Here are summaries of each of the tools you’ve mentioned along with examples of how to implement the ETL (Extract, Transform, Load) process using each tool within a Python workflow:
- Apache Spark: Apache Spark is a powerful open-source cluster-computing framework that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. It’s commonly used for processing large-scale data and running complex ETL pipelines. Example Implementation:
Read on for summaries and samples for each of the 21 options.