What is ETL (Extraction, Transformation and Loading)?

There are many use cases for extraction, transformation, and loading (ELT) within a business. For example, you might want to change databases and need to transfer the data. You might also need to move customer information from one product to another. This can be within the same company or even from one company to another.

ETL is a three-step process. It begins with collecting data from different sources (extraction). You then need to change this data, for example, by ensuring that it’s in the correct format. This is the second step, transform.

Eventually, you need to store it in the new system or destination (loading).

In this article, we answer the question: What is ETL? We then look at why ETL is important and the benefits of using it.

We also explore extract, transform load (ETL) tools and discuss what is ETL process. We learn how to use ETL in Power BI and answer some frequently asked questions.

What is ETL?

Let’s begin by answering the question, “What is ETL?” ETL is a process that involves:

collecting data,
processing it,
and preparing data for analysis, reporting, or storage in a data warehouse.

During collection, we work with raw data. We then process or transform it, making it more suitable for analysis and reporting. Eventually, we load the transformed data into a target system, for example, a data warehouse.

Why is ETL Important?

ETL (extraction, transformation, and loading) is crucial for data integration, warehousing, and data-driven decision-making within organizations.

ETL is important as it ensures:

Data Consistency: ETL processes ensure data consistency by cleansing and transforming data from various sources into a common format. This prevents inconsistencies and discrepancies, allowing for accurate reporting and analysis.
Data Quality: Data cleansing and validation are frequently part of ETL procedures. These improve data quality by identifying and addressing issues like missing values, duplicates, and incorrect data. High-quality data is essential for making informed decisions and avoiding errors.
Data Integration: Organizations typically have data scattered across various systems and formats. ETL facilitates the integration of this disparate data into a centralized repository or data warehouse. This integrated view of the data enables cross-functional analysis and reporting.
Historical Data Preservation: ETL processes can include the loading of historical data. This ensures that organizations maintain a historical record of their data. This historical perspective is valuable for trend analysis, compliance, and auditing purposes.
Performance Optimization: ETL can involve data aggregation and indexing, which improve query performance. When working with enormous datasets, this optimization is essential. It allows for faster data retrieval and analysis.

Benefits of ETL

Using extraction, transformation, and loading (ETL) in your data management process offers several significant benefits:

Automation: ETL workflows can be automated, reducing manual data handling tasks and saving time and effort.
Scalability: ETL processes can scale to handle large volumes of data. This ensures that your data infrastructure can grow with your needs.
Consistency: ETL ensures that data is consistently transformed and loaded. This reduces the risk of errors in reporting and analysis.
Business Intelligence: ETL is crucial for business intelligence and analytics, providing clean, structured data for reporting and visualization.
Data Security: ETL processes can include data security measures, helping protect sensitive information during transformation and loading.
Cost Reduction: By streamlining data processes and improving data quality, ETL can lead to cost savings in data management.
Compliance: ETL processes can help ensure data compliance with regulatory requirements, reducing legal risks.
Competitive Advantage: With faster access to high-quality data, organizations can make data-driven decisions more effectively, gaining a competitive edge.
Real-time Data: Some ETL solutions support real-time or near-real-time data processing, enabling quicker decision-making.

How Does ETL Work?

What is ETL that behind-the-scenes magician making sure data gets from point A to point B in the smoothest way possible? Let’s break it down in simple terms:

Extract (E)

What it does: Imagine E as the detective gathering clues from different places, like databases, applications, or files.
How it works: It uses tools like SQL queries or APIs to fetch data or directly talk to the sources to get the needed information.

Transform (T)

What it does: Think of T as the makeover artist, turning raw data into something that fits nicely into its new home.
How it works: It cleans up data, applies rules, converts types, and does other tricks to make the data look its best. What are ETL tools act like a magic wand, making these transformations easy?

Load (L)

What it does: Picture L as the delivery person, taking the transformed data to its new home, usually a data warehouse or data mart.
How it works: It loads data in different ways, either in big batches for lots of data or bit by bit for changes. The process is optimized to be quick without causing any disruptions.

Automation and Scheduling:

What it does: Think of this as having a personal assistant who does the job regularly without being asked.
How it works: ETL processes can be set to run automatically at specific times, making sure data is always up-to-date without anyone having to remember to do it.

Error Handling and Logging

What it does: Imagine having a superhero that catches mistakes and keeps a record of everything it does.
How it works: ETL processes are smart, they detect errors and keep detailed logs of what happened. This helps in fixing things when something goes wrong and keeps a record for checking later.

Data Quality Assurance

What it does: Think of this as a data guardian making sure only the best-quality data enters the new home.
How it works: ETL processes check if the data meets certain quality standards. If there’s an issue, it can fix it or alert someone to take care of it.

Scalability and Parallel Processing

What it does: Picture this as having more helping hands when things get busier.
How it works: ETL processes are built to handle more data as the need grows. Modern tools can also process data faster by doing multiple things at once.

Metadata Management

What it does: Imagine having a map that shows where everything is and how it got there.
How it works: ETL tools keep track of metadata, which is like a map of the data journey. It helps understand what’s happening and how changes might affect things.

In simple terms, ETL is like a careful process making sure data moves, gets a makeover, and settles into its new home smoothly. It’s the unsung hero behind the scenes, ensuring everything runs like clockwork for efficient data analysis and reporting.

ETL Vs. ELT

What is ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) represent two distinct approaches to data integration, each characterized by a unique sequence of core operations. Let’s explore the intricacies that differentiate ETL from ELT:

ETL (Extract, Transform, Load)

Extract: In ETL, the process commences with the extraction of data from diverse source systems, spanning databases, applications, and various repositories. This extraction is executed through tools and processes employing methods like SQL queries, APIs, or direct connections.

Transform: Following extraction, the data undergoes a transformative phase to align with the requirements of the target system or data warehouse. This entails tasks such as cleaning, validating, applying business rules, and converting data types. ETL tools provide an intuitive graphical interface for designing and executing these transformations.

Load: The transformed data finds its destination in the target system, typically a data warehouse or data mart, where it becomes readily accessible for querying and analysis. Loading mechanisms vary, offering options like bulk loading for substantial data volumes or incremental loading for only the changed or new data.

ELT (Extract, Load, Transform)

Extract: Similar to ETL, ELT initiates the process by extracting data from source systems. This involves leveraging familiar methods like SQL queries, APIs, or direct connections to retrieve data from source systems.

Load: In ELT, a departure occurs as the extracted data is loaded directly into the target system without immediate transformation. This raw data is deposited into the target system, often a data lake or data warehouse.

Transform: Transformation activities unfold after the data has been loaded into the target system. Capitalizing on the computing power and capabilities of the data warehouse, this phase allows for distributed processing and harnesses the scalability offered by modern cloud-based data platforms.

Key Differences

Data Location:

ETL: Transforms data before loading it into the target system.
ELT: Loads raw data into the target system and performs transformations within that system.

Tool and Processing Requirements:

ETL: Requires robust ETL tools to handle complex transformations.
ELT: Leverages the processing power of the target system, often a cloud-based data platform.

Data Storage:

ETL: Transformed data is stored in the target system.
ELT: Raw and transformed data coexist in the target system.

Choosing Between ETL and ELT

ETL is often preferred when:

Transformations are intricate and resource-intensive.
Transformed data needs to be stored in a different location than the source data.

ELT is often preferred when:

The target system, such as a modern cloud-based data warehouse, provides substantial processing power.
Raw and transformed data can seamlessly coexist in the same storage system.

The choice between ETL and ELT hinges on factors such as the complexity of transformations, the capabilities of the target system, and specific business requirements. Visual Analytics plays a crucial role in this decision, as each approach brings its unique strengths, rendering them suitable for distinct scenarios in the data integration process.

What is the Best Tool for ETL?

What is an ETL tool that should efficiently transfer and transform large volumes of data?

It should also support multiple data sources. This way, you can easily combine datasets from disparate systems into a centralized repository.

An intuitive user interface is also key for quickly manipulating data, configuring settings, and scheduling tasks.

The choice of what is ETL (extraction, transformation, and loading) tool depends on:

your specific requirements,
level of automation,
budget,
security and compliance,
the complexity of your data integration tasks,
and the performance and reliability of the tool.

There are several popular ETL tools available, each with its strengths. Here are some of the best:

Informatica PowerCenter

Informatica PowerCenter is a data integration platform. It is used to build, deploy, and manage complex data pipelines. You can use it to extract, transform, and load data into target systems.

Power BI

Power BI has a built-in ETL tool called Power Query Editor. It is a powerful tool that allows you to connect to a wide variety of data sources. You can extract data from those sources and then transform it to meet your needs.

The Power Query Editor provides a visual interface for performing data transformations. In addition, you can perform more complex transformations by writing code in the M language. The M language is a powerful programming language that is specifically designed for data manipulation.

After transforming data in the Power Query Editor, you can load it into a Power BI dataset. You can then use Power BI to create reports and dashboards to analyze your data.

AWS Glue

AWS Glue is a serverless data integration service. It makes it easy to discover, prepare, move, and integrate data from multiple sources.

The data can then be used for analytics, machine learning (ML), and application development. AWS Glue provides:

a centralized data catalog,
a visual ETL builder,
and a variety of pre-built connectors and libraries

These help you get started quickly.

AWS Glue is serverless. This means you don’t have to worry about managing hardware or scaling your ETL jobs.

Azure Data Factory

Azure Data Factory allows users to integrate their data sources with more than 90 built-in, maintenance-free connectors. All this at no added cost.

You can visually integrate data sources and easily construct ETL processes code-free in an intuitive environment. You can write your code, too.

Oracle Data Integrator

Oracle Data Integrator (ODI) provides a unified solution. You can build, deploy, and manage complex data warehouses.

ODI provides a powerful transformation engine that can transform data from any source to any target. It also supports complex data transformations such as data mapping, data filtering, data aggregation, and data enrichment.

Understanding Data Extraction

Extraction involves the retrieval of data from multiple sources. These include databases, spreadsheets, web services, logs, or any other data storage or generation system. Data extraction can be done periodically to ensure the data in the central repository is up-to-date.

During this phase, data is often collected in its raw form. Data quality, integrity, and accuracy are therefore very important even before beginning the ETL process.

Understanding Data Transformation

Transformation is the process of cleaning, structuring, and enriching the raw data extracted from source systems. This makes it suitable for analysis and reporting.

Common transformation tasks include:

data cleansing (removing duplicates and handling missing values),
data enrichment (adding calculated fields),
data aggregation (summarizing data),
and data format conversions.

Data quality checks and validation may also be performed during this stage. This ensures that the data is accurate and consistent.

Understanding Data Loading

Here, the transformed data is loaded into a central data repository. This could be a data warehouse, data lake, or database optimized for analytical queries.

Depending on the data warehousing solution, there are different loading strategies. These include:

full loading (loading all data),
incremental loading (only loading new or changed data since the last extraction),
or historical loading (loading historical data to maintain historical records).

There is also an additional optional step called mapping. Incoming data elements map to pre-existing elements on the destination system.

Additional steps in the ETL process include:

Validation and error handling
Metadata management
Monitoring and maintenance

How to do ETL in Power BI?

In this section, we’ll see an ETL example in Power BI. We’ll use the Sankey Diagram (Sankey Chart) as an example.

Stage 1: Logging in to Power BI

Log in to Power BI.
Enter your email. Click the “Submit” button.

You are redirected to your Microsoft account.
Enter your password and click “Sign in”.

Choose whether to stay signed in.

Once done, the Power BI home screen will open.

Stage 2: Creating a Data Set and Selecting the Data Set to Use in Your Sankey Chart

Click on the “Create” option on the left-side menu.
Select ”Paste or manually enter data“.

select Paste or manually enter data in Power BI ce421

We’ll use the following sales data

Segment	Country	Product	Profit
Government	Canada		$16,185.00
Government	Germany	Carretera	$13,210.00
Midmarket	France	Carretera	$10,890.00
Midmarket	Germany	Carretera	$4,440.00
Midmarket	Mexico	Carretera	$12,350.00
Government	Germany	Carretera	$136,170.00
Midmarket	Germany	Montana	$4,605.00
Channel Partners	Canada		$22,662.00
Government	France	Montana	$18,990.00
Channel Partners	Germany	Montana	$13,905.00
Midmarket	Mexico	Montana
Enterprise		Montana	$13,327.50
Small Business	Mexico	Montana	$47,900.00
Government	Germany	Montana	$4,292.00
Enterprise	Canada	Montana	$1,725.00
Midmarket	United States of America		$3,075.00
Government	Canada	Paseo	$2,920.00
Midmarket	Mexico	Paseo	$4,870.00
Channel Partners	Canada	Paseo	$22,662.00

We’ll use the following dataset to illustrate the ETL process. ETL involves extracting data from various sources. We then clean it up, transform it, and load it. We’ll then load it into Power BI and create a data visualization like a Sankey Diagram.
Paste the above data table into the “Power Query” window.

Select the “Create a dataset only” option as shown below.

Click on the “Data Hub” option on the left-side menu.
Power BI populates the data set list. (If you have not created a data set, refer to the Error! Reference source not found section.)
The data details are shown below:

Click on the “Create a report” dropdown as shown below.
Select “Start from scratch.”

Create Report and start from scratch ce421

You should see the Report Canvas screen as shown below:

Stage 3: Adding the Power BI Sankey Diagram Extension by ChartExpo

To finish creating our Sankey Diagram, we’ll use an add-in or Power BI visual from AppSource.
Navigate to the Power BI Visualizations panel.
Click the ellipsis (…) highlighted above to import the Power BI Sankey Diagram extension by ChartExpo.

The following menu opens:
Select the “Get more visuals” option.
The following window opens:

Enter “Sankey Diagram for Power BI by ChartExpo” in the highlighted search box.
You should see the “Sankey Diagram for Power BI by ChartExpo”, as shown in the image below.

Sankey Diagram for Power BI by ChartExpo

Click the highlighted “Add” button.

Power BI will add the “Sankey Diagram for Power BI by ChartExpo” icon in the visualization panel.

Stage 4: Drawing a Sankey Diagram with ChartExpo’s Power BI extension

Select the “Sankey Diagram for Power BI by ChartExpo” icon in the visualization panel.
The following window opens in the report section of your dashboard:

You can resize the visual as needed.
Go to the right-hand side of your Power BI dashboard.

You’ll select the fields to use in your Sankey chart here.
The ChartExpo visual needs to be selected, though.
Select the fields in the following sequence:
- Product
- Country
- Segment
- Profit

You’ll be asked for a ChartExpo license key or email address.

Stage 5: Activate your ChartExpo Trial or Apply a Subscription Key

Select the ChartExpo visual.
You should see three icons below “Build Visual” in the Visualizations panel.

Select the middle icon, “Format visual.”
The visual properties will be populated as shown below.

If you are a new user,
- Type in your email under the section titled “Trial Mode”.
- This should be the email address that you used to subscribe to the ChartExpo add-in. It is where your ChartExpo license key will be sent.
- Ensure that your email address is valid.
- Click “Enable Trial.” You’ll get a 7-day trial.

You should receive a welcome email from ChartExpo.
The Sankey Diagram you create under the 7-day trial contains the ChartExpo watermark (see below).

If you have obtained a license key:
- Enter your license key in the “ChartExpo License Key” textbox in the “License Settings” section (see below).
- Slide the toggle switch next to “Enable License” to “On“.

To add colors, expand the “Level Colors” properties and select a color.
Do this to change the color of each node.
All changes are automatically saved.

Your final chart should look like the one below. If you get a license, the Sankey Chart will not have a watermark.

Insights

Based on the chart, the following are the data insights:

The highest profit was made by the Government segment with a profit of $191.77k.
The lowest profit was made by the Enterprise segment with a profit of $15.05k.
The most profitable product is the Carretera, with a total profit of $177.06k.
The most profitable country is Germany, with a total profit of $176.62k.

FAQs

What is the extract, transform, and load process?

The extract, transform, and load (ETL) process is a critical component of data integration and data warehousing.

It’s a set of procedures used to collect data from various sources. You then transform it into a format suitable for analysis. Eventually, load it into a destination, typically a data warehouse or a data lake.

What is extraction, transformation, and loading (ETL) an important component of?

The process of extraction, transformation, and loading (ETL) is an important component of various aspects of:

data management,
data analytics,
and business intelligence.

Wrap Up

In conclusion, extraction, transformation, and loading (ETL) are essential pillars of data integration. They play a pivotal role in data-driven decision-making processes.

ETL is not just a technical process. It’s a strategic approach to ensuring that data is extracted efficiently from various sources.

It is then transformed into a usable format. Ultimately, it’s loaded into a destination where it can be analyzed and leveraged to derive valuable insights.

In this article, our main focus has been answering the question: what is extraction, transformation, and loading?

We have looked at why ETL is important and the benefits of using it. We also looked at extract, transform, and load tools and determined what makes a great ETL tool.

We then looked at the extract transform, and load process. Ultimately, we learned how to do ETL transformation in Power BI. We used ChartExpo’s Sankey Diagram to illustrate this. We also answered some frequently asked questions that you might still have.

We hope that you now understand some basic what is ETL concepts and can embark on your ETL journey.

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