What’s next for dlt in 2025: a simpler solution for solving complex problems` The author of the text, Marcin Rudolf, Co-Founder and CTO of `dlt`, discusses the future development plans for the `dlt` library. Since its initial release, `dlt` has grown rapidly, with over 3,000 production deployments and 1.4 million monthly PyPI downloads. The author believes that the increasing adoption of Large Language Models (LLMs) and Python-friendly infrastructure will make `dlt` more accessible to regular Python users while still supporting advanced use cases. To achieve this, they plan to transfer data engineering knowledge into `dlt`, making it simpler for users to write code and perform data engineering tasks. The author also highlights the importance of increasing the quality of life for users, focusing on features such as self-explanatory error messages, meaningful warnings, and intuitive ways to import dependencies. Additionally, they plan to develop specialized assistants for various data engineering tasks, such as exploration and enrichment of raw data, data modeling, and observability. The development roadmap for 2025 is shaped by user interest and traction, with a focus on supporting the building of data platforms, particularly with lakehouse architectures, high-performance Python libraries, and open table formats.