Most teams treat data ingestion as a solved problem: pick a managed connector like Estuary, Fivetran, or a cloud tool (Glue, DLT) and move on. These platforms handle schema changes, retries, rate limits and backfills out of the box, so your engineers don’t spend nights patching broken scripts. Only build custom ingestion if a source really has no connector or if you can’t afford a commercial option. On the transformation side, SQL still rules. Whether you use dbt, stored procedures or Airflow DAGs, you’ll write most logic in SQL. Running dbt directly inside Snowflake cuts out extra tooling, and Databricks’ pipelines give you a unified interface (even if it feels like Python notebooks everywhere). AI is beginning to accelerate model building—think auto-generated HubSpot schemas or template-based joins—but imperfect upstream data and oddball workflows still demand hands-on cleanup. AI tools for analytics and chatbots look exciting but often require nearly as much setup as traditional dashboards. Many teams are instead building lightweight CLI or skills-file integrations with Claude or Codex to automate repetitive tasks. Even so, you can’t skip fundamentals: define what “good data” means (accuracy, freshness, coverage), set SLAs for loading and fixing broken feeds, and agree on ownership before anyone chases AI-driven insights. Finally, match your storage and compute choice to your actual workload. Not every org needs a lakehouse or sophisticated warehouse. Sometimes a read replica and some plain SQL are enough. Think about team size, business questions and growth plans before architecting for hypothetical scale. That way you avoid trendy infrastructure with no real payoff.