# dbt Published articles for dbt. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## ClickHouse is now available on the dbt platform DevFeed: [ClickHouse is now available on the dbt platform]() Original publisher: [Read original article]() Author: Aditya Chidurala; José Muñoz; Alex Francoeur Published: 2026-09-16T19:38:40Z Content type: release Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Cloud](), [Rust](), [Open Source]() Tags: [adapter](), [announcements](), [beta](), [clickhouse](), [cloud](), [dbt](), [open-source](), [platform](), [release](), [rust]() ### AI overview ClickHouse announces a dbt v2 adapter powered by dbt's Rust engine, available in public beta. ClickHouse is also available as a natively supported data warehouse on dbt Platform in private beta, with support for open-source ClickHouse and ClickHouse Cloud. ### Source excerpt The ClickHouse adapter for dbt v2 is in public beta, powered by dbt's Rust engine. ClickHouse also joins the dbt platform in private beta, supporting open-source ClickHouse and ClickHouse Cloud. ## How dbt works, and why orchestrators shouldn't split it into tasks DevFeed: [How dbt works, and why orchestrators shouldn't split it into tasks]() Original publisher: [Read original article]() Author: Ruben Fiszel Published: 2026-08-20T00:00:00Z Content type: article Language: en Sources: [Windmill Blog]() Topics: [Compiler](), [Job](), [airflow](), [data](), [Command-line interface]() Tags: [airflow](), [bigquery](), [cli](), [dagster](), [data](), [data-pipelines](), [dbt](), [dbt-data-pipelines-airflow-dagster-orchestration](), [job](), [orchestration](), [scheduler](), [series]() ### AI overview This primer explains dbt's architecture and examines five ways teams orchestrate it in production. It argues that running a project as one dbt command, while reading dbt's per-model execution state, is generally more efficient and less fragile than creating one orchestrator task per model. It discusses Dagster and astronomer-cosmos's convergence on this approach, including a reported cost comparison. ### Source excerpt How is dbt actually orchestrated, and why does running it as one job beat one task per model? A primer on dbt as a compiler with a scheduler attached, the five ways teams wrap it, and why both Dagster and astronomer-cosmos converged on a single dbt invocation with per-model state projected out of it. ## Empowering Data Through Self-Service: Behind the Scenes of Our Data Platform DevFeed: [Empowering Data Through Self-Service: Behind the Scenes of Our Data Platform]() Original publisher: [Read original article]() Author: Karen Fehmer Published: 2025-06-02T03:53:58Z Content type: article Language: en Sources: [Kogan.com]() Topics: [data](), [Architecture & Design](), [BigQuery](), [CI/CD](), [GitHub Actions](), [Git]() Tags: [architecture](), [bigquery](), [ci-cd](), [data](), [dbt](), [github-actions](), [looker](), [self-service](), [tableau]() ### AI overview Kogan.com describes replacing a request-based BI model with a governed self-service data platform built around BigQuery, dbt, Looker, and Acryl (DataHub). The architecture uses layered models, GitHub Actions CI/CD, testing, documentation, and controlled environments to support trusted analysis and reporting. ### Source excerpt At Kogan.com, our data needs have grown alongside the business. As more teams relied on insights to move quickly, it became clear our request-based BI model couldn't scale. We needed a platform that empowered teams to answer their own questions, trust the numbers, and move independently. That journey led us to build a self-service platform grounded in governance, transparency, and scalability--powered by dbt, Looker, and Acryl (DataHub). Rethinking Our BI Model We originally relied on Tableau. It served us well but had limitations: duplicated logic, inconsistent metrics, and limited collaboration with dbt. Tableau workbooks weren't version-controlled, which made maintaining consistency difficult. To bridge modeling and reporting, we often created extra presentation tables in dbt, adding complexity. We needed a platform that integrated tightly with dbt and supported governed exploration. A New Architecture: Modular, Transparent, Scalable We redesigned the platform around a clean, modular flow: Raw Sources -> BigQuery -> dbt -> Looker -> Acryl (DataHub) Our data transformations are built in dbt, where we follow a layered modeling structure. While we use stg_ (staging) and int_ (intermediate) models primarily for data cleaning and standardization, the marts_ models are the ones that power our analysis and reporting. These models contain our fact and dimension tables, fully aligned with business logic and ready for consumption in Looker. We've integrated CI/CD pipelines using GitHub Actions, and every change is tested before deployment. This includes dbt tests, schema validations, and model documentation to ensure confidence at every layer. Why Looker Was the Right Fit for Self-Service Looker offered a structured, governed approach that aligned with our dbt-first architecture. LookML let us centralize business logic, version it with Git, and deploy changes through CI/CD. With support for multiple environments (UAT and Production), we can test safely before releasing to users