> For the complete documentation index, see [llms.txt](https://upsolver.gitbook.io/content/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://upsolver.gitbook.io/content/reference-1/learning-paths.md).
# Learning Paths
Upsolver offers a low-code solution for ingesting data from your source into a target warehouse or lake. Using familiar SQL syntax, it is easy to create the entities you need. While there are differences in connection and job options between the various data sources and targets, the code required for each path shares common features.
Each learning path starts with the source data that you want to ingest. After you have configured AWS, please follow the learning path for your data source, which will guide you through the steps to enable access to create the relevant connections and jobs for your intended target.
***
## Configure AWS
Regardless of your source and target, you must configure AWS prior to ingesting data. Please see the following guide for instructions. Our [technical support team](/content/support-1/support.md) is on hand if you need help.
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## Learning Paths
Select your source to begin your data ingestion journey:
### Suggested Reading
Upsolver makes coding pipelines easy. While you concentrate on writing bespoke jobs to move your data, we take care of schema evolution, data type changes, duplicates, and bad data, all while ensuring fresh, strongly-ordered data.
We recommend that you understand how these features work under the hood, so we deliver the results you expect:
* [Core Components](/content/articles-1/get-started/core-concepts/core-components.md)
* [Pipeline Basics](/content/articles-1/get-started/pipeline-basics.md)
* [Understanding Sync and Non-Sync Jobs](/content/articles-1/get-started/understanding-sync-and-non-sync-jobs.md)
***
### Further Learning
Enhance your jobs by exploring Upsolver's special features, which will help you deliver high-quality data to your destination:
#### Data Quality
* [Column Transformations](/content/articles-1/data/column-transformations.md)
* [Expectations](/content/articles-1/data/expectations.md)
#### Observability
* [Datasets](/content/reference-1/monitoring/datasets.md)
* [Job Monitoring](/content/reference-1/monitoring/system-catalog/monitoring.md)
* [Job Status](/content/reference-1/monitoring/job-status.md)
---
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