Advanced
These guides are for data engineers, architects, and administrators. They cover architecture decisions for complex, high-volume, or multi-team Mammoth implementations, and assume you are comfortable with core Mammoth functionality.
Advanced guides and when to use them
| Guide | What it covers |
|---|---|
| Enterprise architecture | Multi-team deployments, governance, roles at scale, workspace organization |
| Data validation | Quality gates, reconciliation checks, audit-ready pipeline design |
| Incremental processing | Large datasets, performance optimization, change data capture patterns |
| Multi-source joins | Joining datasets with mismatched keys, fuzzy matching, entity resolution |
| Custom transforms | Complex logic that standard tasks cannot express, using chaining, Generative AI, and AI SQL Query |
| Dataset workflows | Dataset versioning, branching, promotion from development to production |
| AI-powered workflows | Choosing between Generative AI, AI SQL Query, Bulk Replace and the AI Prompt bar, and running them in production |
| API workflows | Triggering Mammoth automations through the API, polling for completion, pushing results out |
| Pull data from a REST API | Connecting a REST or Elasticsearch source with the CLI, sampling it, creating a dataset and refreshing it on a schedule |
Warning: Advanced content
These guides assume proficiency with Mammoth's core features. If you are new to Mammoth, start with the use case guides before the advanced patterns.