AWS Data Pipeline vs Azure Machine Learning: What are the differences?
AWS Data Pipeline: Process and move data between different AWS compute and storage services. AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email; Azure Machine Learning: A fully-managed cloud service for predictive analytics. Azure Machine Learning is a fully-managed cloud service that enables data scientists and developers to efficiently embed predictive analytics into their applications, helping organizations use massive data sets and bring all the benefits of the cloud to machine learning.
AWS Data Pipeline and Azure Machine Learning are primarily classified as "Data Transfer" and "Machine Learning as a Service" tools respectively.
Some of the features offered by AWS Data Pipeline are:
- You can find (and use) a variety of popular AWS Data Pipeline tasks in the AWS Management Console’s template section.
- Hourly analysis of Amazon S3‐based log data
- Daily replication of AmazonDynamoDB data to Amazon S3
On the other hand, Azure Machine Learning provides the following key features:
- Designed for new and experienced users
- Proven algorithms from MS Research, Xbox and Bing
- First class support for the open source language R