Analyzing Yellow Taxi Data. This project can be executed using Docker as Container for loading data to DB as a scheduler Job using Chronos in Mesos Platform or Kubernetes Scheduler

Data Downloading and Loading Process are tracked and managed using table tbl_status

Python 3.7+ and PostgresSql

.. code-block::
.
|-- Dockerfile
|-- MANIFEST.in
|-- Makefile
|-- README.md
|-- dataset
|   |-- 
|-- docker-compose.yml
|-- requirements.txt
|-- setup.cfg
|-- setup.py
|-- sql
|   `-- V1.0_CREATE_TABLE.sql
|-- src
|   `-- processing
|       |-- __init__.py
|       |-- cli.py
|       |-- constant.py
|       |-- transform.py
|       `-- util.py
|-- tests
|   |-- __init__.py
|   `-- test_analysis.py
`-- yellow_taxi_analysis.ipynb

yellow_taxi_analysis.ipynb : Has all the analysis outputs.

Steps:

• git clone https://github.com/krishnabigdata/taxi-data-analysis.git

• pip install -r taxi-data-analysis/requirements.txt

• pip install --upgrade taxi-data-analysis

• docker-compose up -d

Building Docker and using docker

• make build -e VERSION=latest
• make push -e VERSION=latest
• docker run -t -i --network host docker.io/krishnabigdata/taxi_data_analysis -v ${PWD}:/taxi_data_analysis/dataset --action download --year 2019 --month 1 --color yellow

Commands to use the processing cli

.. code-block:: bash 

usage: processing [-h] [--year YEAR] [--month {1,2,3,4,5,6}]
              [--color {yellow}] --action
              {all,download,load,avg_trip,avg_trip_local,rolling_avg_trip}
              [--verbose VERBOSE]

Taxi Data Analysis

optional arguments:
  -h, --help            show this help message and exit
  --year YEAR           year of data to load (default: 2019)
  --month {1,2,3,4,5,6}
                        month of data to load (default: 1)
  --color {yellow}      color of data to load (default: yellow)
  --action {all,download,load,avg_trip,avg_trip_local,rolling_avg_trip}
                        action to be performed (default: all)
  --verbose VERBOSE     logging action to be performed (default: True)

• all: Performs all steps
  • Downloading, LoadingToDB, Queries DB for AVG and Rolling AVG
• avg_trip_local
  • Calculates Trip Distance Average by Month by Querying the Locally downloaded file.
• avg_trip
  • Calculates Trip Distance Average by Month by Querying the DB.
• rolling_avg_trip
  • Calculates 45 Day Rolling Trip Distance Average by Querying the DB.

We can use the below options for distributed processing in order to process huge volume of data which cannot be processed by single instance.

• pyspark - For distributed processing
• DB: Parallel loading of files to DB and analysis using SQL queries.
• Streaming: Data produced as events to Kafka and Processing using Kafa-Connect connectors or Spark Structured Streaming or Consume from Kafka and load to DB -> SQL Query