Data engineering
What makes a data pipeline operable?
A reliable pipeline needs explicit expectations, visible failures, repeatable recovery, and operating knowledge—not only transformation code.
Read the insightEngineering insights
Practical guidance grounded in inspectable work.
These notes explain engineering decisions, review questions, and operating practices Dagitali uses in its own projects. They are educational material, not client case studies or guarantees that one approach fits every system.
Current notes
Start with the system beneath the outcome.
Analytics engineering
What should a useful dbt model review examine?
Review model purpose, grain, dependencies, tests, documentation, materialization, and operating consequences as one connected system.
Read the insightCloud & delivery
Why use private S3, CloudFront, and GitHub OIDC?
A small static site can remain inexpensive while keeping its origin private, delivery repeatable, and deployment credentials short-lived.
Read the insightPublication standard
Evidence and limitations travel with the advice.
Insights distinguish general engineering guidance from behavior demonstrated by a specific Dagitali project. Dates make revisions visible, and links lead to public evidence when it exists.
What these notes are
- Company-authored technical guidance
- Explanations tied to current engineering practices
- Starting points for review and discussion
- Context for related Dagitali services
What these notes are not
- Dagitali client case studies
- Claims about work performed for former employers
- Substitutes for system-specific discovery
- Promises of a particular business result
Apply the guidance