Key features
- SQL-first transforms — APAC SELECT-based APAC model authoring without DDL
- Dependency graph — `ref()` APAC automatic lineage and execution order
- Data tests — built-in APAC not_null/unique/accepted_values + custom tests
- APAC multi-warehouse — BigQuery, Snowflake, Redshift, DuckDB adapters
- dbt docs — auto-generated APAC searchable data catalog site
- Jinja templating — APAC macros and dynamic APAC SQL generation
Best for
- APAC analytics engineering teams adopting ELT — dbt Core is the standard APAC analytics engineering tool; APAC teams using Fivetran/Airbyte for EL and wanting T in the APAC warehouse use dbt Core for APAC transformation layer
- APAC data teams wanting software engineering practices for APAC SQL — dbt's APAC version control, APAC testing, APAC documentation, and APAC code review workflow applies SE practices to APAC SQL transformation work
- APAC organizations building APAC self-service BI foundations — dbt's APAC documented, tested data models provide the APAC semantic layer that APAC BI tools (Looker, Metabase, Superset) reference for APAC consistent metrics
Limitations to know
- ! APAC Python-heavy transformations — dbt is SQL-first; APAC transformation logic that requires APAC Python (ML feature engineering, APAC complex statistical calculations) needs dbt Python models or a separate APAC processing layer
- ! APAC orchestration is external — dbt Core doesn't schedule APAC runs; APAC production dbt scheduling requires Airflow, Prefect, Dagster, or dbt Cloud; APAC teams need APAC orchestration tooling alongside dbt Core
- ! APAC large model runtime performance — dbt executes APAC SQL sequentially by default; APAC large dbt projects with hundreds of APAC models benefit from dbt Cloud's APAC parallel execution or Dagster/Prefect APAC orchestration with concurrency
About dbt Core
dbt Core is an open-source analytics engineering framework from dbt Labs that provides APAC data teams a software engineering approach to APAC data transformation — where APAC analytics engineers write APAC transformation logic as SQL SELECT statements in `.sql` model files (dbt handles the `CREATE TABLE AS SELECT` or `INSERT OVERWRITE` DDL), define APAC data tests in YAML (`not_null`, `unique`, `accepted_values`, `relationships` for referential integrity), and run `dbt run` to execute APAC transformations in the configured APAC data warehouse (BigQuery, Snowflake, Redshift, PostgreSQL, DuckDB).
dbt's APAC lineage — where dbt automatically infers APAC model dependencies from `ref()` and `source()` function calls in APAC SQL files (e.g., `FROM {{ ref('apac_stg_payments') }}`), builds a directed acyclic APAC graph of model dependencies, executes APAC models in correct dependency order, and generates APAC lineage documentation — provides APAC data teams automatic APAC data lineage from model references without separate APAC lineage tooling.
dbt's APAC testing framework — where APAC analytics engineers define APAC data quality tests in `schema.yml` files (asserting that `apac_payment_id` is `not_null` and `unique`, that `apac_status` values are within `['pending','completed','failed']`, that `apac_user_id` has a foreign key in `apac_users`) and run `dbt test` to execute tests against the APAC warehouse — provides APAC data teams automated APAC data quality gates that surface APAC upstream data issues before they propagate to APAC BI tools and APAC reports.
dbt's APAC documentation site — where `dbt docs generate` + `dbt docs serve` creates an APAC searchable data catalog site showing all APAC models, their APAC SQL definitions, APAC column descriptions, APAC lineage graph, and APAC test results — provides APAC organizations a self-generated APAC data documentation site that stays current with APAC model changes rather than requiring manual APAC documentation maintenance in Confluence or Notion.
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