A Data Orchestrator models dependencies between different tasks in complex heterogeneous cloud environments end-to-end. It handles integrations with legacy systems, new cloud-based tools, and your data lakes and data warehouses. It invokes computation, such as wrangling your business logic in SQL and Python and applying ML models at the right time based on a time-based trigger or by custom-defined logic.
More Insights in Data Orchestration Trends: The Shift from Data Pipelines to Data Products.
What is the relationship of data pipelines and data products