🚀 DataFlow - Simple ETL for Efficient Data Processing

📥 Overview

DataFlow is a high-performance ETL (Extract, Transform, Load) pipeline library for .NET. It helps you process data from various sources, including CSV, JSON, Excel, and SQL. The library is designed to use minimal memory through its streaming operations, making it ideal for large datasets.

🚀 Getting Started

To begin using DataFlow, follow the steps below. You will need a Windows, Mac, or Linux machine with the .NET runtime installed. If you don’t have it installed, you can download it from the official .NET website.

📂 Download & Install

1. Visit this page to download: DataFlow Releases.

2. Look for the latest version of DataFlow listed on the page.

3. Click on the appropriate file to download it. Downloading will start automatically.

4. Once the download is complete, locate the downloaded file on your computer.

5. Double-click the file to run DataFlow.

🔧 Features

• Multiple Data Formats: Work with CSV, JSON, Excel, and SQL data formats effortlessly.
• Streaming Operations: Process large datasets efficiently with minimal memory usage.
• Easy Integration: Designed for straightforward integration with your .NET applications.
• Data Transformation: Transform and clean data seamlessly before loading it to your target destination.

💻 System Requirements

• Operating System: Windows 10 or later, macOS Sierra or later, or any Linux distribution.
• Processor: 1 GHz or faster.
• RAM: Minimum of 2 GB recommended.
• .NET Runtime: Version 5.0 or later.

📄 Usage

To use DataFlow effectively, follow these steps:

1. Initialization: Start by creating a new DataFlow instance in your .NET application.

2. Load Data: Use the provided methods to load data from your chosen source (CSV, JSON, etc.).

3. Transform Data: Apply any transformations or cleaning operations necessary for your application.

4. Store Data: Finally, save or load your processed data into your desired target.

Example

Here’s a simple example of how to use DataFlow in your application:

using DataFlow;

var dataPipeline = new DataFlowPipeline();
dataPipeline.LoadData("path/to/your/file.csv");
dataPipeline.TransformData();
dataPipeline.SaveData("path/to/destination/file.json");

📊 Support & Resources

• Documentation: Comprehensive documentation is available at DataFlow Wiki.
• Community: Join our community on GitHub Discussions for support and feature requests.

📞 Contact

If you have questions or need assistance, please open an issue on our GitHub page for help.

🔗 Important Links

• DataFlow Releases
• GitHub Repository
• .NET Runtime Download

Thank you for choosing DataFlow for your data processing needs! Enjoy smooth and efficient data operations.