Welcome to our Data Platform Project! This README provides an overview of the project, including its purpose, tech stack, and instructions for getting started.

• Data Platform Project
  • Table of Contents
  • Introduction
  • Architecture
  • Getting Started
    • Prerequisites
    • Installation

Welcome to the Data Platform Project for ASACI TECHNOLOGIES. This platform is designed to support our data management and processing needs, providing a robust and scalable solution for handling large volumes of data. Our goal is to enhance data integrity, reliability, and accessibility across our operations.

This platform integrates advanced technologies to streamline our data workflows, from ingestion and transformation to orchestration and metadata management. It aims to empower our data team to efficiently manage and analyze data, drive insights, and support our business objectives effectively. This README will guide you through the tools and processes involved in our data platform, helping you make the most of its capabilities.

The architecture of the Data Platform Project is designed to support a comprehensive and efficient data workflow. Below are the key components of our platform:

1. Data Ingestion:

   • Sling: Manages the scalable ingestion of data from various sources into the system.
   • dlt (data load tool): Facilitates the loading of data into our data warehouse, ensuring smooth data flow.

2. Data Transformation:

   • DBT (Data Build Tool): Transforms raw data into actionable insights, managing complex data models and transformations.

3. Data Storage:

   • PostgreSQL: Handles transactional data and complex queries.
   • ClickHouse: Optimized for analytical queries and real-time data analysis.
   • MySQL: Provides reliable relational database capabilities for various applications.
   • S3: Used for scalable storage of large datasets and data backups.

4. Data Orchestration:

   • Dagster: Coordinates the execution of data pipelines, ensuring tasks are performed in the correct order for reliable and repeatable data processes.

5. Metadata Management:

   • DataHub: Manages metadata, offering visibility into data assets and their relationships to maintain data quality and governance.

6. Data Querying and Analysis:

   • Cube.dev: Provides a powerful and flexible analytical data layer, enabling efficient querying and aggregation of data for reporting and analysis.
   • MindsDB: Integrates machine learning capabilities into the data pipeline, allowing for predictive analytics and advanced data modeling.

7. Data Visualization and Reporting:

   • PowerBI: Delivers interactive visualizations and business intelligence capabilities, enabling data-driven decision-making through dynamic reports and dashboards.

This architecture ensures a robust, scalable, and flexible data platform, supporting ASACI TECHNOLOGIES' data management needs and facilitating comprehensive data analysis and reporting.

To get started with the Data Platform Project, follow these steps:

• Python
• Docker

1. Clone the repository:

git clone https://github.com/your-username/data-platform-project.git
cd data-platform-project