Transferring data between APIs and databases is a complex task that involves building an Extract, Transform, and Load (ETL) pipeline with considerations such as pagination, API rate limits, and fault tolerance to prevent duplicate records and optimize compute usage. Using HelpScout's List Conversations API as an example, the guide outlines how to retrieve, transform, and save records using Pipedream workflows, emphasizing the role of pagination strategies, either page-based or cursor-based, in ensuring efficient data handling. It highlights the importance of using Pipedream's Data Stores for tracking the progress of pagination, managing API rate limits, and optimizing memory usage to prevent exhaustion. The guide also compares the use of workflow triggers for new records and explains why batching multiple records may provide better optimization and compute efficiency for ETL processes. Readers are encouraged to experiment with ETL pipeline building on Pipedream's platform, which offers a free plan with no credit card requirement, allowing users to build from templates or create custom workflows.