automated-data-integration-pipeline

Automated Data Pipeline for Grant Data Integration

Description:

This project involves the development of an automated data pipeline to integrate grant data from a public source into a data platform. The pipeline incorporates data extraction, transformation, validation, and loading processes to ensure data quality and enhance the information available within the platform. This project showcases the implementation of robust data engineering practices to improve data accuracy and team efficiency.

Project Overview:

This project streamlines the extraction, processing, and integration of grant data. The key components are:

1. Automated Data Extraction:
   • Automates the extraction of data from a public website using web automation techniques.
   • Handles form submissions and website changes to ensure reliable data extraction.
2. Data Storage & Transfer:
   • Stores the extracted data in a temporary storage location.
   • Automates the transfer of data to a cloud-based storage system.
3. Cloud-Based Storage:
   • Utilizes cloud storage for scalable and reliable storage of raw data.
4. Cloud-Based Data Processing:
   • Processes data in a cloud-based distributed computing environment.
   • Performs data cleaning, transformation, and matching.
   • Enriches data with information from an existing data source.
5. Database Integration:
   • Loads the processed data into a database.
   • Maintains data integrity and updates information.
6. Data Validation:
   • Implements data validation to identify and rectify discrepancies.
   • Defines expectations, runs checks, and generates quality reports.
7. Data Profiling:
   • Profiles data to identify issues like missing values and inconsistencies.
8. Standardized Data Ingestion Template:
   • Streamlines ingestion processes for consistency and efficiency.
9. Data Lineage Tracking:
   • Tracks data transformations for integrity and traceability.
10. Data Visualization:
    • Visualizes integrated data for analysis.

Architecture

Public Website (Web Automation) –> Temporary Storage –> Cloud Storage –> Cloud-Based Processing –> Database –> Data Visualization

Technologies

• Automation & Web Scraping:
  • Web Automation Library (e.g., Selenium)
  • Automation Tool (e.g., Power Automate or similar)
• Python:
  • Libraries for web scraping (e.g., Beautiful Soup, Requests)
  • Libraries for cloud storage interaction (e.g., boto3 for AWS)
  • Libraries for data manipulation (e.g., Pandas)
  • Libraries for fuzzy matching
• Cloud Computing:
  • Cloud Platform (e.g., AWS, Azure, GCP)
  • Cloud Storage Service (e.g., AWS S3, Azure Blob Storage, Google Cloud Storage)
  • Cloud-Based Distributed Computing (e.g., AWS Databricks, Azure Synapse, Google Cloud Dataproc)
• Distributed Processing:
  • Distributed Processing Framework (e.g., PySpark)
• Database:
  • Database System (e.g., PostgreSQL, MySQL, Cloud-based databases)
• Visualization:
  • Data Visualization Tool (e.g., Power BI, Tableau, Looker)
• Orchestration:
  • Workflow Orchestration Tool (e.g., Apache Airflow, Prefect, Dagster)
• Validation:
  • Data Validation Library (e.g., Great Expectations)
• Profiling:
  • Data Profiling Library (e.g., Pandas Profiling)
• Containerization:
  • Containerization Platform (e.g., Docker)
• Version Control:
  • Git

Setup and Installation

1. Cloud Platform Setup:
   • Set up a cloud platform account and configure necessary services.
   • Configure IAM roles/permissions for access.
2. Data Storage Setup:
   • Configure storage services for temporary and cloud-based storage.
3. Database Setup:
   • Ensure access to the database system.
4. Python Dependencies:
   • Install necessary Python libraries using pip install (list specific libraries used).
5. Distributed Computing Setup:
   • Set up and configure the distributed computing environment.
6. Web Automation Setup:
   • Install and configure the web automation library and any related tools.
7. Workflow Orchestration Setup:
   • Install and configure the workflow orchestration tool.
8. Containerization Setup:
   • Install and configure the containerization platform.

Usage

1. Configure necessary credentials and environment variables.
2. Run the web automation script to extract data.
3. Ensure the automation tool is active and transferring data to the temporary storage.
4. Verify data transfer to the cloud storage.
5. Process data using the distributed computing environment.
6. Load the processed data into the database.
7. Visualize the integrated data using the chosen visualization tool.
8. Schedule the processes using the workflow orchestration tool.
9. Run data validation checks.
10. Run data profiling to analyze data quality.

Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues to suggest improvements or report bugs.

License

This project is licensed under the MIT License.

Contact:

• LinkedIn: URL