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Etsy Data Engineering Project

Table of Contents

1. Introduction
2. System Architecture
3. Data Sources
4. Technologies
5. Getting Started
6. Usage
7. Monitoring and Maintenance
8. Key Takeaways

Introduction

This project aims to create an end-to-end data engineering pipeline for retrieving, processing, and analyzing data from the Etsy marketplace using the Etsy API. The pipeline includes data ingestion, processing, storage, and analysis components, allowing for the extraction of valuable insights and information from Etsy's product listings, shop data, reviews, and more.

System Architecture

The project is designed with the following components:

• Etsy API: Retrieves data like product listings, shop info, and reviews from Etsy.
• Data Ingestion: Fetches and feeds data from Etsy API into the pipeline.
• Data Processing: Cleans, transforms, and enriches the ingested data.
• Data Storage: Utilizes databases or data lakes for storing processed data.
• Data Analysis: Employs tools for insights generation and visualization.

Additionally, it utilizes:

• Apache Airflow: Orchestrates the pipeline and manages PostgreSQL data storage.
• Apache Kafka & Zookeeper: Streams data from PostgreSQL to processing engines.
• Control Center & Schema Registry: Monitors and manages Kafka streams.
• Apache Spark: Executes data processing with master-worker node architecture.
• Cassandra: Stores the finalized data.

Data Sources

• Etsy API: Provides access to product listings, shop information, reviews, and more from the Etsy marketplace.

Technologies

• Python: Language for data ingestion, processing, and analysis.
• Etsy API: Retrieves data from Etsy marketplace.
• Apache Airflow: Manages and schedules data pipeline tasks.
• PostgreSQL: Stores processed data.
• Docker: Packages and deploys the data engineering environment.
• Apache Kafka, Zookeeper, Spark: Power real-time data processing.
• Cassandra: Distributed database for scalable storage.

Getting Started

Prerequisites

• Python installed
• Docker installed
• Etsy API credentials

Setup

1. Clone the repository:

   git clone [repository-url]
   

2. Navigate to the project directory:

   cd etsy-data-engineering
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Set up Etsy API credentials:
   • Register for Etsy API access and obtain API keys
   • Set the API keys as environment variables or configure them in the application

Usage

1. Start the data engineering pipeline:

   docker-compose up
   

2. Monitor the pipeline execution and data processing tasks using Apache Airflow UI.

Monitoring and Maintenance

• Monitor the pipeline execution and data ingestion tasks to ensure they're running smoothly.
• Implement logging, alerting, and monitoring mechanisms to track any issues or failures.
• Regularly review and update the pipeline to accommodate changes or updates to the Etsy API or data processing requirements.

Key Takeaways

• Data Pipeline Expertise: Proficient in configuring and managing data workflows using Apache Airflow for effective orchestration.

• Real-time Streaming Capabilities: Skilled in leveraging Apache Kafka for real-time data streaming, facilitating timely data processing and insightful analysis.

• Distributed Systems Mastery: Experienced in utilizing Apache Zookeeper for distributed synchronization, ensuring consistent and reliable data operations across systems.

• Advanced Data Processing: Adept in employing Apache Spark for large-scale data processing tasks, enhancing data processing efficiency and performance.

• Storage Solutions Proficiency: Familiar with utilizing Cassandra and PostgreSQL for scalable and efficient data storage solutions tailored to diverse requirements.

• Containerization Expertise: Experienced in leveraging Docker for containerizing data engineering setups, streamlining deployment, scaling, and management processes.

• End-to-End Pipeline Development: Comprehensive understanding of building end-to-end data engineering pipelines, encompassing data ingestion, processing, storage, and analysis stages.

• Integration Skills: Skilled in integrating diverse technologies and components to develop cohesive and efficient data engineering solutions.

• Adherence to Best Practices: Committed to applying industry best practices and techniques for designing, implementing, and optimizing data pipelines and workflows.

• Hands-on Practical Experience: Demonstrated ability to apply theoretical knowledge through practical exercises, projects, and real-world examples, enhancing proficiency in data engineering concepts and tools.