ETL and SQL: How They Work Together, Best Tools & Best Practices

The advancement of technology and data management tools has made it possible for businesses and organizations to generate data at a higher rate. These data are then analyzed, and the corresponding insights are used by the organization to gain a competitive advantage. Organizations must extract, transform, and load (ETL) their data into usable formats for analysis to effectively unlock meaningful insights from the data.

This write-up will shed light on the role of SQL (Structured Query Language) in modern ETL processes. We will also analyze how ETL and SQL are used at each stage, discuss the best practices, and explore the popular ETL tools.

What Is ETL and SQL?

ETL stands for Extract, Transform, and Load. It is a data integration process that combines, cleans, and manages data from multiple sources into a single data set for storage in a target system. SQL, or Structured Query Language, is a programming language used to manipulate, communicate, and manipulate data within Relational Database Management Systems (RDBMS). It is designed for users to specify what they want the database to do rather than how to do it.

Difference Between ETL and SQL

ETL	SQL
ETL is a data integration process, consolidating data from various sources into a data warehouse.	SQL is a programming language used for querying and managing data in relational database
ETL focuses on the extraction, transformation, and loading of data within a data pipeline.	SQL is used for simple and complex queries and operations on datasets.
ETL tool automates the extraction and transformation processes, improving efficiency.	SQL requires writing manual coding for these tasks.

How ETL and SQL Work Together

While ETL and SQL are distinct in their operations, they are, however, frequently used by organizations for data management. 

• SQL is used to write queries to extract, transform, and load data. ETL automates this process, making it more efficient.
• Using ETL with SQL allows data teams to handle large amounts of data in a target data warehouse.
• ETL testing is another process that is facilitated by complex SQL queries.

Common ETL Tasks Performed Using SQL 

Extraction

SQL extracts data from sources like spreadsheets, databases, and CRMs. SQL Queries can filter and retrieve specific datasets, ensuring only important information is collected. For example, we can use SQL query to select customer sales data from a database, allowing for targeted extraction.  See a sample of the SQL query below:

SELECT customer_id, name, email, city
FROM sales;
WHERE sales. city = 'Port-Harcourt'

Transformation

During the transformation stage, SQL functions apply essential changes to the data. This includes aggregations, data cleansing, and using business rules. SQL queries can validate transformations by comparing results against expected outputs. Various data cleansing techniques also allow filtering, sorting, and aggregations. For example, an SQL query can calculate the total sales per customer.

Loading 

In the loading phase, SQL with ETL allows the loading of large transformed data into the target data warehouse, which involves inserting and updating records based on specific conditions. 

By using SQL at each stage of the ETL process, you can handle and prepare data for analytical purposes.

SQL Queries and ETL Testing

SQL Query Testing

• Data Validation: Ensure data types and constraints are implemented in the database schema correctly.
• Performance Testing: Analyze the efficiency of queries, especially with indexes, to optimize performance.

ETL Testing

• Data Integrity Checks: Validate that data extracted, transformed, and loaded maintains accuracy and consistency throughout the ETL process.
• Transformation Validation: Ensure that data transformations meet business rules.
• End-to-End Testing: Test the entire ETL workflow to verify that data flows from source to destination without loss.   

What Are the Best SQL ETL Tools to Use?

1. Hevo: Hevo is a cloud-based data integration platform for ETL solutions and extracting data from multiple sources. It is easy for users to design data pipelines without extensive coding experience. The platform also comes with a user-friendly interface and 24/7 customer support.
2. Airbyte: Airbyte is a free, open-source ETL data integration tool for more mainstream data sources. It has an easy user interface, API, and Terraform Provider.
3. dbForge Studio for MySQL: dbForge Studio for MySQL is an integrated development environment (IDE) and graphical user interface (GUI) tool designed for managing and developing MySQL databases.
4. DBeaver: DBeaver is a free, cross-platform, open-source database management tool with a neat SQL editor. It also provides an Eclipse plugin for developers that use Eclipse Integrated Development Environment (IDE).

You can also explore the 20 best ETL tools to choose the right tool that fits your needs.

How to Choose the Right ETL Tool

• Data Sources: Checking the tool you are considering helps support your company data types and sources.
• Data Volume: Picking/ selecting tools that can handle large volumes and scale for big data management, whether you are a small business or not. 
• User-Friendliness: Look for an intuitive and easy tool for team members without extensive technical knowledge and background.
• Maintenance and Support: Regular maintenance and updates are important to keep your data warehouse running smoothly.

Best Practices for Using SQL in ETL Pipelines

1. Automation: Schedule and automate ETL processes to ensure data freshness and reduce manual intervention.
2. Windowing Functions: To simplify complex queries, use windowing functions for calculations across rows, like running totals.
3. Employ Modularity: Breaking down complex tasks into small, manageable SQL functions to enhance maintainability and efficiency.
4. Optimize SQL Queries: Implement indexing to minimize processing time for data scans to improve performance with large datasets.

Challenges of Using ETL & SQL Together

• Maintenance: Overlooking maintenance is a common ETL and SQL challenge. The ETL process may need to be optimized to handle the increased workload as the volume and complicacy of the data grow over time. 
• Fragile Code:  SQL and ETL use stored procedures, which tend to be fragile and are frequently done outside the Software Development Life Cycle (SDLC).
• Sustainability: SQL and ETL lack long-term sustainability. Data speed is almost impossible when assigned to an organization’s IT person.

ETL With SQL Examples & Use Cases

Supply Chain

ETL can extract logistics data. Using SQL, extracted data can be used to generate new data, like projected arrival and delivery dates.

Human Resources

Using SQL queries to calculate key metrics and build predictive models with machine learning algorithms, human resources teams can transform employee data.

Finance

ETL tools can be used to extract data and calculate financial metrics in companies using queries. This can help in generating financial reports. SQL queries can also be used to filter out company transactions to give a clearer picture of their outgoing expenses.

Conclusion

SQL is a very powerful tool for any data professional involved in the extraction, transformation, and loading of datasets. By employing SQL and its various applications within the ETL pipelines, your organization can extract, transform, and load data into a suitable database. This enables organizations to gain valuable insights from their data and make informed, data-driven decisions to gain a competitive edge.

Hevo simplifies ETL and SQL workflows by automating data ingestion, transformation, and analysis with its no-code platform. With 150+ integrations, it ensures seamless data pipeline management.

Want to take Hevo for a spin?  Sign up and experience the feature-rich Hevo suite firsthand. You can also have a look at the unbeatable Hevo pricing that will help you choose the right plan for your business needs.

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