Practice and extend - Step 15 of 15
Recap and next steps
Review the workflow, then choose a larger project to build next.
Course complete
Build your next pipeline
Choose a project that adds a new data source, a new domain, or a more complete deployment workflow.
NYC Taxi Data Pipeline
Build ingestion, staging models, reports, and checks with real NYC taxi data and DuckDB.
Salesforce to Snowflake ELT
Build a Salesforce-to-Snowflake pipeline with Bruin Cloud, MCP, and a managed agent.
Shopify Data Pipeline
Build an ecommerce pipeline from source data through reporting and an AI analyst.
Local stock analyst
Build a local financial-data pipeline, then use it with an AI assistant.
What you completed
You built a local analytics project from source files through a reporting table. Along the way, you practiced how to:
- Define what one row means before writing SQL.
- Clean source data, model orders and customers, and build a daily revenue table.
- Add checks that catch missing values, duplicate rows, and unexpected status values.
- Rerun a selected date range without creating duplicates.
- Keep the metric definition, owners, and limitations in the reporting asset.
- Review a local change, prove the output, and extend the pipeline with returns.
Keep the workflow
Start each new project with a clear question, a small source sample, and a definition of the output row. Build one layer at a time, check the results against the source, and only then choose how to automate the runs.
The projects above give you more data sources and domains to practice with. Choose the one closest to the work you want to do next.