├─ .gitignore
├─ .gitpod\* # required for the gitpod setup
├─ .pylintrc # configuration for pylint
├─ LICENCE
├─ poetry.lock
├─ pyproject.toml
└─ README.md # The current file

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.

---
title: Citibike Pipeline
---
flowchart LR
  Raw["fa:fa-file words.txt"] -->  J1{{word_count.py}} --> Bronze["fa:fa-file-csv word_count.csv"]

Simple *.txt file containing text.

A single *.csv file containing data similar to:

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

poetry build && poetry run spark-submit \
    --master local \
    --py-files dist/data_transformations-*.whl \
    jobs/word_count.py \
    <INPUT_FILE_PATH> \
    <OUTPUT_PATH>

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.

---
title: Citibike Pipeline
---
flowchart TD
  Raw["fa:fa-file-csv citibike.csv"] -->  J1{{citibike_ingest.py}} --> Bronze["fa:fa-table-columns citibike.parquet"] --> J2{{citibike_distance_calculation.py}} --> Silver["fa:fa-table-columns citibike_distance.parquet"]

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,...
...

poetry build && poetry run spark-submit \
    --master local \
    --py-files dist/data_transformations-*.whl \
    jobs/citibike_ingest.py \
    <INPUT_FILE_PATH> \
    <OUTPUT_PATH>

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

Hint:

• For distance calculation, consider using Haversine 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
...

poetry build && poetry run spark-submit \
    --master local \
    --py-files dist/data_transformations-*.whl \
    jobs/citibike_distance_calculation.py \
    <INPUT_PATH> \
    <OUTPUT_PATH>

If you are unfamiliar with some of the tools used here, get started from:

• pytest: official
• pyspark: official and especially the DataFrame quickstart