doc(readme): add introduction.

README.md

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-# sparkle
+# Sparkle ✨
+
+**Sparkle** is a meta-framework built on top of [Apache
+Spark](https://spark.apache.org/), designed to streamline data
+engineering workflows and accelerate the delivery of data
+products. Developed by [**DataChef**](https://datachef.co), Sparkle
+focuses on three main areas:
+
+1. **Improving Developer Experience (DevEx) 🚀**
+2. **Reducing Time to Market ⏱️**
+3. **Easy Maintenance 🔧**
+
+With these goals in mind, Sparkle has enabled DataChef to deliver
+functional data products from day one, allowing for seamless handovers
+to internal teams.
+
+## Key Features
+
+### 1. Improved Developer Experience 🚀
+
+Sparkle enhances the developer experience by abstracting away
+non-business-critical aspects of Spark application development. It
+achieves this through:
+
+- **Sophisticated Configuration Mechanism**: Simplifies the setup and
+  configuration of Spark applications, allowing developers to focus
+  solely on business logic.
+- **Automatic Functional Tests 🧪**: Generates tests for each
+  application automatically, based on predefined input and output
+  fixtures. This ensures that the application behaves as expected
+  without requiring extensive manual testing.
+
+### 2. Reduced Time to Market ⏱️
+
+Sparkle significantly reduces the time to market by automating the
+deployment and testing processes. This allows data engineers to
+concentrate exclusively on developing the business logic, with all
+other aspects handled by Sparkle:
+
+- **Automated Testing ✅**: Ensures that all applications are robust
+  and ready for deployment without manual intervention.
+- **Seamless Deployment 🚢**: Automates the deployment pipeline,
+  reducing the time needed to bring new data products to market.
+
+### 3. Enhanced Maintenance 🔧
+
+Sparkle simplifies maintenance through heavy testing and abstraction
+of non-business functional requirements. This provides a reliable and
+trustworthy system that is easy to maintain:
+
+- **Abstraction of Non-Business Logic 📦**: By focusing on business
+  logic, Sparkle minimizes the complexity associated with maintaining
+  Spark applications.
+- **Heavily Tested Framework 🔍**: All non-business functionalities
+  are thoroughly tested, reducing the risk of bugs and ensuring a
+  stable environment for data applications.
+
+## How It Works 🛠️
+
+The Sparkle framework operates on a principle similar to Function as a
+Service (FaaS). Developers can instantiate a Sparkle application that
+takes a list of input DataFrames and focuses solely on transforming
+these DataFrames according to the business logic. The Sparkle
+application then automatically writes the output of this
+transformation to the desired destination.
+
+## Getting Started 🚀
+
+Sparkle is currently under heavy development, and we are continuously
+working on improving and expanding its capabilities.
+
+To stay updated on our progress and access the latest information,
+follow us on [LinkedIn](https://nl.linkedin.com/company/datachefco)
+and [GitHub](https://github.com/DataChefHQ/Sparkle).
+
+## Contributing 🤝
+
+We welcome contributions from the community! If you're interested in
+contributing to Sparkle, please check our [GitHub
+repository](https://github.com/DataChefHQ/Sparkle) for more details on
+how you can get involved.
+
+## License 📄
+
+Sparkle is licensed under the Apache v2.0 License. See the
+[LICENSE](LICENSE) file for more details.
+
+## Contact 📬
+
+For more information, questions, or feedback, feel free to reach out
+to us on [LinkedIn](https://nl.linkedin.com/company/datachefco) or
+open an issue on our
+[GitHub](https://github.com/DataChefHQ/sparkle/issues) repository.
+
+---
+
+Thank you for your interest in Sparkle! We're excited to have you join
+us on this journey to revolutionize data engineering with Apache
+Spark. 🎉