ETL with SQL: The Definitive Guide (2025)

In today’s data-driven world, businesses compete on how fast and accurately they can make sense of their data. With more and more data scattered across numerous CRMs, cloud platforms, and internal systems, the raw records don’t translate into useful insights quite so easily.

This where ETL (Extract, Transform, Load) comes in, taking data from a multitude of sources, cleaning and sorting it, and depositing it into a central repository like a database or a data warehouse. At its center stands the Structured Query Language, which remains the de facto method to query, transform, and manage data in relational systems.

While ETL and SQL may seem like separate tools, they operate in lockstep. In this guide, we’ll unpack the SQL ETL fit. You’ll explore what ETL and SQL are, and why they matter, discover how they work together in real-world workflows, go through ETL tools that make the best use of SQL, and learn the best practices for writing SQL in ETL pipelines.

Table of Contents

1. What is ETL?
2. What is SQL?
3. How SQL and ETL Work Together: A Symbiotic Relationship
4. Top ETL Tools that Leverage SQL
5. Best Practices for Using SQL in ETL Pipelines
6. SQL and ETL Testing: Ensuring Data Accuracy
7. Conclusion

What is ETL?

ETL stands for Extract, Transform, Load, three core steps that define how raw data becomes usable business intelligence. It’s a foundational process in data integration, helping organizations pull data from different systems, prepare it for analysis, and deliver it to a centralized repository. Let’s break it down.

Extract
This is the starting point that involves collecting the raw data. It is gathered from one or multiple sources, such as CRMs, spreadsheets, APIs, cloud apps, or legacy systems. 

Transform
Once extracted, the data might be incomplete, inconsistent, or in the wrong format. During transformation, the data is cleaned, validated, deduplicated, and restructured. On this stage, you can apply business rules, map fields, and prepare the data to match the target schema.

Load
Finally, the transformed data is moved to a relational database or a cloud data warehouse like Snowflake, BigQuery, or Amazon Redshift. Once loaded, the data is ready for querying, reporting, or powering dashboards.

Together, these three steps form the automated workflow. Without ETL, teams would be stuck stitching spreadsheets and exporting CSVs by hand. 

ETL ensures that decision-makers work with accurate, timely information, whether they’re tracking KPIs, forecasting trends, or building machine learning models. 

What is SQL?

Structured Query Language is the standard language used to interact with relational databases. Be it pulling sales data from a customer table or updating inventory records, SQL is a way of instructing a database to do something.

It’s been around since the 1970s, and even after decades of developments in the data realm, SQL is still the foundation of data management due to its reliability, ease of use, and cross-platform support for all databases like MySQL, PostgreSQL, SQL Server, and Oracle.

To understand how to implement SQL ETL integration, it is helpful to have knowledge of the fundamentals. These commands are the base of ETL operations, particularly when SQL is utilized in or with ETL tools:

• SELECT – gets records from a single or multiple tables.
• INSERT – adds a new record into a table.
• UPDATE – modifies existing records in a table.
• DELETE – removes records from a table based on criteria.

JOIN – Combines rows from various tables based on matching columns, which enables more sophisticated queries and transformations.

SQL is the mechanism that makes databases worthwhile. It allows you to interact with structured data: query it, filter it, modify it, and move it. In the context of ETL, SQL powers the transformation logic. It helps enforce data integrity and supports analytical queries once data is loaded. 

How SQL and ETL Work Together: A Symbiotic Relationship

While ETL defines the what, SQL generally defines the how. Most ETL processes are dependent upon SQL behind the scenes to shape, convert, and validate data in every stage of the pipeline. It’s the language that gives precision and sequence to ETL processes.

SQL is the script that tells the ETL pipeline what to do, how to filter data, how to transform it, and how to load it into the destination system. By embedding it directly into ETL pipelines, you gain control, flexibility, and performance.

Let’s look at several examples of  using SQL on each stage of ETL:

Extraction

Extraction means getting data from the source and usually involves the SELECT command. This command pulls only the data you need from the source system, often with filters to narrow down the dataset.
In the statement below, we extract customer records created after January 1, 2024.

SELECT id, name, email
FROM customers
WHERE created_at >= '2024-01-01';

Transformation

On this stage, SQL shines the most. From simple cleaning tasks to advanced aggregations and joins, it offers a rich toolbox for reshaping data before it’s loaded.

The following statement selects customer data, removes the unnecessary spaces, and brings values to the specific format.

SELECT 
  id,
  TRIM(LOWER(email)) AS cleaned_email,
  UPPER(country) AS country_code
FROM customers;

Another example demonstrates how data can be shaped. The statement counts the average number of orders and groups the results by country.

SELECT 
  country,
  COUNT(*) AS total_customers,
  AVG(order_value) AS avg_order
FROM orders
GROUP BY country;

The following statement shows getting data from the customers and orders tables at once.

SELECT 
  o.order_id,
  c.name,
  o.order_value
FROM orders 
JOIN customers c ON o.customer_id = c.id;

These transformations prepare the data for downstream analysis by ensuring consistency, removing duplicates, enriching with joins, and summarizing with aggregations.

Loading

The final step is loading transformed data into the destination, and again, SQL commands do the heavy lifting. Depending on your setup, this might involve INSERT, UPDATE, or other operations.

For example, the statement below adds a new record in the target table.

INSERT INTO analytics.customers_cleaned (id, email, country)
VALUES (123, 'jane.doe@example.com', 'US');
The statement below updates the existing target record with new data. 
UPDATE analytics.customers_cleaned
SET email = 'john.new@example.com'
WHERE id = 456;

Top ETL Tools that Leverage SQL

Modern ETL tools are designed to simplify and automate the process of moving and transforming data. Instead of writing long scripts manually, users can build pipelines using visual interfaces, reusable components, and scheduling options. But behind the scenes, many of these platforms still rely on SQL to extract data, apply transformations, and load it into the destination.

In other words, even if you’re dragging and dropping in a no-code environment, SQL is used under the hood.

List of Popular Tools

Skyvia is a no-code cloud data platform that handles diverse data integration methods and scenarios for nearly 300 data sources. With Skyvia, you can connect to any supported app and execute an SQL query against it. If you are not a tech savvy, it’s not a problem for Skyvia. Its Visual Query Builder and Gallery with predefined queries will help you. Additionally, Skyvia allows you to write custom SQL commands for advanced use cases.

Microsoft SQL Server Integration Services (SSIS) is an enterprise-grade ETL platform deeply integrated with SQL Server. SSIS uses SQL statements in data flow tasks and provides powerful transformation components for manipulating structured data.

AWS Glue is a serverless data integration service that allows developers to write transformations in SQL, Python, or Scala. Glue supports SQL queries over S3 and integrates tightly with AWS data lakes and warehouses.

Google Cloud Dataflow is a fully managed stream and batch processing service that can work with SQL via BigQuery SQL and Apache Beam SQL extensions. It’s ideal for complex transformations in Google Cloud environments.

Informatica PowerCenter, a long-standing leader in the ETL space, supports SQL-based transformations and pushdown optimization, allowing SQL to be executed directly on source or target systems for better performance.

ActiveBatch. While more focused on workload automation, ActiveBatch supports ETL orchestration and SQL execution steps, making it a flexible choice for managing complex, multi-system workflows.

These tools vary in complexity and use case, but they all share a common thread: SQL is at the core of their ability to extract, transform, and load data reliably.

Best Practices for Using SQL in ETL Pipelines

When SQL is used in ETL workflows, it becomes part of your data infrastructure. That’s why writing clean, optimized, and maintainable queries is essential for scalability, accuracy, and performance. Below are some best practices to keep in mind when working with SQL in your ETL pipelines.

Optimize Your Queries

Performance matters. A poorly written command can result in poor performance, especially when dealing with large datasets. Make sure you:

• Use indexes appropriately to speed up searches and joins
• Avoid SELECT * in production pipelines — select only the columns you need
• Minimize full table scans by filtering early with WHERE clauses.
• Monitor query execution plans to spot bottlenecks.

Even small improvements in query design matter. 

Modularity and Reusability

Avoid having a huge single script to try everything at once. Divide your logic into modular pieces:

• Utilize views or Common Table Expressions (CTEs) to logically structure queries.
• Write reusable SQL snippets or stored procedures for operations used across pipelines.
• Parameterize and structure each module by its purpose (i.e., staging, cleaning, enriching).

Data Validation and Testing

SQL also can be a great tool to test and validate. Use intentional queries to:

• Confirm row counts in source tables versus destination tables
• Identify duplicates, nulls, or out-of-range values.
• Compare before-after transformation aggregate values (e.g., sums or averages).

Having validation steps at every level of ETL assures finding issues early and allowing only high-quality information to your reporting layer.

Error Handling

Your scripts should be resilient. Use error handling techniques to manage unexpected issues gracefully:

• Implement conditional checks (IF EXISTS, TRY...CATCH, etc.)
• Log failed rows to a separate table for review.
• Include rollback logic for critical updates or inserts

This level of control is fundamental when working in production environments where silent failures can compromise entire datasets.

Documentation

Clear documentation makes your ETL SQL future-proof. Comment your code to explain:

• The purpose of each step or transformation
• Any assumptions or business rules applied
  Known limitations or dependencies

Well-documented SQL helps collaborators understand your pipeline and speeds up maintenance and troubleshooting down the line.

SQL and ETL Testing: Ensuring Data Accuracy

No matter how polished your ETL pipeline is, it’s only as good as the results it generates. That’s why testing is necessary. SQL is the tool of choice for checking data integrity at every stage of the ETL process.

Did everything make it to the destination? Are there missing values? Were transformations performed as expected? These are key to building trust in your data and catching problems before they land on dashboards or decision-makers.

Types of ETL Testing with SQL

Data Completeness Testing
This type of testing is used to make sure that no records were lost during the ETL process. By comparing row counts between source and target systems, teams can confirm that data was extracted, transformed, and loaded without omissions.

For example,

Source

SELECT COUNT(*) FROM legacy_orders;

Target

SELECT COUNT(*) FROM warehouse.orders;

Data Quality Testing

Quality checking can detect duplicate records, null values for required fields, or inconsistent formats. SQL makes the definition and enforcement of these rules easy so that only clean, valuable data moves forward along the pipeline.

For example, the statement below checks if any null email values or emails are missing the @ character.

SELECT *
FROM customers
WHERE email IS NULL OR email NOT LIKE '%@%';

Transformation Testing

Once business rules are applied at the time of transformation, one must ensure whether the logic was used appropriately. This involves comparing the data before and after the transformation to make sure that values were calculated, mapped, or aggregated as planned.

For example, the query below checks if the total_price = quantity * unit_price.

SELECT *
FROM sales
WHERE total_price <> quantity * unit_price;

Incorporating these SQL-driven tests into your ETL workflows helps build confidence in the data and the decisions based on it. 

Conclusion

ETL and SQL form the backbone of data operations in the present day. ETL gives the structure on how data moves from one system to another, and SQL introduces the logic and control that makes every step meaningful and consistent.

Throughout this guide, we’ve shown how the two go together hand in glove. SQL powers the extraction of target datasets, shapes raw inputs into structured insight within transformation, and controls loading into data warehouses. We also reviewed popular tools that leverage SQL internally, best practices for crafting clean and modular SQL, and employing it for verification and testing of your ETL flows.

If you’re working with data, mastering SQL is non-negotiable. It’s a key driver of clean, efficient, and trustworthy ETL pipelines.

Looking to simplify and strengthen your ETL process? Explore how Skyvia’s no-code integration platform can help you build powerful, SQL-enabled workflows without the overhead.