Data Dependency Graph
A Data Dependency Graph is a directed graph representation that models dependencies between data elements, such as variables, functions, or tasks, in a system. It visualizes how data flows and is transformed, with nodes representing data entities and edges indicating dependencies (e.g., one element depends on another for computation). This concept is widely used in fields like compiler design, data engineering, and workflow management to analyze and optimize data processing.
Developers should learn about Data Dependency Graphs when working on systems that involve complex data transformations, such as in ETL pipelines, build systems, or parallel computing, to identify bottlenecks, ensure correct execution order, and enable optimizations like parallelization. It is crucial for tasks like deadlock detection, scheduling in distributed systems, and implementing incremental builds in tools like Make or Apache Airflow.