SQL for Data Engineering: ETL Concepts and Building Scalable Data Pipelines

Table of Contents

1. Introduction
2. What Is Data Engineering and Why SQL Matters
3. Understanding ETL (Extract, Transform, Load)
4. SQL’s Role in Each ETL Stage
5. Data Sources: Raw Data Ingestion with SQL
6. Transformations: Cleaning and Normalizing Data
7. Using Staging Tables in SQL Pipelines
8. Surrogate Keys and Slowly Changing Dimensions (SCDs)
9. Incremental Loads and Change Data Capture (CDC)
10. Partitioning and Batching Strategies
11. Deduplication and Idempotency in SQL
12. Building Materialized Views
13. Scheduling and Automating ETL Workflows
14. Logging and Monitoring in SQL Pipelines
15. Error Handling and Data Quality Checks
16. Working with Semi-Structured Data (JSON, XML)
17. Schema Design for Analytical Workloads
18. Integration with Airflow, dbt, and Other Tools
19. Best Practices for SQL Data Engineering
20. Summary and What’s Next

1. Introduction

Data engineering is the backbone of data-driven organizations. SQL remains the foundation for building reliable, scalable, and maintainable data pipelines that power reporting, analytics, and machine learning.

2. What Is Data Engineering and Why SQL Matters

Data engineering involves the design, development, and maintenance of systems that enable efficient data collection, storage, transformation, and distribution. SQL is crucial for:

• Writing ETL logic
• Creating clean, normalized, and enriched datasets
• Building warehouse-ready schemas
• Orchestrating transformations and analytics layers

3. Understanding ETL (Extract, Transform, Load)

SQL is typically used for T and L steps, and sometimes for E within RDBMS-based connectors.

4. SQL’s Role in Each ETL Stage

• Extract: Read from APIs, logs, or staging databases
• Transform: Use SELECT, JOIN, CASE, WINDOW, etc.
• Load: Insert into fact/dimension tables or materialized views

5. Data Sources: Raw Data Ingestion with SQL

-- Load raw CSV into staging
COPY staging_orders FROM '/data/orders.csv' DELIMITER ',' CSV HEADER;

Or via external connectors (e.g., Fivetran, Airbyte).

6. Transformations: Cleaning and Normalizing Data

Example transformation SQL:

INSERT INTO cleaned_orders
SELECT
  id,
  LOWER(TRIM(customer_name)) AS customer_name,
  COALESCE(total_amount, 0) AS total_amount,
  CASE
    WHEN status = 'delivered' THEN 'fulfilled'
    ELSE status
  END AS status_cleaned
FROM staging_orders
WHERE total_amount IS NOT NULL;

7. Using Staging Tables in SQL Pipelines

Staging tables help isolate raw data and ensure replayability:

-- staging_users
-- cleaned_users
-- users_final

Follow a clear naming pattern: stg_, cleaned_, fct_, dim_

8. Surrogate Keys and Slowly Changing Dimensions (SCDs)

Surrogate Keys:

ALTER TABLE customers ADD COLUMN customer_key SERIAL PRIMARY KEY;

SCD Type 2 (history tracking):

-- Insert new row with new version
INSERT INTO dim_customer_history (customer_id, name, version, valid_from, valid_to)
VALUES (1, 'New Name', 2, CURRENT_DATE, NULL);

9. Incremental Loads and Change Data Capture (CDC)

Last updated timestamp method:

INSERT INTO fact_orders
SELECT *
FROM staging_orders
WHERE updated_at > (SELECT MAX(updated_at) FROM fact_orders);

CDC tools: Debezium, Fivetran, Snowpipe (Snowflake), etc.

10. Partitioning and Batching Strategies

Partitioned tables improve performance:

CREATE TABLE sales (
  id INT,
  sale_date DATE,
  amount NUMERIC
) PARTITION BY RANGE (sale_date);

Batching:

-- Load only last 7 days
WHERE order_date >= CURRENT_DATE - INTERVAL '7 days';

11. Deduplication and Idempotency in SQL

-- Deduplicate by latest timestamp
SELECT DISTINCT ON (order_id) *
FROM staging_orders
ORDER BY order_id, updated_at DESC;

Use MERGE or UPSERT patterns to avoid duplicates.

12. Building Materialized Views

CREATE MATERIALIZED VIEW daily_sales_summary AS
SELECT order_date, SUM(amount) AS total
FROM fact_orders
GROUP BY order_date;

Use REFRESH MATERIALIZED VIEW to update periodically.

13. Scheduling and Automating ETL Workflows

• Use cron jobs, Airflow, dbt Cloud, or other orchestrators
• SQL examples:

-- PostgreSQL job
SELECT refresh_sales_summary();

• Define dependencies and run incrementally

14. Logging and Monitoring in SQL Pipelines

Create a log table:

CREATE TABLE etl_log (
  job_name TEXT,
  start_time TIMESTAMP,
  end_time TIMESTAMP,
  status TEXT,
  rows_processed INT
);

Insert log entry at the end of ETL steps.

15. Error Handling and Data Quality Checks

Examples:

-- Constraint check
SELECT * FROM staging_users WHERE email IS NULL;

-- Threshold check
SELECT COUNT(*) FROM orders WHERE total < 0;

Raise alerts or halt pipeline on failure.

16. Working with Semi-Structured Data (JSON, XML)

JSON:

SELECT json_data->>'user_id' AS user_id
FROM event_logs;

Flattening nested JSON:

SELECT id, json_array_elements(data->'items') AS item
FROM orders_json;

17. Schema Design for Analytical Workloads

Use Star Schema:

• Fact tables: Events, transactions
• Dimension tables: Customer, Product, Region

Avoid unnecessary normalization in analytical queries.

18. Integration with Airflow, dbt, and Other Tools

• Airflow: Orchestrate SQL DAGs
• dbt: SQL-based transformations with version control and testing
• Dataform, Luigi, Dagster — alternative orchestration tools

Example dbt model:

-- models/fct_orders.sql
SELECT * FROM {{ ref('stg_orders') }}
WHERE status = 'complete'

19. Best Practices for SQL Data Engineering

• Use idempotent transformations (can re-run safely)
• Break logic into modular layers (staging → clean → final)
• Keep metadata logs for traceability
• Use surrogate keys for dimension tables
• Prefer batch-based incremental updates
• Automate quality checks with row counts, NOT NULL, etc.
• Document pipeline logic and dependencies

20. Summary and What’s Next

SQL plays a central role in modern data engineering — from raw data ingestion to clean, analytics-ready datasets. Mastering SQL-based ETL, transformation layers, and orchestration tools is essential for building trustworthy, production-grade data pipelines.