In Databricks, what do you use to orchestrate data analytics pipelines?

In Databricks, orchestrating data analytics pipelines is primarily accomplished through the use of jobs. Jobs allow users to run complex workflows that can involve multiple tasks, which could include running notebooks, submitting Spark jobs, or performing data transformations. This orchestration capability is essential for automating and scheduling data workflows, ensuring that each component runs in the correct sequence and can handle dependencies effectively.

Using jobs enables users to set up periodic tasks, manage job execution and monitoring, and handle failures or retries automatically. This is particularly useful in a production environment where reliability and automation are critical.

While notebooks play an important role in developing and testing data transformations and algorithms, they are typically more focused on interactive tasks rather than orchestrating complete pipelines. Workspaces serve as the organizational layer for Databricks assets and do not directly manage the execution of tasks. Dataframes, on the other hand, are a fundamental data structure used for handling large datasets and are integral to data manipulation within the context of data analytics, but they do not have orchestration capabilities.

Thus, jobs are the key tool in Databricks for orchestrating data analytics pipelines, making them the correct choice in this scenario.