19. Data Transformations with dbt (data build tool): Modular SQL Models, Jinja Macros, Materializations & Testing • Data Engineering, Big Data & ETL Infrastructure Architecture • Rishabh Ki Kaksha

Data Engineering, Big Data & ETL Infrastructure Architecture › 19. Data Transformations with dbt (data build tool): Modular SQL Models, Jinja Macros, Materializations & Testing

50 mins

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Course Syllabus

Module 1: Modern Data Engineering Architecture & The Data Stack

01. The Modern Data Stack: OLTP vs OLAP, ETL vs ELT & Batch vs Stream Processing Architecture 02. Dimensional Modeling: Kimball Methodology, Star Schema, Snowflake Schema & Fact vs Dimension Tables 03. Columnar Storage Formats: Apache Parquet, ORC, Snappy Compression & Dictionary Encoding Internals

Module 2: Distributed Processing with Apache Spark & PySpark

04. Apache Spark Core Architecture: Driver, Executors, RDDs, DAG Scheduler & Spark Catalyst Optimizer 05. High-Performance PySpark DataFrames: Transformations (Narrow vs Wide), Actions & Lazy Evaluation 06. Spark Optimization Masterclass: Partitioning, Coalesce vs Repartition, Broadcast Joins & Skew Data Handling 07. Spark Structured Streaming: Event-Time Processing, Watermarking & Windowed Aggregations

Module 3: Real-Time Event Streaming with Apache Kafka

08. Apache Kafka Architecture: Brokers, Topics, Partitions, Replicas, ISR (In-Sync Replicas) & KRaft Consensus 09. Kafka Producers & Consumers: Partitioning Keys, Consumer Groups, Rebalancing & Offset Commit Management 10. Schema Evolution & Governance: Confluent Schema Registry with Apache Avro and Protobuf Schemas

Module 4: Modern Data Lakehouse Architecture (Delta Lake, Apache Iceberg)

11. Data Lakehouse Architecture: Evolution from Data Lakes (S3/HDFS) to ACID Lakehouses 12. Delta Lake Architecture: Transaction Log (_delta_log), Time Travel, Schema Enforcement & ACID Guarantees 13. Apache Iceberg: Metadata Layer, Hidden Partitioning, Snapshot Isolation & Compaction Optimization 14. The Medallion Architecture: Bronze (Raw Ingestion) -> Silver (Enriched) -> Gold (Business Aggregates)

Module 5: Workflow Orchestration with Apache Airflow & Dagster

15. Apache Airflow Core: DAGs, Operators, Sensors, Task Execution Engine & Celery/Kubernetes Executors 16. Production Airflow Best Practices: Idempotent Tasks, Dynamic DAG Generation, Backfilling & SLA Alerts 17. Modern Data Orchestration with Dagster: Software-Defined Assets, Declarative Scheduling & Type Safety

Module 6: Cloud Data Warehousing (Snowflake / BigQuery) & dbt Transformations

18. Cloud Data Warehouses: Snowflake Multi-Cluster Shared Data Architecture vs Google BigQuery Dremel Engine 19. Data Transformations with dbt (data build tool): Modular SQL Models, Jinja Macros, Materializations & Testing 20. End-to-End Enterprise Data Pipeline Project: Kafka -> Spark -> Delta Lake -> dbt -> Snowflake Dashboard

Interactive Lesson Studio

# 19. Data Transformations with dbt (data build tool): Modular SQL Models, Jinja Macros, Materializations & Testing

> Data Engineering & Big Data Notes by Rishabh - Topic 19

---

### 1. The dbt Transformation Paradigm (Software Engineering for SQL 🛠️)

No more 2000-line monolithic stored procedures!  
**dbt:** Modular `SELECT` statements with Git version control, automated testing, and DAG dependency compilation:

```sql
-- models/silver/fct_course_completions.sql
{{ config(materialized='incremental', unique_key='completion_id') }}

SELECT
    completion_id,
    user_id,
    course_id,
    completed_at,
    {{ get_course_category('course_id') }} AS category
FROM {{ ref('stg_completions') }}

{% if is_incremental() %}
    WHERE completed_at > (SELECT MAX(completed_at) FROM {{ this }})
{% endif %}
```

---

### 2. Automated Schema & Data Quality Testing (`schema.yml`)

```yaml
version: 2
models:
  - name: fct_course_completions
    columns:
      - name: completion_id
        tests:
          - unique
          - not_null
      - name: user_id
        tests:
          - relationships:
              to: ref('dim_users')
              field: id
```

---

### 3. Master Comparison Matrix

---

### 4. Interview Ke 3 Sawal Is Topic Se

1. **`{{ ref('model_name') }}` Jinja macro dbt me kya karta hai?** -> Table dependency DAG construct karta hai aur target environment (dev/prod schemas) ke anusaar correct database table path interpolate karta hai.
2. **dbt Snapshots (SCD Type 2) historical changes kaise track karte hain?** -> `check_cols` ya `updated_at` timestamps monitor karke `dbt_valid_from` aur `dbt_valid_to` columns manage karna.
3. **dbt Semantic Layer kya hota hai?** -> Metrics (e.g. `monthly_active_users`, `revenue_growth`) ko centralized define karna taaki har BI tool (Tableau, Looker) same exact KPI formula query kare.

---
**Next Topic:** 20. End-to-End Enterprise Data Pipeline Project: Kafka -> Spark -> Delta Lake -> dbt -> Snowflake Dashboard

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