Results for “dbt-core”

Provides guidance and best practices for working with dbt, covering core concepts, patterns, troubleshooting, and deployment strategies.

dbt (data build tool) transforms data in your warehouse using SQL SELECT statements. Learn project setup, models, tests, documentation, incremental materializations, and integration with data warehouses like PostgreSQL, BigQuery, and Snowflake.

Transforms data in warehouses using dbt with SQL models, tests, and documentation. Use for analytics engineering and data transformation.

Manages and monitors dbt projects, model runs, and test results via dbt CLI and dbt Cloud API, covering run status, test failures, source freshness, and manifest analysis.

SQL with dbt. SQL queries.

Use for creating, reviewing, debugging, or testing dbt SQL projects involving models, sources, ref, tests, macros, materializations, incremental models, snapshots, seeds, selectors, compiled SQL, or dbt artifacts. Do not use for generic SQL with no dbt graph, warehouse administration, semantic-layer product configuration, or orchestration outside dbt.

Provides dbt patterns for data transformation and analytics engineering, including model structures, incremental models, and testing.

Transforms raw data into analytics-ready models using dbt, covering models, tests, macros, sources, snapshots, documentation, and packages.

Get best practices for Entity Framework Core, covering data context design, entity design, performance, migrations, querying, change tracking, security, and testing.

Generate D2 database diagrams from Entity Framework Core models by analyzing DbContext, entities, Fluent API, and migrations.

Guides the setup and use of dbt for extract-transform-load workflows.

Apache Airflow, dbt, Prefect, Dagster, and modern data orchestration for production data pipelines

Use for writing, reviewing, debugging, testing, or optimizing Python code that embeds DuckDB, executes analytical SQL, manages DuckDB connections and transactions, builds DuckDB relations, queries Parquet/CSV/Arrow/pandas/Polars inputs, or exports query results. Trigger on connection scope, parameters, replacement scans, materialization, concurrency, extensions, and query plans. Do not use for generic SQL with another engine, DuckDB CLI-only work, server-database administration, dbt-only projects, or dataframe work that does not call DuckDB.

Guides OLTP database schema design through six stages: domain and access patterns, entities and relationships, keys and constraints, normalization trade-offs, indexing and performance, and operations and evolution. Includes a final review checklist and tips for effective guidance.