Any modern company’s lifeline is data. Every day you have to work on transforming data points into information in order to generate profits. It is crucial to have the right building blocks in order to run a successful business.

Here is where ETL and SQL come into play.

Although you might have seen them stacked against one another, you actually get more value from combining ETL and SQL. This post demonstrates SQL and ETL Examples, while providing an excellent resource for data-related efforts.

Before we go into SQL or ETL examples, let’s first define the terms.

What’s SQL?

Structured Query Language ( SQL) is a wide range of syntactic terms used to pass directives for managing data in databases. Database management systems receive SQL commands and can perform a variety of actions on specific tables and rows.

“INSERT INTO” Customers (CustomerName. City. Country).
SELECT SourceName, Country From SuppliersWHERE Canada’s; 

Here’s an example of a SQL query.

The query above instructs the database management software to create new records in a table called Customers.

These new rows are generated from a table called ” suppliers”

The brackets indicate which columns will be filled by the corresponding data from the selected table. However, only the ” Canada” column is required to show ” Canada.”

When you need to extract data from a database, it is helpful to know the basics of SQL commands. You’ll soon realize how common SQL is in mainstream database management systems. Since its initial release in 1974, the list has been growing.

Just to name a few:

1.Microsoft SQL Server MS SQL Server (MS SQL Server) is a relational data management system (RDBMS), developed by Microsoft. This product can be used to store and retrieve data for other applications. You can run it on the same computer as another over a network.

2.Microsoft Access Microsoft Access is much simpler to use than client-server database applications. MS Access is a popular personal computer program. The software does not require any special training.

3.Postgres – PostgreSQL, a powerful open-source object-relational databases system, has over 30 years of experience in active development. It has a strong reputation for its reliability, robustness and performance.

4.MySQL All You Need To Know MySQL is one the most well-known technologies in the modern big-data ecosystem. It’s often called the most widely used database, and is currently being used in a wide range of industries. Anyone involved with IT or enterprise data should have a basic knowledge of MySQL.

5.Oracle Oracle provides a relational database management software.

6.Aurora – Aurora MySQL makes it easy and affordable to create, manage, and scale new and existing MySQL installations. This allows you to concentrate on your business and your applications.

What’s ETL?

These are the pertinent questions: “What is ETL and what does it have with anything?”

ETL is a collection of events that occur along a data flow. This will help you to understand how it works. These events are the extraction and transformation of data. You will need a source from which you can obtain subject data, a process to transform it and a destination to store the results.


The first stage of ETL workflow often involves extraction. This can include database management systems, metrics sources, or simple storage methods like spreadsheets.

SQL commands can also be used to facilitate ETL in this area, as they pull data from separate tables or databases.


The data transformation bit is perhaps the most important part of an ETL process. Transforming data can be as simple or complex as sorting or removing parts from a batch of data, or running calculations to create new knowledge from the extracted source.

In either case, both the input and the output of the transformation should be able to make a business case. Here is where ETL tools can make a difference.


Exiting an ETL process creates reports, or pushes new data/information into dashboards. This would likely result in new items being added to databases for business purposes.

Loading takes place at pre-defined times. This depends on the data’s time sensitive nature. If data isn’t too large, it could be done in real-time. Large batches can often run when there are few connections to the database.

As a consequence

A good example of ETL would be to use sales records for the production of analytic reports. These data flows with any business model and the data extraction process involves raw data such as dates and sales volumes. Next, the transformation stage can incorporate website traffic, analytics and Salesforce leads data to create visualizations that aid in making quick decisions.

Sometimes, all it takes to make decisions is to glance at an ETL dashboard.

How do you choose an ETL tool

You should learn more about the market before you decide on an ETL tool for your data workflow. There are many tools that can only do a superficial job of integrating with your business and being compatible.

Take a look at the following ETL tool characteristics to get an idea of size:

  1. Automation options – Look for ETL tools that are easy to integrate with your business data points. A no-code workflow creation experience is the best. Drag-and-drop components into an ETL workflow. The entire ETL process should not require too much technical attention, if any, to ensure that the data is fresh and relevant.
  2. A complex data transformation suite. The best ETL tools offer prewritten SQL commands that can transform data into useful information. Integrations with external tools may be available that allow for complex data transformations. This could be used to connect sales data from site (as in the previous example) with Salesforce services.
  3. Automatic compliance: It is essential that you comply with all regional laws and regulations, even when you are transforming data. This applies to a broad range of businesses, including non-profits and high-volume retailers.

To ensure smooth operation after the demo phase, it is a good practice to run test with copies of real data before you adopt any ETL tool. This helps you determine if there are technical issues that need to be addressed.

ETL and SQL examples and use cases

You now have a good understanding of SQL and ETL. Let’s merge our efforts to maximize the potential of your databases.

Let’s be more specific. You hire someone who can code SQL. Is this enough skill? It is hard to say “no” when it comes down to it.

Administrators and data engineers should be able to use ETL tools. One should be able build and maintain an entire ETL workflow.

Data Warehousing

ETL workflows often include a data warehouse solution. This not only makes space for historical data inclusion in decision-making, but also provides the compute required for complex data transformation. Data warehousing typically produces connections that simple SQL commands would miss.

Consider the use case of integrating social media ads (Facebook ads) with data warehouse tools that have strong analytical power. This will help to maintain a high return on ads spend. For easy maintenance and connection, this could be any one of the API-accessible accounting programs.

This output can be more complex than the default Facebook dashboards, but it is easy enough to see when to increase spending.

Final thoughts: Do I need ETL?

ETL is essential for even the most simple datasets. This will limit your business’s potential. ETL is the foundation of new areas and methods of data analysis for decision-making.

These fields include Machine Learning and Artificial Intelligence. They are rapidly revolutionizing businesses around the globe.

ETL should be an integral part of data management. ETL is a must-have for any business that wants to survive and thrive.

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