# Airflow + dbt - Orkestrasi Pipeline (Data Engineering)

> astronomer-cosmos, DbtDagParser, dbt run/test di Airflow, manajemen dependensi, monitoring end-to-end

- 20 pertanyaan wawancara
- Senior
- [Pertanyaan Wawancara: Data Engineering](https://sharpskill.dev/id/technologies/data-engineering/pertanyaan-wawancara.md)

## 1. Apa keuntungan utama menggunakan astronomer-cosmos untuk mengintegrasikan dbt ke dalam Airflow?

**Jawaban**

Astronomer-cosmos secara otomatis mengonversi model dbt menjadi task Airflow individual, memberikan visibilitas granular pada setiap model di UI Airflow. Hal ini memungkinkan pemanfaatan fitur Airflow (retry, alerting, monitoring) di tingkat model alih-alih di seluruh proyek dbt.

## 2. Bagaimana cosmos menangani dependensi antara model dbt dalam DAG Airflow?

**Jawaban**

Cosmos menganalisis manifest.json dbt untuk mengekstrak graf dependensi antar model. Kemudian secara otomatis membuat hubungan dependensi (upstream/downstream) antar task Airflow yang sesuai, sehingga menghormati urutan eksekusi yang didefinisikan oleh refs di proyek dbt.

## 3. Apa perbedaan antara mode eksekusi 'local' dan 'docker' di cosmos?

**Jawaban**

Dalam mode local, cosmos menjalankan dbt langsung di lingkungan Python worker Airflow, mengharuskan dbt terinstal. Dalam mode docker, setiap task dbt berjalan dalam container Docker terisolasi dengan image dbt sendiri, memberikan isolasi dan reproduksibilitas dependensi yang lebih baik.

## 17 pertanyaan lagi tersedia

- Bagaimana cara mengkonfigurasi cosmos untuk menjalankan hanya sebagian model dbt berdasarkan tag?
- Apa peran DbtTaskGroup dalam integrasi Airflow-dbt dengan cosmos?

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