Data Transformation

Transform raw data into analytical assets using modern transformation patterns, frameworks, and orchestration tools.

Purpose

Select and implement data transformation patterns across the modern data stack. Transform raw data into clean, tested, and documented analytical datasets using SQL (dbt), Python DataFrames (pandas, polars, PySpark), and pipeline orchestration (Airflow, Dagster, Prefect).

When to Use

Invoke this skill when:

• Choosing between ETL and ELT transformation patterns
• Building dbt models (staging, intermediate, marts)
• Implementing incremental data loads and merge strategies
• Migrating pandas code to polars for performance improvements
• Orchestrating data pipelines with dependencies and retries
• Adding data quality tests and validation
• Processing large datasets with PySpark
• Creating production-ready transformation workflows

Quick Start: Common Patterns

dbt Incremental Model

{{
  config(
    materialized='incremental',
    unique_key='order_id'
  )
}}

select order_id, customer_id, order_created_at, sum(revenue) as total_revenue
from {{ ref('int_order_items_joined') }}
group by 1, 2, 3

{% if is_incremental() %}
    where order_created_at > (select max(order_created_at) from {{ this }})
{% endif %}

polars High-Performance Transformation

import polars as pl

result = (
    pl.scan_csv('large_dataset.csv')
    .filter(pl.col('year') == 2024)
    .with_columns([(pl.col('quantity') * pl.col('price')).alias('revenue')])
    .group_by('region')
    .agg(pl.col('revenue').sum())
    .collect()  # Execute lazy query
)

Airflow Data Pipeline

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta

with DAG(
    dag_id='daily_sales_pipeline',
    schedule_interval='0 2 * * *',
    default_args={'retries': 2, 'retry_delay': timedelta(minutes=5)},
    start_date=datetime(2024, 1, 1),
    catchup=False
) as dag:
    extract = PythonOperator(task_id='extract', python_callable=extract_data)
    transform = PythonOperator(task_id='transform', python_callable=transform_data)
    extract >> transform

Decision Frameworks

ETL vs ELT Selection

Use ELT (Extract, Load, Transform) when:

• Using modern cloud data warehouse (Snowflake, BigQuery, Databricks)
• Transformation logic changes frequently
• Team includes SQL analysts
• Data volume 10GB-1TB+ (leverage warehouse parallelism)

Tools: dbt, Dataform, Snowflake tasks, BigQuery scheduled queries

Use ETL (Extract, Transform, Load) when:

• Regulatory compliance requires pre-load data redaction (PII/PHI)
• Target system lacks compute power
• Real-time streaming with immediate transformation
• Legacy systems without cloud warehouse

Tools: AWS Glue, Azure Data Factory, custom Python scripts

Use Hybrid when combining sensitive data cleansing (ETL) with analytics transformations (ELT).

Default recommendation: ELT with dbt unless specific compliance or performance constraints require ETL.

For detailed patterns, see references/etl-vs-elt-patterns.md.

DataFrame Library Selection

Choose pandas when:

• Data size < 500MB
• Prototyping or exploratory analysis
• Need compatibility with pandas-only libraries

Choose polars when:

• Data size 500MB-100GB
• Performance critical (10-100x faster than pandas)
• Production pipelines with memory constraints
• Want lazy evaluation with query optimization

Choose PySpark when:

• Data size > 100GB
• Need distributed processing across cluster
• Existing Spark infrastructure (EMR, Databricks)

Migration path: pandas → polars (easier, similar API) or pandas → PySpark (requires cluster)

For comparisons and migration guides, see references/dataframe-comparison.md.

Orchestration Tool Selection

Choose Airflow when:

• Enterprise production (proven at scale)
• Need 5,000+ integrations
• Managed services available (AWS MWAA, GCP Cloud Composer)

Choose Dagster when:

• Heavy dbt usage (native dbt_assets integration)
• Data lineage and asset-based workflows prioritized
• ML pipelines requiring testability

Choose Prefect when:

• Dynamic workflows (runtime task generation)
• Cloud-native architecture preferred
• Pythonic API with decorators

Safe default: Airflow (battle-tested) unless specific needs for Dagster/Prefect.

For detailed patterns, see references/orchestration-patterns.md.

SQL Transformations with dbt

Model Layer Structure

1. Staging Layer (models/staging/)

   • 1:1 with source tables
   • Minimal transformations (renaming, type casting, basic filtering)
   • Materialized as views or ephemeral

2. Intermediate Layer (models/intermediate/)

   • Business logic and complex joins
   • Not exposed to end users
   • Often ephemeral (CTEs only)

3. Marts Layer (models/marts/)

   • Final models for reporting
   • Fact tables (events, transactions)
   • Dimension tables (customers, products)
   • Materialized as tables or incremental

dbt Materialization Types

View: Query re-run each time model referenced. Use for fast queries, staging layer.

Table: Full refresh on each run. Use for frequently queried models, expensive computations.

Incremental: Only processes new/changed records. Use for large fact tables, event logs.

Ephemeral: CTE only, not persisted. Use for intermediate calculations.

dbt Testing

models:
  - name: fct_orders
    columns:
      - name: order_id
        tests:
          - unique
          - not_null
      - name: customer_id
        tests:
          - relationships:
              to: ref('dim_customers')
              field: customer_id
      - name: total_revenue
        tests:
          - dbt_utils.accepted_range:
              min_value: 0

For comprehensive dbt patterns, see:

• references/dbt-best-practices.md
• references/incremental-strategies.md

Python DataFrame Transformations

pandas Transformation

import pandas as pd

df = pd.read_csv('sales.csv')
result = (
    df
    .query('year == 2024')
    .assign(revenue=lambda x: x['quantity'] * x['price'])
    .groupby('region')
    .agg({'revenue': ['sum', 'mean']})
)

polars Transformation (10-100x Faster)

import polars as pl

result = (
    pl.scan_csv('sales.csv')  # Lazy evaluation
    .filter(pl.col('year') == 2024)
    .with_columns([(pl.col('quantity') * pl.col('price')).alias('revenue')])
    .group_by('region')
    .agg([
        pl.col('revenue').sum().alias('revenue_sum'),
        pl.col('revenue').mean().alias('revenue_mean')
    ])
    .collect()  # Execute lazy query
)

Key differences:

• polars uses scan_csv() (lazy) vs pandas read_csv() (eager)
• polars uses with_columns() vs pandas assign()
• polars uses pl.col() expressions vs pandas string references
• polars requires collect() to execute lazy queries

PySpark for Distributed Processing

from pyspark.sql import SparkSession, functions as F

spark = SparkSession.builder.appName("Transform").getOrCreate()
df = spark.read.csv('sales.csv', header=True, inferSchema=True)

result = (
    df
    .filter(F.col('year') == 2024)
    .withColumn('revenue', F.col('quantity') * F.col('price'))
    .groupBy('region')
    .agg(F.sum('revenue').alias('total_revenue'))
)

For migration guides, see references/dataframe-comparison.md.

Pipeline Orchestration

Airflow DAG Structure

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta

default_args = {
    'owner': 'data-engineering',
    'retries': 2,
    'retry_delay': timedelta(minutes=5)
}

with DAG(
    dag_id='data_pipeline',
    default_args=default_args,
    schedule_interval='0 2 * * *',  # Daily at 2 AM
    start_date=datetime(2024, 1, 1),
    catchup=False
) as dag:
    task1 = PythonOperator(task_id='extract', python_callable=extract_fn)
    task2 = PythonOperator(task_id='transform', python_callable=transform_fn)
    task1 >> task2  # Define dependency

Task Dependency Patterns

Linear: A >> B >> C (sequential) Fan-out: A >> [B, C, D] (parallel after A) Fan-in: [A, B, C] >> D (D waits for all)

For Airflow, Dagster, and Prefect patterns, see references/orchestration-patterns.md.

Data Quality and Testing

dbt Tests

Generic tests (reusable): unique, not_null, accepted_values, relationships

Singular tests (custom SQL):

-- tests/assert_positive_revenue.sql
select * from {{ ref('fct_orders') }}
where total_revenue < 0

Great Expectations

import great_expectations as gx

context = gx.get_context()
suite = context.add_expectation_suite("orders_suite")

suite.add_expectation(
    gx.expectations.ExpectColumnValuesToNotBeNull(column="order_id")
)
suite.add_expectation(
    gx.expectations.ExpectColumnValuesToBeBetween(
        column="total_revenue", min_value=0
    )
)

For comprehensive testing patterns, see references/data-quality-testing.md.

Advanced SQL Patterns

Window functions for analytics:

select
    order_date,
    daily_revenue,
    avg(daily_revenue) over (
        partition by region
        order by order_date
        rows between 6 preceding and current row
    ) as revenue_7d_ma,
    sum(daily_revenue) over (
        partition by region
        order by order_date
    ) as cumulative_revenue
from daily_sales

For advanced window functions, see references/window-functions-guide.md.

Production Best Practices

Idempotency

Ensure transformations produce same result when run multiple times:

• Use merge statements in incremental models
• Implement deduplication logic
• Use unique_key in dbt incremental models

Incremental Loading

{% if is_incremental() %}
    where created_at > (select max(created_at) from {{ this }})
{% endif %}

Error Handling

try:
    result = perform_transformation()
    validate_result(result)
except ValidationError as e:
    log_error(e)
    raise

Monitoring

• Set up Airflow email/Slack alerts on task failure
• Monitor dbt test failures
• Track data freshness (SLAs)
• Log row counts and data quality metrics

Tool Recommendations

SQL Transformations: dbt Core (industry standard, multi-warehouse, rich ecosystem)

pip install dbt-core dbt-snowflake

Python DataFrames: polars (10-100x faster than pandas, multi-threaded, lazy evaluation)

pip install polars

Orchestration: Apache Airflow (battle-tested at scale, 5,000+ integrations)

pip install apache-airflow

Examples

Working examples in:

• examples/python/pandas-basics.py – pandas transformations
• examples/python/polars-migration.py – pandas to polars migration
• examples/python/pyspark-transformations.py – PySpark operations
• examples/python/airflow-data-pipeline.py – Complete Airflow DAG
• examples/sql/dbt-staging-model.sql – dbt staging layer
• examples/sql/dbt-intermediate-model.sql – dbt intermediate layer
• examples/sql/dbt-incremental-model.sql – Incremental patterns
• examples/sql/window-functions.sql – Advanced SQL

Scripts

• scripts/generate_dbt_models.py – Generate dbt model boilerplate
• scripts/benchmark_dataframes.py – Compare pandas vs polars performance

Related Skills

For data ingestion patterns, see ingesting-data. For data visualization, see visualizing-data. For database design, see databases-* skills. For real-time streaming, see streaming-data. For data platform architecture, see ai-data-engineering. For monitoring pipelines, see observability.

Merged Content from etl-pipelines

name: data_transform description: Design ETL/ELT pipelines with proper orchestration, error handling, and monitoring. Use when building data pipelines, designing data workflows, or implementing data transformations.

ETL Designer

Design robust ETL/ELT pipelines for data processing.

Quick Start

Use Airflow for orchestration, implement idempotent operations, add error handling, monitor pipeline health.

Instructions

Airflow DAG Structure

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta

default_args = {
    'owner': 'data-team',
    'retries': 3,
    'retry_delay': timedelta(minutes=5),
    'email_on_failure': True,
    'email': ['alerts@company.com']
}

with DAG(
    'etl_pipeline',
    default_args=default_args,
    schedule_interval='0 2 * * *',  # Daily at 2 AM
    start_date=datetime(2024, 1, 1),
    catchup=False
) as dag:
    
    extract = PythonOperator(
        task_id='extract_data',
        python_callable=extract_from_source
    )
    
    transform = PythonOperator(
        task_id='transform_data',
        python_callable=transform_data
    )
    
    load = PythonOperator(
        task_id='load_to_warehouse',
        python_callable=load_to_warehouse
    )
    
    extract >> transform >> load

Incremental Processing

def extract_incremental(last_run_date):
    query = f"""
        SELECT * FROM source_table
        WHERE updated_at > '{last_run_date}'
    """
    return pd.read_sql(query, conn)

Error Handling

def safe_transform(data):
    try:
        transformed = transform_data(data)
        return transformed
    except Exception as e:
        logger.error(f"Transform failed: {e}")
        send_alert(f"Pipeline failed: {e}")
        raise

Best Practices

🔄 Workflow

Kaynak: dbt Labs – Best Practices & Polars Performance Guide

Aşama 1: Data Contract & Source Audit

• Data Contracts: Veri kaynağı (Source) ve hedef (Target) arasındaki şemayı sabitle.
• Profiling: Ham verideki eksikleri, null oranlarını ve tipleri (Profiling) analiz et.
• Pattern Selection: Veri boyutuna göre ETL (Pandas/Polars) veya ELT (SQL/dbt) seçimi yap.

Aşama 2: Transformation Engine Setup

• Infrastructure: dbt-core profilini kur veya Cloud IDE yapılandır.
• Modular Modeling: Veriyi Staging (Renaming), Intermediate (Logic) ve Marts (Final) katmanlarına ayır.
• Polars Optimization: Python tabanlı dönüşümlerde lazy modunu (scan_csv / collect) kullanarak bellek ve hız optimizasyonu yap.

Aşama 3: Testing & Orchestration

• Unit Tests: Kritik dönüşüm mantığı için dbt tests veya Great Expectations ile validation yaz.
• Idempotency: Boru hattının (Pipeline) hata durumunda tekrar çalıştırılabilir (Idempotent) olduğundan emin ol.
• Orchestration: İş akışını Airflow veya Dagster üzerinde takvime bağla ve hata bildirimlerini kur.

Kontrol Noktaları

Data Transformation v2.0 – With Workflow