dbt vs AWS Glue for AI Data Transformation

Data transformation is a critical step in AI pipelines: raw data must be cleaned, joined, aggregated, and shaped into features before models can use it. dbt and AWS Glue are popular tools for this work, but they approach the problem differently.

Platform Overview

dbt (data build tool) is a SQL-first transformation framework. It transforms data already loaded into a data warehouse (Redshift, Snowflake, BigQuery) using SQL SELECT statements. dbt handles dependency management, testing, documentation, and version control. Available as dbt Core (open source) or dbt Cloud (managed).

AWS Glue is a serverless data integration service. It handles extraction, transformation, and loading (ETL) using PySpark, Python, or Spark SQL. Glue can read from and write to diverse data sources (S3, databases, APIs, streaming). It includes a data catalog, schema discovery, and job scheduling.

Fundamental Difference

The key difference: dbt transforms data in place within a data warehouse. Glue moves and transforms data across systems.

If your data is already in a warehouse and you need to create derived tables, views, and feature tables from it - dbt is the natural choice. If you need to extract data from multiple sources, transform it, and load it into a target - Glue is the natural choice.

Feature Comparison

For AI Feature Engineering

dbt for Features

dbt excels at creating feature tables from warehouse data:

-- models/features/customer_features.sql
SELECT
    customer_id,
    COUNT(orders) AS total_orders,
    AVG(order_amount) AS avg_order_value,
    MAX(order_date) AS last_order_date,
    DATEDIFF(day, MAX(order_date), CURRENT_DATE) AS days_since_last_order
FROM {{ ref('stg_orders') }}
GROUP BY customer_id

The feature definition is clear, testable, and version-controlled. dbt’s incremental models efficiently update features as new data arrives.

Advantages for AI:

• Feature definitions are pure SQL, readable by data scientists and engineers
• Built-in testing validates feature quality (no null customer IDs, amounts within range)
• Lineage tracking shows which source tables feed which features
• Incremental models reduce processing time for large datasets
• Version control enables reproducing feature definitions for any model version

Glue for Features

Glue excels when feature engineering requires data from multiple sources or complex Python logic:

• Read customer data from RDS, transaction data from S3, behavior data from Kinesis
• Apply complex transformations using PySpark (custom functions, ML preprocessing)
• Write results to S3, Redshift, or DynamoDB

Advantages for AI:

• Access data from any source without loading it into a warehouse first
• Python/PySpark enables complex transformations (text processing, image feature extraction)
• Serverless execution scales automatically
• Can process data too large or too raw for a warehouse

Cost

dbt Core is free. The warehouse compute cost is the only expense, and the warehouse is likely already running. dbt Cloud adds $100+/seat/month for scheduling, IDE, and collaboration features.

AWS Glue charges $0.44 per DPU-hour. A minimal job (2 DPUs) running for 10 minutes costs ~$0.15. A feature engineering job processing 100GB might cost $2-10. Monthly costs depend entirely on job frequency and data volume.

For SQL transformations within an existing warehouse, dbt is nearly free (warehouse compute is shared). For ETL from external sources, Glue’s pay-per-use model is cost-effective.

Developer Experience

dbt is beloved by analytics engineers and data scientists who think in SQL. The project structure is clean: models in SQL files, tests as YAML, documentation auto-generated. The CLI is fast and the feedback loop is tight.

Glue requires PySpark knowledge, which is less common than SQL. The development experience is improving (Glue Studio visual editor, Glue interactive sessions) but still more complex than dbt’s SQL-first approach. Debugging Spark jobs is harder than debugging SQL queries.

When to Choose dbt

• Data is already in a warehouse (Redshift, Snowflake, BigQuery)
• Transformations can be expressed in SQL
• Feature engineering is primarily aggregations, joins, and window functions
• Team includes SQL-proficient data analysts or analytics engineers
• Data testing and documentation are priorities

When to Choose Glue

• Need to extract data from diverse sources (databases, APIs, S3, streams)
• Transformations require Python/PySpark (text processing, complex logic)
• Data must be processed before loading into a warehouse
• Working with raw, unstructured data (logs, JSON, nested structures)
• Serverless execution without managing infrastructure

Using Both

A common and effective pattern: Glue handles extraction and initial loading (EL), dbt handles transformation (T). Glue moves raw data from source systems into the warehouse. dbt transforms raw data into clean, tested, documented feature tables within the warehouse. This separation of concerns plays to each tool’s strengths.