Ship your Data in days, not weeks

You get a powerful data comparison tool designed to process CSV files entirely in your local environment. ShipDataFast includes features like customizable column mapping, primary key support, detailed mismatch reports, and both CLI and web UI options. Plus, you receive the full repository with the app's code, giving you complete access to modify, integrate, and scale the tool to suit your needs. It's everything you need for fast, accurate data comparisons without relying on the cloud—just reliable, secure performance right on your system.

ShipDataFast is built using a robust and scalable tech stack designed for flexibility and performance. The core backend is written in Python (3.8+), optimized for handling large CSV files and complex data comparisons. For data manipulation, it leverages Pandas, while the web interface is built using Flask to provide a responsive and user-friendly experience. The CLI is integrated with pytest for smooth automation and testing. This stack ensures efficient processing, easy customization, and seamless integration with various systems.

ShipDataFast is a robust data comparison tool designed to efficiently process and compare large CSV files within your local environment. It offers customizable column mapping, primary key support, detailed mismatch reports, and both Command-Line Interface (CLI) and Web User Interface (Web UI) options. ShipDataFast empowers users to perform fast and accurate data comparisons without relying on cloud services, ensuring data security and reliability. Additionally, users receive the full repository with the application's source code, allowing complete access for modification, integration, and scaling to meet specific needs.

ShipDataFast is optimized for handling large CSV datasets through its efficient processing algorithms. The tool leverages memory-efficient data structures and parallel processing where applicable to ensure swift comparisons even with substantial file sizes. Additionally, users can define specific columns and primary keys to streamline the comparison process, reducing computational overhead and improving performance. This optimization ensures that ShipDataFast remains responsive and accurate, regardless of the CSV file size.

Absolutely! ShipDataFast provides flexible column mapping capabilities, allowing you to define how columns from File A correspond to those in File B. You can use the Web Admin Interface to create or modify mapping configurations interactively. This customization includes setting data types, format requirements, and tolerance levels for numerical comparisons, ensuring that the tool accurately aligns and compares your data according to your specific criteria.

ShipDataFast is designed to be platform-agnostic, supporting major operating systems including Windows, macOS, and Linux. Since it's built with Python, it can be easily installed and run on any system that supports Python 3.8 or higher. Whether you're working on a local machine, a server, or a virtual environment, ShipDataFast provides the flexibility to integrate seamlessly into your existing workflows.

ShipDataFast prioritizes data security by operating entirely within your local environment. Unlike cloud-based solutions, your CSV files and sensitive data remain on your system, eliminating concerns about data breaches or unauthorized access from external servers. Additionally, since ShipDataFast provides access to the full source code, you can audit and verify the application's security measures to ensure compliance with your organization's data protection policies.

Yes, ShipDataFast offers comprehensive technical support to assist users with installation, configuration, and troubleshooting. Support is available through email, initially. Additionally, detailed documentation, tutorials, and sample files are provided on the ShipDataFast website to help users get started and make the most of the tool's features.

Installing ShipDataFast is straightforward. Follow these steps to get started:

1. System Requirements: Ensure your system has Python 3.8 or higher installed.
2. Download: Visit shipdatafast.com and download the latest version of ShipDataFast.
3. Installation: Extract the downloaded files and navigate to the directory in your terminal or command prompt.
4. Dependencies: Install the required Python packages by running:

   pip install -r requirements.txt

5. Run the Application: Launch ShipDataFast via the CLI or start the Web UI by executing:

   python app.py

6. Access the Web UI: Open your web browser and navigate to http://localhost:port to access the user-friendly interface.

For detailed installation instructions, refer to the Getting Started section in our documentation.

Yes, ShipDataFast is built with integration in mind. Its modular architecture and accessible source code allow seamless integration with various systems and workflows. Whether you need to incorporate ShipDataFast into existing data pipelines, automate comparisons using scripts, or integrate with continuous integration/continuous deployment (CI/CD) tools like Azure DevOps, Jenkins or GitHub Actions, ShipDataFast provides the flexibility to connect and enhance your data management processes effectively.

ShipDataFast is designed to run efficiently on most modern systems. The minimum system requirements include:

• Operating System: Windows 10 or later, macOS Catalina or later, Linux (Ubuntu 18.04+ recommended)
• Processor: Intel i5 or equivalent
• Memory: 8 GB RAM (16 GB recommended for large datasets)
• Storage: 100 MB of free disk space for installation; additional space for CSV files
• Software: Python 3.8 or higher, pip package manager

For optimal performance, especially when handling very large CSV files, higher specifications are recommended.

ShipDataFast supports automation through its Command-Line Interface (CLI) and integration with testing frameworks like pytest. You can create scripts to run comparisons at scheduled intervals, integrate them into CI/CD pipelines, or trigger them based on specific events. Here is a basic example using the CLI:

pytest compare_data.py --file_a path/to/file_a.csv --file_b path/to/file_b.csv --mapping path/to/mapping_file.csv

By incorporating such commands into shell scripts or pipeline configurations, you can automate the data comparison process, ensuring continuous monitoring and validation of your CSV data.

Yes, ShipDataFast offers comprehensive documentation, including a detailed Getting Started guide, step-by-step tutorials, and example use cases. These resources are designed to help users at all levels maximize the potential of ShipDataFast effectively.

ShipDataFast provides flexibility in how you access and view mismatch reports. After a comparison, reports can be generated in various formats such as HTML for easy viewing in a web browser, CSV for further analysis in spreadsheet applications, or JSON for integration with other tools and systems. Users can choose to expand the out-of-the-box CSV report to their preferred format, and make it available via the Web UI or specify it in CLI commands to suit their workflow needs.

Yes, ShipDataFast allows the designation of multiple primary keys to uniquely identify records in your CSV files. By specifying multiple columns as primary keys in the mapping_file.csv or through the Web Admin Interface, you can enhance the accuracy of data comparisons, especially in datasets where a single column is insufficient to uniquely identify records. This feature helps prevent mismatches and ensures precise data alignment.

ShipDataFast is actively maintained and regularly updated to introduce new features, improvements, and security enhancements. Updates are typically released on a monthly basis, with additional patches and hotfixes deployed as needed to address critical issues. Users are encouraged to subscribe to our newsletter or follow our release notes to stay informed about the latest updates and enhancements.

ShipDataFast offers flexible pricing plans to accommodate different user needs, including individual professionals, small businesses, and large enterprises. For our launch the pricing tiers include:

• Pro Tier: Enhanced features with priority support for small to medium-sized businesses.
• Enterprise Tier: Comprehensive features, custom integrations, and dedicated support for large organizations.

Yes, one of the key benefits of ShipDataFast is that it provides access to the full source code. This allows users to customize and extend the tool to meet specific requirements. Whether you need to add new features, integrate with other systems, or modify existing functionalities, you have the freedom to adapt ShipDataFast to your unique needs. All modifications can be managed through standard version control systems like Git.

While ShipDataFast is primarily designed for comparing CSV files, its flexible architecture allows for support of additional data formats with some customization. By leveraging Python's extensive libraries, you can extend ShipDataFast to handle formats such as JSON, Excel, or XML. However, out-of-the-box support is optimized for CSV, ensuring the highest performance and accuracy for this format.

Mismatch reports in ShipDataFast are generated after a data comparison is executed. These reports provide a comprehensive overview of discrepancies between the two CSV files. They typically include:

• Summary Statistics: Total records compared, number of matches, and number of mismatches.
• Detailed Logs: Specific details of each mismatch, including the row number, column name, expected value, and actual value.
• Error Types: Categorization of mismatches based on error types such as data type mismatches, format inconsistencies, or value discrepancies beyond defined tolerances.
• Export Options: Ability to export reports in formats like HTML, CSV, or JSON for further analysis or integration with other tools.

These detailed insights help users quickly identify and address data inconsistencies.

While ShipDataFast is primarily designed for batch processing of CSV files, it can be integrated into real-time data pipelines with the appropriate setup. By leveraging its CLI and scripting capabilities, you can automate comparisons triggered by real-time data events or through continuous monitoring systems. However, for high-frequency real-time comparisons, additional infrastructure and optimizations may be necessary to ensure performance and responsiveness.