spb/tools-spboucher-ai
Public
1{2 "aliases": [3 "dbt Core",4 "data build tool",5 "dbt Fusion engine",6 "dbt platform (dbt Cloud)"7 ],8 "workflows": [9 "Model a warehouse from raw loaded tables to marts",10 "Local development against DuckDB, then deploy to a warehouse",11 "Schedule dbt runs from Airflow, Dagster or Prefect",12 "Enforce data tests in CI before merging model changes"13 ],14 "advantages": [15 "Software-engineering practices (version control, tests, docs) for SQL",16 "Large community, packages and adapter ecosystem",17 "Apache 2.0 Core runs anywhere",18 "Runs on the warehouse's own compute"19 ],20 "limitations": [21 "Transformation only; extraction and loading need other tools",22 "Primarily SQL; Python models limited to certain platforms",23 "Core v1 to v2 and Fusion transition in progress",24 "Advanced collaboration features are in the paid platform"25 ],26 "prerequisites": [27 "A supported warehouse or database and credentials",28 "Python for dbt Core v1, or the Fusion/Core v2 binary"29 ],30 "audiences": [31 "Analytics engineers",32 "Data teams building warehouse models"33 ],34 "alternatives": [35 "apache-airflow",36 "dagster",37 "prefect",38 "duckdb",39 "apache-spark"40 ],41 "tags": [42 "sql",43 "elt",44 "analytics-engineering",45 "open-source",46 "cli",47 "python",48 "rust"49 ],50 "use_cases": [51 "transform-data",52 "test-data-pipelines"53 ],54 "operating_systems": [55 "macos",56 "linux",57 "windows"58 ],59 "deployment_models": [60 "on-premises",61 "saas"62 ],63 "integrations": [64 "Snowflake",65 "BigQuery",66 "Databricks",67 "Amazon Redshift",68 "PostgreSQL",69 "DuckDB",70 "ClickHouse",71 "Apache Airflow",72 "Dagster",73 "Prefect",74 "Fivetran"75 ],76 "data_formats": [77 "SQL",78 "YAML",79 "CSV seeds",80 "JSON artifacts",81 "Parquet artifacts (v2)"82 ],83 "languages": [84 "SQL",85 "Jinja",86 "Python",87 "Rust"88 ],89 "self_hostable": true,90 "discovery_method": "editorial-research",91 "depth": "deep",92 "editorial_status": "reviewed",93 "visibility": "public",94 "featured": false,95 "author": "batch-D",96 "slug": "dbt",97 "name": "dbt",98 "vendor": {99 "name": "dbt Labs (Fivetran + dbt Labs)",100 "slug": "dbt-labs",101 "website": "https://www.getdbt.com/"102 },103 "homepage_url": "https://www.getdbt.com/",104 "docs_url": "https://docs.getdbt.com/",105 "repo_url": "https://github.com/dbt-labs/dbt",106 "license_url": "https://github.com/dbt-labs/dbt/blob/main/LICENSE",107 "pricing_url": "https://www.getdbt.com/pricing",108 "pricing_checked_at": "2026-11-02",109 "product_type": "framework",110 "product_status": "active",111 "summary": "SQL-first transformation framework that compiles templated models, tests and documentation into warehouse tables; open-source Core plus a commercial platform",112 "description": "dbt (data build tool) is a transformation framework from dbt Labs in which analysts write SELECT statements as versioned models, and the tool compiles them with Jinja templating, resolves dependencies into a DAG, materializes tables, views or incremental tables in the target warehouse, and runs schema and data tests alongside generated documentation and lineage. It performs the T of ELT: data already loaded into Snowflake, BigQuery, Databricks, Redshift, PostgreSQL, DuckDB or ClickHouse is reshaped in place through adapters, and dbt itself holds no data.\n\ndbt Core is the open-source command-line tool under Apache 2.0. In 2025 dbt Labs introduced the Rust-based dbt Fusion engine, and in June 2026 announced dbt Core v2 as an alpha built on the same open-sourced foundations, with the dbt-core repository (now github.com/dbt-labs/dbt) as the canonical home and the Fusion binary distributed free with some proprietary components. The company completed its merger with Fivetran on 1 June 2026. The commercial dbt platform (formerly dbt Cloud) adds a hosted IDE, scheduling, CI, a semantic layer and the Catalog, with free developer, team and enterprise plans.\n\ndbt is the de facto standard for modeling layers in analytics engineering and pairs naturally with Airflow, Dagster or Prefect for orchestration and with DuckDB for local development. Limitations: it is SQL-centric (Python models exist only on some warehouses), it does not move data, large projects can have long parse and run times on v1, and the fast-moving Core v1/v2 and Fusion transition means teams must track which engine and adapter versions they run.",113 "capabilities": [114 "SQL models with Jinja templating and ref() dependency resolution",115 "Materializations: table, view, incremental, snapshot, seed",116 "Schema and data tests, unit tests and source freshness checks",117 "Generated documentation and lineage graph",118 "Adapters for Snowflake, BigQuery, Databricks, Redshift, PostgreSQL, DuckDB, ClickHouse and more",119 "Packages, macros and semantic-layer metrics",120 "Hosted scheduling, IDE and CI in the dbt platform"121 ],122 "comparison_notes": "Airflow, Dagster and Prefect are orchestrators that schedule and monitor pipelines, including dbt runs; dbt itself only defines and executes transformations inside the warehouse. Spark is a general compute engine that can perform the same transformations in code rather than templated SQL. DuckDB is a frequent local target for dbt projects. Dagster in particular models dbt assets natively.",123 "categories": [124 {125 "slug": "etl-elt",126 "primary": true127 },128 {129 "slug": "analytics-engines"130 }131 ],132 "execution_modes": [133 "local",134 "self-hosted",135 "cloud"136 ],137 "interfaces": [138 "cli",139 "web",140 "api"141 ],142 "source_model": "mixed",143 "license_spdx": "Apache-2.0",144 "pricing_model": [145 "open-source",146 "freemium",147 "paid",148 "enterprise"149 ],150 "sources": [151 {152 "url": "https://www.getdbt.com/",153 "type": "official_site",154 "supports": [155 "name",156 "summary",157 "vendor"158 ],159 "checked_at": "2026-11-02",160 "title": "dbt Labs"161 },162 {163 "url": "https://docs.getdbt.com/",164 "type": "official_docs",165 "supports": [166 "capabilities",167 "integrations"168 ],169 "checked_at": "2026-11-02",170 "title": "dbt Developer Hub"171 },172 {173 "url": "https://docs.getdbt.com/blog/dbt-core-v2-is-here",174 "type": "release_notes",175 "supports": [176 "product_status",177 "license_spdx",178 "repo_url",179 "vendor"180 ],181 "checked_at": "2026-11-02",182 "title": "dbt Core v2 is here (June 2026)",183 "note": "States Core v2 is Apache 2.0 in the dbt-core repository and refers to the combined Fivetran + dbt Labs team"184 },185 {186 "url": "https://github.com/dbt-labs/dbt/blob/main/LICENSE",187 "type": "license_page",188 "supports": [189 "license_spdx",190 "source_model"191 ],192 "checked_at": "2026-11-02",193 "title": "dbt LICENSE (Apache 2.0)",194 "note": "github.com/dbt-labs/dbt-core redirects to this repository"195 },196 {197 "url": "https://www.getdbt.com/pricing",198 "type": "pricing_page",199 "supports": [200 "pricing_model"201 ],202 "checked_at": "2026-11-02",203 "title": "dbt pricing"204 },205 {206 "url": "https://www.fivetran.com/press/fivetran-dbt-labs-complete-merger-to-create-the-data-infrastructure-for-trusted-ai-agents",207 "type": "news",208 "supports": [209 "vendor"210 ],211 "checked_at": "2026-11-02",212 "title": "Fivetran and dbt Labs complete merger (press release)"213 }214 ],215 "confidence": "high",216 "uncertainty_note": "Fusion engine licensing for its proprietary components was not reviewed in detail; license_spdx applies to dbt Core."217}218