ETL And SQL: How They Can Work Together (+ Examples)

Data is a valuable asset to most enterprises today. However, it is crucial to transform raw data into useful and actionable information to maximize its utilization. You can achieve this by implementing effective data management solutions such as ETL (extract, transform, load) processes and SQL (Structured Query Language). 

ETL is a systematic method for extracting data from various sources, transforming it into a suitable format, and loading it into a data warehouse. On the other hand, SQL is a programming language that can help you query and manage your data in relational databases. This article will explore how to perform ETL in SQL to efficiently process and analyze your data and enable informed business decisions.

What Is ETL?

ETL is a data integration process that allows you to consolidate data from disparate sources into a single, unified data store. It helps you convert your raw data into an understandable format optimized for further analysis. This approach allows you to leverage data-driven insights to measure your organization's performance and evaluate your business objectives. 

The ETL process consists of three stages—extraction, transformation, and loading.

Extraction

Extraction is the first step of the ETL process, where you collect data from multiple sources, including spreadsheets, CRMs, APIs, or other data storage systems. This data can be of various formats, such as images, audio files, videos, or text. The primary goal is to retrieve all relevant data without affecting the performance or availability of your source systems. It is also crucial to ensure the data’s integrity and completeness during this stage.  

Transformation

Transformation involves converting the extracted data into a suitable format for analysis and reporting. This stage includes data cleaning, filtering, aggregation, and normalization. You can also enrich the data by integrating it with additional information from other sources. The transformation process maintains your data’s consistency, quality, accuracy, and alignment with the target schema or business rules.

Loading 

Loading is the final step, where you move the transformed data into your preferred data repository, such as a data warehouse, data mart, or lake. This stage involves inserting, updating, or replacing data at the destination to make it readily available for business intelligence tools and advanced analytics platforms. Efficient loading processes minimize downtime and ensure your data is quickly accessible across the organization for decision-making.   

What Is SQL?

SQL stands for Structured Query Language. It is a programming language that you can use to manage and manipulate relational databases. You can also perform various operations, such as inserting new data or updating and deleting existing records. Some popular tools that work on SQL are MySQL, PostgreSQL, SQL Server, and Oracle.

You can categorize SQL commands into Data Query Language (DQL), Data Definition Language (DDL), Data Manipulation Language (DML), and Data Control Language (DCL).  These commands help ease data retrieval, define database structures, modify data, and control data access. 

SQL empowers you to execute complex data transformation tasks involving multiple tables using JOIN operations and summarize data using aggregate functions. Its flexibility and scalability can easily accommodate your growing data needs. 

How Can ETL & SQL Work Together in Data Management?

ETL and SQL are powerful tools that can be used together in data warehousing systems to streamline your data management process. SQL provides commands to extract, transform, and load data into a central repository, while ETL helps you automate this process and make it cost-efficient. 

Here's how you can implement SQL in each stage of the ETL process:

Extraction

SQL queries can easily extract data from spreadsheets, CRMs, and cloud storage. You can write queries to target tables, filter data based on conditions, and retrieve only the relevant information needed for analysis.

For example, if you want to retrieve information on customers in Mumbai from your sales database, then your SQL query might look like this:

SELECT customer_id, name, email, city
FROM sales;
WHERE sales.city = ‘Mumbai’

This query fetches relevant customer information, and the ETL tool or script moves it to the staging area for transformation. 

Transformation 

SQL offers various functionalities to clean and standardize your data during transformation. You can use queries to remove duplicates, fix inconsistencies in formatting, handle missing values, and normalize your data. There are various SQL data cleaning techniques that also allow you to filter, sort, and aggregate your data.

For example, you can use the following SQL query to calculate the total sales per customer. This query joins two databases—sales_orders and customers—and provides a streamlined output required for sales analysis.

SELECT c.customer_id, c.name, s.total_sales 
FROM customers c 
JOIN (SELECT customer_id, SUM(total_amount) AS total_sales FROM sales_orders GROUP BY customer_id) s 
ON c.customer_id = s.customer_id;

Loading

In the loading stage, SQL with ETL efficiently enables the loading of large amounts of transformed data into the target data warehouse or database. This can involve inserting, updating, or deleting records based on specific conditions.

For example, if you want to load the aggregated sales data obtained previously into a data warehouse table, then your query will look like this.

INSERT INTO customer_sales (customer_id, total_sales) 
SELECT customer_id, SUM(total_amount) 
FROM sales_orders 
GROUP BY customer_id;

By leveraging SQL at each stage of the ETL process, you can efficiently handle and prepare your data for analytical purposes. This also ensures high-quality data throughout your data pipeline. 

How Is SQL Used in ETL Testing?

SQL plays a crucial role in various stages of ETL testing to ensure that the ETL process functions appropriately and maintains data integrity.

Data Verification During Extraction

This involves checking for metadata consistency, where you verify that the structure of the source and destination systems, including data types, lengths, and formats, aligns. SQL queries can also help ensure data completeness and quality by comparing the target and source record counts while maintaining data accuracy and consistency.  

Data Transformation Validation

As data transforms, SQL helps verify that these transformations are correctly applied. You can write queries to compare the transformed data against the expected results. This enables you to ensure the proper implementation of business rules and transformation logic specified in the ETL process.  

ETL Performance Testing

You can use SQL queries to test the performance of the ETL data pipelines. This includes checking the execution time of queries, the efficiency of data retrieval, and ensuring that the ETL process runs within the acceptable time limits. 

Regression Testing

When you update or modify the ETL process, using SQL in regression testing can ensure that new changes don’t negatively impact the existing ETL workflows. You can write SQL scripts to compare current data with the previous data to identify any significant discrepancies,

ETL Data Integration Testing

ETL data integration testing ensures the ETL process seamlessly interacts with other systems and applications within your organization. You can use SQL to compare data across ETL pipelines and other applications to verify consistency and identify integration issues that can cause data loss or corruption. 

4 Best SQL ETL Tools You Can Use

When managing and transforming data, choosing the right ETL SQL tool is crucial for efficient and effective data handling. Here are four popular tools for you to explore and select from based on your organizational needs and goals.

Airbyte

Airbyte is a no-code data integration platform that utilizes the (EL)T approach to build your data pipelines. This unique approach helps reduce latency when extracting and loading high-volume, high-velocity data and facilitates a consolidated view of your raw data for exploratory analysis.

Airbyte is a versatile, user-intuitive tool that automates data movement with its library of over 350 pre-built connectors. It also allows you to create custom connectors using the Connector Development Kit within minutes.

Airbyte supports various SQL databases and allows you to define SQL queries in the transformation stage using dbt Cloud integration. This simplifies complex data transformations and provides ready-to-use data for further processing and reporting. 

Additionally, Airbyte’s schema change management feature allows you to automatically propagate and reflect all source schema modifications at the destination, ensuring data consistency and integrity. 

What’s more! Airbyte also provides an open-source version and an active community of over 15K members who can guide you with Airbyte implementations. You can easily access the official documentation, blogs, videos, and other resources to explore its features and familiarize yourself with the platform.

Talend

Talend is a comprehensive data integration platform known for its robust ETL capabilities. You can seamlessly integrate Talend with other SQL databases and manipulate complex data using SQL scripts. It can easily handle large volumes of data efficiently and provides a user-friendly interface where you can design ETL jobs visually. Talend also allows you to embed custom SQL code, offering flexibility and ease of use.

Fivetran

Fivetran is known for its simplicity and efficiency in handling different data integration processes. It automates the data extraction and loading processes, minimizing the need for manual intervention. Fivetran supports a wide range of data sources and destinations, including popular SQL databases, making it a versatile tool for data integration. It leverages the processing power of the SQL database and also supports dbt transformations, ensuring high performance and scalability.

SSIS

SSIS is a powerful SQL ETL tool offered by Microsoft SQL Server. It is known for its deep integration with the SQL Server ecosystem and robust data extraction, transformation, and loading capabilities. SSIS provides a graphical interface for designing ETL workflows, making it accessible to users with varying levels of technical expertise. 

These tools offer unique strengths for performing ETL in SQL, making them suitable for different use cases and organizational needs. Based on your requirements, you can decide which tool best fits your business model. 

Key takeaways

Combining ETL processes with SQL commands can enhance data management by automating data extraction, transformation, and loading. This results in accurate, consistent, high-quality, clean data, streamlining your organization’s workflows. SQL also helps in ETL testing, informing you of the overall health of your data pipelines.   

This article thoroughly explains how ETL in SQL can help you optimize your data flow and maximize its utilization for agile decision-making. It also introduces you to tools like Airbyte, Talend, Fivetran, and SSIS that can make implementing this combination much easier with their user-friendly and intuitive interfaces. 

Based on your organizational requirements and budget, you can invest in SQL ETL tools that scale with your evolving needs and optimize data integration and analysis processes. This helps you save time and resources for high-level strategic planning for future business growth. 

FAQs

Q. What is the difference between ETL and SQL?

ETL is a data integration process that consolidates data from multiple sources into a data warehouse. SQL is a programming language for querying, manipulating, and managing data in relational databases.

Q. How to perform ETL with SQL?

To perform ETL with SQL, you can use SQL-based ETL tools or write and execute SQL scripts at every stage of the ETL process. 

Q. Why use ETL tools when we have SQL?

ETL tools offer a user-friendly interface, automation, and advanced data transformation capabilities, reducing the manual coding efforts required for SQL.