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Project Scenario 🎩

You are a data engineer at a data analytics consulting company. You have been assigned to a project that aims to de-congest the national highways by analyzing the road traffic data from different toll plazas. Each highway is operated by a different toll operator with a different IT setup that uses different file formats. Your job is to collect data available in different formats and consolidate it into a single file.

Objectives📝

  • In this project you will create a shell script using bash commands to:
    • Extract data from a CSV file
    • Extract data from a TSV file
    • Extract data from a fixed-width file
    • Transform the data
    • Load the transformed data into a new CSV file
    • You will then create a DAG to call the shell script.

Reach/Follow me on 🚀

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Prepare the lab environment 📦

  1. Start Apache Airflow.
  2. Download the dataset from the source to the destination /your path directory/airflow/dags using wget command. Source:
https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DB0250EN-SkillsNetwork/labs/Final%20Assignment/tolldata.tgz

Note: While downloading the file in the terminal use the sudo command before the command used to download the file.

Directions 🗺

Task 1.1 - Define DAG arguments

  • Define the DAG arguments as per the following details:
Parameter 🔰 Value
owner < You may use any dummy name>
start_date today
email < You may use any dummy email>
email_on_failure True
email_on_retry True
retries 1
retry_delay 5 minutes

Task 1.2 - Define the DAG

  • Create a DAG as per the following details.
Parameter 🔰 Value
DAG id ETL_toll_data
Schedule Daily once
default_args as you have defined in the previous step
description Apache Airflow Project

Task 1.3 - Create a shell script Extract_Transform_data.sh and add the following commands to your tasks:

  • Write a command to unzip the data.
  • Use the downloaded data from the URL given in the first part of this project and uncompress it into the destination directory.

Hint: Read through the file fileformats.txt to understand the column details.

Task 1.4 - Update the shell script to add a command to Extract Data From CSV file

  • You should extract the fields Rowid, Timestamp, Anonymized Vehicle number, and Vehicle type from the vehicle-data.csv file and save them into a file named csv_data.csv.

Task 1.5 - Update the shell script to add a command to Extract Data From TSV file

  • You should extract the fields Number of axles, Tollplaza id, and Tollplaza code from the tollplaza-data.tsv file and save it into a file named tsv_data.csv.

Task 1.6 - Update the shell script to add a command to Extract Data From fixed-width file

  • You should extract the fields Type of Payment code, and Vehicle Code from the fixed width file payment-data.txt and save it into a file named fixed_width_data.csv.

Task 1.7 -Update the shell script to add a command to consolidate data Extracted from previous tasks

  • You should create a single CSV file named extracted_data.csv by combining data from the following files:
  • csv_data.csv
  • tsv_data.csv
  • fixed_width_data.csv
  • The final CSV file should use the fields in the order given below: Rowid, Timestamp, Anonymized Vehicle number, Vehicle type, Number of axles, Tollplaza id, Tollplaza code, Type of Payment code, and Vehicle Code

Hint: Use the bash paste command. paste command merges lines of files. Example : paste file1 file2 > newfile

Task 1.8 -. Update the shell script to add a command to Transform and load the data

  • You should transform the vehicle_type field in extracted_data.csv into capital letters and save it into a file named transformed_data.csv in the staging directory.

Note: Copy the shell script to /your path/airflow/dags folder

Task 1.9 - Create a task extract_transform_load in the ETL_toll_data.py to call the shell script.

  • Save the DAG you defined into a file named ETL_toll_data.py
  • Define the task pipeline as per the details given below:
Task 🔰 Functionality
First task extract_transform_load

SnapShot and Results 📸

  • I provided my solution for this project a python file go and check it out.
  • After implementations your results at Airflow should look like this: dag_runs

Contributing 📝

Contributions are welcome! Please open an issue or pull request for any changes or improvements.