SQL and Data Integration: ETL and ELT

In this article, we will discuss use cases and methods for using ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes along with SQL to integrate data from various sources.

SQL and Data Integration: ETL and ELT
Image by Author




SQL is a standardized programming language and powerful tool used for managing and analyzing data stored in relational databases and performing various operations on the data is SQL (Structured Query Language). It is an essential skill for data analysts, data scientists, and data warehousing professionals because it allows users to create, modify, and query the data in these databases. 

As data continues to grow in volume, variety, and complexity, the significance of data integration will only increase. Businesses that are able to effectively integrate data from multiple sources using SQL will be better equipped to make informed decisions and gain a competitive advantage.ETL and ELT are common ways of data extraction from multiple sources, transformation into an analysis-ready format, and loading into a database or data warehouse that we will be discussing in this article.


ETL vs ELT: Which is Right for Your Use Case?


ETL( Extract, Transform, Load) and ELT( Extract, Load, Transform) are two common ways used to integrate data from multiple sources into a destination database or data warehouse. The main difference between the two approaches is the order in which the data transformation and loading steps are performed.

In ETL, the data is extracted from the source systems, transformed into a format suitable for analysis, and then loaded into the destination database. This is the traditional approach to data integration and is well-suited for cases where the source systems are relatively simple, and the transformation process is relatively straightforward.

In ELT, the data is extracted from the source systems and loaded into the destination database first, and then transformed into a suitable format for analysis. This approach is becoming increasingly popular in modern data infrastructures due to the capability of modern data storage to handle large volumes of data and the increasing complexity of data transformation processes.

When deciding between ETL and ELT, there are several factors to consider, including:


The Complexity of the Transformation Process


ETL is more suitable for simple transformation processes, while ELT is better suited for more complex transformations.


The Size and Complexity of the Source Systems


ETL may be more suitable for destination systems with limited processing power or storage, while ELT is better suited for more powerful systems.


The Capabilities of the Destination Database or Data Warehouse


ETL may be more suitable for destination systems with limited processing power or storage, while ELT is better suited for more powerful systems.


The Data Processing and Analysis Requirements of the Organization


ETL may be more applicable for organizations with more traditional data processing and analysis requirements, while ELT may be better suited for organizations with more complex or real-time data processing and analysis requirements.


The Available Resources


ETL requires more upfront setup and maintenance, while ELT may require more resources during the transformation and loading process.


The Security and Compliance Requirements


ETL allows for more control over the transformation process, which may be important in cases where security and compliance are a concern.


Basic Techniques for Data Integration Using SQL


Extracting Data from Multiple Sources


To extract data from a table in a relational database, you can use a ‘SELECT’ statement with the ‘FROM’ and ‘WHERE’ clauses:

SELECT * FROM customers WHERE country = 'USA';


This statement will extract all rows from the customers’ table where the country column is equal to the USA.

To extract data from a flat file, such as a CSV or TXT file, you can use the ‘LOAD DATA INFILE’ command:

LOAD DATA INFILE '/path/to/file.csv'
INTO TABLE customers


This command will load the data from the CSV file into the ‘customers’ table, using the  ‘, ‘character as the field delimiter and the ‘ " ‘ character as the field enclosure. 

To extract data from an API, you can use a programming language such as Python or Java to make HTTP requests and parse the response data. For example, in Python you can use the ‘requests’ library to make a GET request to an API endpoint and then use the ‘JSON ()’ method to parse the response data into a dictionary:

RESPONSE = REQUESTS.GET('https://api.example.com/endpoint')


Transforming Data using SQL Queries


To apply a function to a column of data, you can use the function name followed by the column name in the ‘SELECT’ clause:

SELECT LOWER(name) AS lower_name FROM customers;


This statement will transform the ‘name column’ by applying the ‘LOWER()’ function to each value, and the result will be aliased as ‘lower_name’.

To rename a column, you can use the ‘AS’ keyword in the ‘SELECT’ clause:

SELECT name AS full_name FROM customers;


This statement will rename the ‘name’ column as ‘full_name’.

To merge data from multiple sources, you can use the UNION operator:

SELECT * FROM customers
SELECT * FROM orders;


This statement will merge the data from the ‘customers’ and ‘orders’ tables, eliminating duplicates.


Loading Data into a Destination Database or Data Warehouse


To insert new rows into a table, you can use the ‘INSERT INTO’ statement:

INSERT INTO customers (name, email, country)
VALUES ('John Doe', 'johndoe@example.com', 'USA');


This statement will insert a new row into the customers table with the specified values for the ‘name’, ‘email’, and ‘country’ columns.

To update existing rows in a table, you can use the ‘UPDATE’ statement with the ‘SET’ and ‘WHERE’ clauses:

UPDATE customers
SET email = 'john.smith@example.com'
WHERE name = 'John Smith';


This statement will update the email column of the row where the ‘name’ column is equal to 'John Smith' with the value  'john.smith@example.com'




I hope you enjoyed reading the article. Please feel free to share your thoughts or feedback in the comment section. I would conclude my discussion with some final thoughts. The future of data integration with SQL is likely to involve the integration of machine learning algorithms, greater integration with big data technologies, and more sophisticated ETL and ELT processes. By staying up to date on the latest ways and technologies for data integration with SQL, businesses can ensure that they're well-positioned to take advantage of the opportunities and challenges of the data-driven economy.
Kanwal Mehreen is an aspiring software developer with a keen interest in data science and applications of AI in medicine. Kanwal was selected as the Google Generation Scholar 2022 for the APAC region. Kanwal loves to share technical knowledge by writing articles on trending topics, and is passionate about improving the representation of women in tech industry.