curtalfrey / data-multivector

Data-MultiVector is a powerful data processing & analysis app designed for complex, multi-dimensional datasets efficiently. Manipulate, analyze, and visualize data from various sources. Whether it's structured, time series, or unstructured data, Data-MultiVector provides a flexible and extensible platform for a wide range of data processing tasks.

Data-MultiVector (open-source)

Created, designed, and maintained by Curtis Alfrey Alpha test phase v 1.0

The main thing, is to keep the main thing, the main thing.

and THE MAIN THING is to create a tool that will do what a linux symlink command will do, but, with mulitple inputs and/or outputs where symlink only connects 1 input and 1 output...... Data-MultiVector will create, manage, track, and report multiple data location links.

install

2 example pictures of how data can be managed through this system from multiple input directories and output in 1 or more directories.

Data-MultiVector is a powerful data processing & analysis app designed for complex, multi-dimensional datasets efficiently. Manipulate, analyze, and visualize data from various sources. Whether it's structured, time series, or unstructured data, Data-MultiVector provides a flexible and extensible platform for a wide range of data processing tasks.

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Installation instructions

Key Capabilities:

Data Import and Integration: Data-MultiVector allows users to import data from diverse sources, including databases, CSV files, REST APIs, and more. It supports the integration of data from multiple origins into a unified workspace.

Data Transformation: Users can perform data transformation operations such as filtering, aggregation, cleansing, and feature engineering to prepare data for analysis. The application supports complex data transformations and scripting for custom data manipulation.

Multi-Dimensional Analysis: Data-MultiVector excels in multi-dimensional analysis, making it suitable for tasks like financial modeling, scientific research, and business intelligence. Users can explore data across various dimensions and time periods.

Data Visualization: The program includes powerful data visualization tools for creating insightful charts, graphs, and dashboards. Users can visualize data trends, anomalies, and patterns to gain deeper insights.

Statistical Analysis: Data-MultiVector offers a wide range of statistical analysis capabilities, including descriptive statistics, hypothesis testing, regression analysis, and more. It supports both basic and advanced statistical techniques.

Machine Learning Integration: Users can leverage machine learning libraries and algorithms to build predictive models and perform advanced analytics. Data-MultiVector provides seamless integration with popular machine learning frameworks.

Real-Time Data Processing: For applications requiring real-time data processing, Data-MultiVector offers the ability to ingest, process, and analyze streaming data, enabling timely decision-making.

Extensibility: The program is designed to be extensible, allowing users to add custom functions, plugins, and scripts to tailor it to their specific needs. It supports integration with third-party libraries and data sources.

Data Export and Reporting: Users can export processed data, visualizations, and analysis results in various formats (e.g., CSV, Excel, PDF) or generate automated reports for sharing insights with stakeholders.

Scalability and Performance: Data-MultiVector is optimized for performance and can handle large datasets efficiently. It can be deployed on cloud platforms for scalability as data volumes grow.

Use Cases:

Financial Analysis and Modeling Scientific Data Exploration Business Intelligence and Reporting Predictive Analytics IoT Data Processing Social Media Analytics Healthcare Data Analysis Supply Chain Optimization Fraud Detection Market Research Data-MultiVector empowers data analysts, scientists, and engineers to unlock the potential of their data, enabling informed decision-making and driving innovation across various domains. Its flexible architecture, rich feature set, and extensibility make it a valuable tool for data professionals working on diverse data challenges.

data-multivector's Issues

dev-ops goals

<title>Developer Instructions - data-multivector</title>

Developer Instructions - data-multivector Project

<h2>1. Data Processing Logic</h2>
<p>Implement the logic to process input paths, output paths, and connections:</p>
<ul>
    <li>Create dictionaries or data structures to store connection data and change logs.</li>
    <li>Process and validate user-submitted data (input paths, output paths, connections).</li>
    <li>Implement logic to handle data storage, such as creating symlinks, copying files, etc.</li>
    <li>Ensure data validation and security checks to prevent malicious input.</li>
</ul>

<h2>2. Logging Configuration</h2>
<p>Enhance logging functionality for better debugging and monitoring:</p>
<ul>
    <li>Add timestamps and log levels to log entries.</li>
    <li>Improve log messages with relevant information about each logged event.</li>
</ul>

<h2>3. Error Handling</h2>
<p>Implement error handling to gracefully handle exceptions:</p>
<ul>
    <li>Wrap relevant parts of the code in try-except blocks to catch and handle exceptions.</li>
    <li>Provide meaningful error messages to users and log details for debugging.</li>
</ul>

<h2>4. Security Measures</h2>
<p>Ensure the security of the application:</p>
<ul>
    <li>Implement CSRF protection to prevent cross-site request forgery attacks.</li>
    <li>Secure routes and endpoints to restrict access to authorized users.</li>
    <li>Validate and sanitize user input to prevent injection and other security vulnerabilities.</li>
</ul>

<h2>5. Frontend Design</h2>
<p>Design user-friendly frontend templates:</p>
<ul>
    <li>Create or update HTML templates (index.html, developer.html, documentation.html) for better user interaction.</li>
    <li>Add forms, input fields, and buttons for submitting data to the backend.</li>
</ul>

<h2>6. Deployment Considerations</h2>
<p>Prepare the application for production deployment:</p>
<ul>
    <li>Replace the Flask development server with a production-ready server (e.g., Gunicorn).</li>
    <li>Configure the application for secure deployment using HTTPS.</li>
</ul>

<!-- Add more sections as the project evolves -->

building code in program script(s) then dockerize project

building code in program script(s) then dockerize project