Overview
In this 1-hour interactive session, we walked through how teams can move from raw files in S3 to actionable insights using a lightweight, modern analytics stack built on dbt, DuckDB, DuckLake, Datacoves, and MotherDuck—without the operational overhead of traditional data platforms.
The workshop demonstrated an end-to-end workflow, covering ingestion, transformation, and analysis, and highlighted how this approach reduces complexity while improving developer velocity.
Key Takeaways
- How to connect directly to S3 and build dbt models on DuckLake
- Why dbt + DuckDB + Datacoves enables fast, flexible analytics without rigid infrastructure
- Ways teams can lower cost and operational burden by avoiding always-on warehouses
- Real-world patterns showing how analysts and analytics engineers iterate faster with local-first, cloud-enabled tooling
Who This Was Most Relevant For
- Data and analytics engineers looking to simplify pipelines
- BI developers and architects exploring modern warehouse alternatives
- Teams experimenting with leaner stacks for faster time-to-insight
The session reinforced that powerful analytics doesn’t require massive budgets or complex infrastructure, just the right combination of tools and a streamlined workflow.
