Define dbt sources in a sources.yaml entry named eth with schema eth_schema and tables contracts, token transfers, and transactions. Test source with a test model and verify lineage in dbt.

Create an enriched transactions model in dbt by selecting hash, block number, date, from and to addresses, value, receipt contract address, and input from the transactions source, aliased as t.

Learn how incremental models handle adding a new field with on schema change parameters, ignoring, failing, or syncing all columns, and when to use a full refresh to backfill records.

Convert business logic into performant sql queries by building simple, single-task dbt models that follow dry principles, reuse blocks, and apply cte-based joins and efficient materializations.

Master dbt run with full project execution, select specific models using dash dash select, and use empty and fail fast flags to validate and speed up development.

Define schema tests in dbt by creating a schema.yaml for models, applying not null and unique checks on the transactions hash column, and running dbt test to verify data quality.

Learn how to use dbt seed to load a stablecoins csv into a seeds table, then join it to your activity per day model and apply dimension-only seed best practices.

Master the dbt_project.yml as the single source of truth and control center for your dbt project, governing models, tests, seeds, and macros.

Explore alias configuration in dbt_project.yml and model-level aliases to materialize models with meaningful warehouse names; leverage variables and jinja for dynamic token activity per day.

Learn to use dbt packages, focusing on the code gen package from dbt hub. Install via packages.yaml, manage dependencies with dbt devs, and generate sources and models using macros.

Simulate a merge conflict when multiple developers edit the same dbt project yaml; learn to resolve conflicts locally or via remote pull requests, choosing current or incoming changes.

Create a prod environment in profiles.yaml and use target.name to tailor database and schema per environment, then dbt compile and run to limit data in dev or prod.

Use the codegen macro to generate model yaml for dbt, define schemas in schema.yaml, and enforce contracts true to lock column names and data types for models exposed to customers.

Explore dbt model access through groups, using private, protected, and public policies to control which models reference others, demonstrated by analytics engineering and fraud platform groups configured in YAML.