This paper presents a survey of coordination models and languages for concurrent and distributed computing. It classifies these models as either data-driven or control-driven, and discusses their features, applications, and differences. The paper begins by introducing the concept of coordination, which is central to managing the interaction between components in distributed and parallel systems. Coordination models and languages aim to provide a framework for integrating heterogeneous components, enabling them to work together in a coordinated manner. The paper discusses the evolution of coordination models, starting from multilingual and heterogeneous systems, and how coordination paradigms have emerged to address the challenges of developing complex distributed and parallel systems. It describes various coordination models and languages, including Linda, Bauhaus Linda, Bonita, Law-Governed Linda, Objective Linda, and LAURA. These models differ in their approaches to coordination, with data-driven models focusing on the flow of data and control-driven models focusing on the control of processes. The paper highlights the importance of coordination in enabling the development of large-scale applications that run on parallel and distributed systems. It discusses the characteristics of data-driven and control-driven models, their applications, and their differences. The paper concludes by emphasizing the need for further research and development in coordination models and languages to support the growing demands of modern computing systems.This paper presents a survey of coordination models and languages for concurrent and distributed computing. It classifies these models as either data-driven or control-driven, and discusses their features, applications, and differences. The paper begins by introducing the concept of coordination, which is central to managing the interaction between components in distributed and parallel systems. Coordination models and languages aim to provide a framework for integrating heterogeneous components, enabling them to work together in a coordinated manner. The paper discusses the evolution of coordination models, starting from multilingual and heterogeneous systems, and how coordination paradigms have emerged to address the challenges of developing complex distributed and parallel systems. It describes various coordination models and languages, including Linda, Bauhaus Linda, Bonita, Law-Governed Linda, Objective Linda, and LAURA. These models differ in their approaches to coordination, with data-driven models focusing on the flow of data and control-driven models focusing on the control of processes. The paper highlights the importance of coordination in enabling the development of large-scale applications that run on parallel and distributed systems. It discusses the characteristics of data-driven and control-driven models, their applications, and their differences. The paper concludes by emphasizing the need for further research and development in coordination models and languages to support the growing demands of modern computing systems.