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Project Overview

This project focuses on developing a Data Vault storage solution for data retrieved from Amazon S3, leveraging a robust technology stack to manage and analyze data effectively.

Technology Stack

  • Data Vault: The methodology applied for creating a scalable and flexible data warehouse using the Data Vault architecture.
  • Apache Airflow: Used for orchestrating and automating the workflows for data extraction, loading, and transformation.
  • Vertica DB: Serves as the high-performance SQL database for storing and querying large datasets, utilizing its columnar storage capabilities.
  • S3 API: Utilized for accessing and managing data stored in Amazon S3 buckets.
  • Docker: Employed to containerize and isolate the application environment, ensuring consistency across different development and production settings.

Main Task

The primary objective of this project is to develop a Data Vault-based storage system. This involves:

  • Designing and implementing a scalable Data Vault model within Vertica DB.
  • Extracting data from Amazon S3 using S3 API integrations.
  • Automating data flows and ETL processes with Apache Airflow to populate the Data Vault.
  • Ensuring data consistency and integrity throughout the data lifecycle.
  • Configuring Docker containers to manage the application and its dependencies efficiently.

Goals

  • Efficiency: Optimize data retrieval and storage processes to handle large volumes of data efficiently.
  • Scalability: Design the system to scale seamlessly with increasing data sizes and complexity.
  • Reliability: Ensure high availability and reliability of the data storage and retrieval processes.

This project aims to harness the strengths of each component in the technology stack to create a robust data warehousing solution that supports complex data analysis and business intelligence activities.