/E-commerce-Sales-Analysis-Pipeline

Create an advanced data engineering pipeline that processes and analyzes sales data from an e-commerce website using Apache Airflow for workflow management and ClickHouse as the high-performance data warehouse.

Primary LanguageJupyter Notebook

E-commerce-Sales-Analysis-Pipeline

Create an advanced data engineering pipeline that processes and analyzes sales data from an e-commerce website using Apache Airflow for workflow management and ClickHouse as the high-performance data warehouse.

Global Architecture

Project Tasks:

1. Data Collection:

  • Obtain or generate a sample dataset of e-commerce sales data. Include information such as transaction date, product sold, quantity, price, customer details, etc.

2. Data Preprocessing:

  • Cleanse and preprocess the data to handle missing values, outliers, and data inconsistencies.

3. Data Warehouse Setup with ClickHouse:

  • Install and configure ClickHouse, an open-source columnar database management system designed for high-performance analytics.
  • Create tables and optimize the schema for efficient data storage and querying.

4. ETL Pipeline with Apache Airflow:

  • Set up Apache Airflow to manage the end-to-end ETL pipeline.
  • Design and implement Airflow DAGs (Directed Acyclic Graphs) to orchestrate data extraction, transformation, and loading processes.
  • Schedule DAG runs to process data at regular intervals (e.g., daily or weekly).

5. Data Visualization:

  • Use Grafana and other python libraries to create insightful visualizations, showcasing sales trends, customer behavior, and other important metrics. dashboard-1

    dashboard-2

6. Automation with Ansible:

  • Implement Ansible playbooks to automate the deployment and configuration of the ClickHouse database and Apache Airflow on a server or cloud platform.
  • Use Ansible to manage the project's infrastructure and dependencies efficiently.

7. Error Handling and Logging:

  • Incorporate error handling mechanisms within the Airflow DAGs to handle failures gracefully and send notifications if any issues occur.
  • Implement logging to track the pipeline's performance and troubleshoot potential problems.

8. Documentation:

  • Provide clear and detailed documentation of the project, including instructions for setting up the environment, running Airflow DAGs, and accessing ClickHouse data.

9. Bonus Enhancements:

  • Implement data streaming capabilities using Apache Kafka and process real-time data updates.
  • Deploy the entire data engineering pipeline on cloud services like AWS or Azure using infrastructure-as-code (IaC) tools like Terraform.
  • Implement more complex analytics, such as customer lifetime value or cohort analysis.

By incorporating Apache Airflow, ClickHouse, and Ansible automation, we create a scalable, efficient, and automated data engineering project. This project aims to manage relativily complex workflows, leverage high-performance databases, and automate essential processes. Happy coding!