Bump version to 0.9.3.1 in pyproject.toml and add tale_of_pydala2.md for project storytelling

Author: legout

pyproject.toml

@@ -23,7 +23,7 @@ name = "pydala2"
 readme = "README.md"
 repository = "https://github.com/legout/pydala2"
 requires-python = ">= 3.10"
-version = "0.9.3"
+version = "0.9.3.1"
 
 [project.optional-dependencies]
 legacy = ["polars-lts-cpu>=0.20.4"]

tale_of_pydala2.md

@@ -0,0 +1,31 @@
+## Tale of PyDala2
+Once upon a time in the bustling world of data engineering, there was a library named PyDala2. Born from the mind of a data enthusiast named Volker L., PyDala2 entered the scene on August 13, 2024, aiming to simplify the lives of those who dealt with large datasets daily. 
+
+### The Creation
+PyDala2 was designed with one vision: to streamline the interaction with Parquet datasets. This format, known for its efficiency in big data scenarios, often required complex handling. Volker L. saw an opportunity to make this interaction less daunting, leading to the creation of PyDala2. The library promised:
+
+ - Smart Dataset Management: To make handling datasets as intuitive as possible, allowing users to focus more on analysis than on data plumbing.
+ - Metadata Handling: To keep track of essential dataset information, making datasets not just data but also knowledge.
+ - Built-in Caching: To speed up repetitive tasks by storing data temporarily, reducing the need to reload data from disk or network every time.
+ - Integration with Polars/Arrow/DuckDB: Offering a bridge between different data computation tools, PyDala2 made it easy to switch contexts or leverage the strengths of different systems.
+
+
+### The Journey
+The tale of PyDala2 is one of integration and efficiency. With its release, data engineers and scientists found a friend in their quest for performance. PyDala2 was not just another library; it was a tool that understood the needs of its users:
+
+ - Ease of Use: It provided a straightforward API that allowed developers to manage Parquet datasets with less code and more efficiency.
+ - Advanced SQL-like Querying: It empowered users with the ability to query datasets in a familiar SQL-like language, thus bridging the gap between traditional databases and modern data formats.
+
+
+### The Community
+The library soon found a community of users who appreciated its simplicity and power. Discussions flourished around how PyDala2 could integrate with other tools, its potential in real-world applications, and its role in the future of data handling. 
+
+### The Future
+As PyDala2 grew, so did its capabilities:
+
+ - Version Updates: Regular updates kept the library current with the latest in data technology, ensuring that it remained relevant and effective.
+
+ - Community Support: The library's community grew, contributing ideas, bug fixes, and new features, making PyDala2 not just a tool but a community project.
+
+And so, PyDala2 continues its tale, evolving with each use case, adapting to new data challenges, and forever striving to make the life of data engineers and scientists just a little bit easier. The library, with its MIT license, remains open-source, inviting all who wish to contribute to its story.
+