Interlace.sh Proposes a Single Abstraction to Unify Fragmented Data Pipelines

Modern data engineering stacks typically require multiple separate tools — such as dbt, Airflow, and dlt — each with its own configuration, testing framework, and mental model, creating fragile handoffs between layers. The team behind Interlace argues that these 'seams' between tools are the root cause of silent failures, such as schema drift going undetected or transformations running on stale data. Their solution, Interlace, treats everything — ingestion, transformation, and orchestration — as a single 'model' abstraction, whether written in SQL or Python. Dependencies are inferred automatically from query syntax or function parameters, eliminating the need for manual wiring or separate configuration files. The project draws a parallel to how general-purpose programming unified fragmented workflows through abstractions like functions and package managers, suggesting data engineering is roughly a decade behind on the same trajectory.