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.
- 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.
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.
- 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.