Data professionals often face the challenge of switching between various tools and rewriting code for different environments, which leads to inefficiency and frustration. The dlt datasets offer a solution by providing a consistent interface for data processing across multiple platforms such as DuckDB, BigQuery, and Snowflake, enabling code to work seamlessly regardless of the environment. This approach allows for local development and testing and easy migration to production environments without altering the core code, thus optimizing costs and enhancing productivity. dlt leverages open-source projects like ibis and sqlglot to provide a uniform interface for both SQL and Python users, integrating specialized tools into a cohesive experience. While there are limitations, such as the need to understand the data environment and focusing mainly on structured data, dlt presents a practical method to streamline data engineering tasks, encouraging users to start small and gradually expand its use.