🌄 Youtube-ETL-Pipeline

In this project, I build a simple data pipeline following the ETL(extract - transform - load) model using Youtube-Trending-Video dataset, perform data processing, transformation and calculation using Apache Spark big data technology, serving the video search and recommendation system

🔦 About Project

• Data Source: This project uses two main data sources: Youtube Trending Video data and Youtube API
  • Youtube Trending Video data is downloaded from Kaggle.com with .csv file format, then loaded into MySQL, considered as a data source
  • Using Video ID and Category ID from Youtube Trending Video data, we collect some additional information fields from Youtube API such as Video Link and Video Category
• Extract Data: Extract the above data sources using Polars DataFrame, now we have the raw layer, then load the data into MinIO datalake
• Tranform Data: From MinIO, we use Apache Spark, specifically PySpark
  • convert from Polars DataFrame to PySpark DataFrame for processing and calculation, we get silver and gold layers
  • Data stored in MinIO is in .parquet format, providing better processing performance
• Load Data: Load the gold layer into the PostgreSQL data warehouse, perform additional transform with dbt to create an index, making video searching faster
• Serving: The data was used for visualization using Metabase and creating a video recommendation application using Streamlit
• package and orchestrator: Use Docker to containerize and package projects and Dagster to coordinate assets across different tasks

📦 Technologies

• MySQL
• Youtube API
• Polars
• MinIO
• Apache Spark
• PostgreSQL
• Dbt
• Metabase
• Streamlit
• Dagster
• Docker
• Apache Superset
• Unittest
• Pytest

🦄 Features

Here's what you can do with:

• You can completely change the logic or create new assets in the data pipeline as you wish, perform aggregate calculations on the assets in the pipeline according to your purposes.
• You can also create new data charts as well as change existing charts as you like with extremely diverse chart types on Metabase and Apache Superset.
• You can also create new or change my existing dashboards as you like
• Search videos quickly with any keyword, for Video Recommendation Apps
• Search in many different languages, not just English such as: Japanese, Canadian, German, Indian, Russian
• Recommend videos based on category and tags video

👩🏽‍🍳 The Process

📚 What I Learned

During this project, I learned important skills, understood complex ideas, knew how to install and set up popular and useful tools, which brought me closer to becoming a Data Engineer.

• Logical thinking: I learned how to think like a data person, find the cause of the data problem and then come up with the most reasonable solution to achieve high data accuracy.
• Architecture: I understand and grasp the ideas and architecture of today's popular and popular big data processing tool, Apache Spark.
• Installation: I learned how to install popular data processing, visualization and storage tools such as: Metabase, Streamlit, MinIO,... with Docker
• Setup: I know how to setup Spark Standalone Cluster using Docker with three Worker Nodes on my local machine

📈 Overall Growth:

Each part of this project has helped me understand more about how to build a data engineering, data management project. Learn new knowledge and improve my skills in future work

💭 How can it be improved?

• Add more data sources to increase data richness.
• Refer to other data warehouses besides PostgreSQL such as Amazon Redshift or Snowflake.
• Perform more cleaning and optimization processing of the data.
• Perform more advanced statistics, analysis and calculations with Apache Spark.
• Check out other popular and popular data orchestration tools like Apache Airflow.
• Separate dbt into a separate service (separate container) in docker when the project expands
• Setup Spark Cluster on cloud platforms instead of on local machines
• Refer to cloud computing services if the project is more extensive
• Learn about dbt packages like dbt-labs/dbt_utils to help make the transformation process faster and more optimal.

🚦 Running the Project

To run the project in your local environment, follow these steps:

1. Run command after to clone the repository to your local machine.

   git clone https://github.com/longNguyen010203/Youtube-ETL-Pipeline.git

2. Run the following commands to build the images from the Dockerfile, pull images from docker hub and launch services

   make build
   make up

3. Run the following commands to access the SQL editor on the terminal and Check if local_infile was turned on

   make to_mysql_root

   SET GLOBAL local_infile=TRUE;
   SHOW VARIABLES LIKE "local_infile";
   exit

4. Run the following commands to create tables with schema for MySQL, load data from CSV file to MySQL and create tables with schema for PostgreSQL

   make mysql_create
   make mysql_load
   make psql_create

5. Open http://localhost:3001 to view Dagster UI and click Materialize all button to run the Pipeline
6. Open http://localhost:9001 to view MinIO UI and check the data to be loaded
7. Open http://localhost:8080 to view Spark UI and three workers are running
8. Open http://localhost:3030 to see charts and dashboards on Metabase
9. Open http://localhost:8501 to try out the video recommendation app on Streamlit

🍿 Video

Screencast.from.2024-06-16.22-00-03.webm