Today, many organizations generate and store data in Software as a service (SaaS)-based applications. They want to ingest this data in a central repository (often a data lake) so that they can analyze it though a single pane of glass and derive insights from it. However, because different SaaS applications provide different mechanisms of extracting the data, a single approach does not always work.
This paper outlines different patterns using Amazon Web Services (AWS) services to ingest SaaS data into a data lake on AWS.
