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5 Fantastic Data Pipeline Orchestration Tools For R
Explore Excellent Options for Data Pipeline Orchestration for R Users
The data pipeline orchestration tool is critical for producing healthy and reliable data-driven decisions. R is one of the popular languages for data scientists. With R’s exceptional packages, the R programming language is great for data manipulation, statistical analysis, and visualization.
One pattern that often brings data scientists’ R local script to production is to rewrite using Python or Scala (Spark), then schedule the data pipeline and model building via modern data pipeline orchestration tools like Apache Airflow.
However, many modern data orchestration projects like Apache Airflow, Prefect, and Luigi are Python-based. Can they work seamlessly with R? Can you write in R to define a DAG? In this article, let’s explore the popular data pipeline orchestration tool for R scripts and review which fits your use case.
The Key Components of the Successful Data Pipeline Orchestration
Data pipeline orchestration can be broken down into three main components from my experience: DAG (dependencies), Scheduler, and Plugins.
