Data Orchestration Vs Data Orchestrator

Data Orchestration refers to the process of coordinating and managing the movement, transformation, and processing of data within an organization’s data ecosystem. It involves the automation and coordination of various data-related tasks, such as data ingestion, data transformation, data quality checks, data integration, and data delivery.

Data Orchestration ensures that data flows smoothly across different systems, applications, and processes, enabling data-driven workflows and facilitating the availability of accurate and timely data for analysis and decision-making.

Data Orchestrator refers to a technology or tool that facilitates the management and orchestration of data workflows. It is a software component or platform that provides capabilities for designing, scheduling, executing, and monitoring data pipelines and workflows.

A Data Orchestrator typically offers features such as workflow design and configuration, scheduling and triggering of data tasks, dependency management, data transformation capabilities, monitoring and logging of data pipelines, and integration with various data sources and systems.

Data Orchestrators often provide a graphical interface or a programming interface that allows users to define and configure data workflows, specify task dependencies, and set up scheduling rules. They automate the execution of data tasks based on the defined workflows and ensure the smooth flow of data across the data ecosystem.

Data orchestration is the process of gathering siloed data from various locations across the company, organizing it into a consistent, usable format, and activating it for use by data analysis tools.

And this seems to be a data orchestrator is (from Apache Airflow):

Workflow management platform for data engineering pipelines. It started at Airbnb in October 2014 as a solution to manage the company’s increasingly complex workflows. Creating Airflow allowed Airbnb to programmatically author and schedule their workflows and monitor them via the built-in Airflow user interface.

It is fair to say thus:

1. That data orchestration is the high level process of acquiring data from different locations, combining them, transforming them, and getting them ready for analysis
2. A data orchestrator is more of a workflow management tool that handles coordinating all the different steps in the pipeline (like Fivetran, kafka, dbt, great expectations, DB management, etc.). The data orchestrator doesn’t really do the data orchestration work — it just schedules and is workflow management for all the different modules in the MDS.

In summary, Data Orchestration is the broader concept of managing data workflows and processes, while a Data Orchestrator is a specific tool or technology that facilitates the automation and management of data pipelines and workflows. A Data Orchestrator is used to implement Data Orchestration practices within an organization’s data infrastructure.