What is ETL? How does ETL work and why use elt?

What is ETL? How does ETL work and why use elt? Picture 1

ETL (Extract, Transform, Load) has been widely used in the field of big data as well as data warehouse, helping to integrate data from many different sources into a target data warehouse. To better understand ETL, how it works and its purpose in today's life, let's find out with TipsMake  in the article below.

What is ETL?

ETL  stands for Extract - Transform - Load. In computing, extract - transform - load (ETL) is the general process of copying data from one or more sources into a target system that represents the data differently from the source. The ETL process became a popular concept in the 1970s and is often used in data warehousing.

Extract is the process of reading data from a database. In this phase, data is collected, usually from a variety of sources.

Transformation  is the process of converting data extracted from a previous form into the form required to be placed into another database. Transformation occurs using rules or lookup tables or by combining this data with other data.

Load  is the process of writing data to the target database.

A properly designed ETL system will extract data from source systems, enforce data quality and consistency standards, conform the data so that separate sources can be used together, and finally deliver the data in a format that is ready for application developers to build applications and end users to make decisions.

Since data extraction takes time, three stages are usually performed in parallel. While data is being extracted, another Transform executes while processing the received data and preparing it for loading while the data loading starts without waiting for the previous stages to complete.

To help you visualize ETL better, we would like to provide you with a visual example of ETL in managing sales data in a shopping mall.

If the user wants to view historical as well as current data in the shopping mall, the first step is always for the user to follow the ETL process. Then that data will be used for reporting purposes.

How ETL Works

In this section, we will take an in-depth look at each of the three steps of the ETL process.

Image showing how ETL works

Extraction Phase - Extract

This is the first part of the ETL process, which involves extracting data from source systems.

Very few businesses use only one type of data or system. Most businesses manage data from multiple sources and use a number of data analysis tools to optimize management. In order for data to be transferred to a new destination, it must first be extracted from the sources.

In the first step of the ETL process, structured and unstructured data are imported and consolidated into a single repository. Raw data can be extracted from a variety of sources, including:

1. Existing database
2. Sales and marketing applications
3. Mobile Apps and Devices
4. CRM Customer Management System
5. Data storage platform
6. Data warehouse
7. Analysis tools

While this data can be processed manually, manually extracting encrypted data can be time-consuming and error-prone. ETL tools automate the extraction process and create a more efficient and reliable workflow.

Transform Phase

During this phase of the ETL process, rules and regulations can be applied to ensure data quality and accessibility. The data transformation process includes several sub-processes:

Data cleansing : aims to transmit only the "correct" data to the target

Standardization - formatting rules applied to the data set.

De-duplicating data - identical data is excluded or eliminated.

Verification - unusable data is deleted and anomalous data is flagged.

Sort - data is arranged by type.

Other actions - any additional rules/options that can be applied to improve data quality.

Transformation is often considered the most important part of the ETL process. Data transformation improves data integrity and ensures that the data arriving at the new destination is fully compatible and ready to use.

Load Phase

The final step in the ETL process is to load the newly transformed data into a new destination. The data can be loaded all at once (full load) or at scheduled intervals (incremental load).

Full Load - During full ETL, the data is entered into new, unique records in the data warehouse. While this is useful for research purposes, full load creates exponentially growing datasets and can quickly become difficult to maintain.

Incremental Loading - A less comprehensive but more manageable approach is incremental loading. Incremental loading compares incoming data to what was previously available and only creates additional records if new and unique information is found. This type of loading is less costly and helps with business intelligence.

The Importance of ETL in Business

Businesses have relied on ETL processes for years to gain a holistic view of data that drives better business decisions.

By providing a consolidated view, ETL makes it easy for business users to analyze and report on data relevant to their initiatives.

ETL can improve the productivity of data professionals because it codifies and reuses data movement processes without requiring technical skills to write code or scripts.

Organizations need both ETL and ELT to connect data together, ensuring data accuracy for reporting.

In short, ETL tools are the essential first step in the data warehousing process, allowing you to make better decisions in less time.

Types of ETL tools available today:

1. Hand coding
2. Batch processing tools
3. Open source tools
4. Cloud-based tools
5. Real-time tools
6. Which ETL tool is right for your organization?

Different types of ETL tools are suitable for different needs. Depending on the needs 

- Batch processing ETL tools:

If real-time data processing is not a high priority, then ETL batch data processing can be both fast and efficient. 

- Open source tools:

As a low-cost, alternative to commercial software packages, open source ETL works well for organizations that operate and maintain software, want to avoid proprietary software, and don't need to perform complex data transformations.

- Cloud-based ELT tools: 

If your business prefers cloud-based management tools, then you should opt for cloud ETL. Cloud-based tools can be hosted in the cloud as SaaS or deployed directly into your own cloud infrastructure. 

- Real-time tools: 

If you need to transform and manage big data or stream data in real-time, then a real-time ETL tool is for you. However, keep in mind that not all data needs to be processed in real-time.

Benefits of ETL for businesses

ETL helps businesses have a broader and deeper view of historical data, from which businesses can combine old and new data from modern platforms and applications to have a long-term and comprehensive view of information.

Data consolidation

Data management is time-consuming and requires coordination between multiple departments within a company, resulting in inefficient data processing. ETL combines different databases and data formats into a unified view. This helps improve data quality and reduces the time spent moving, sorting, and standardizing data. Businesses can easily analyze data and create detailed reports.

Accurate data analysis

Because ETL has the ability to analyze data accurately, meeting standards and regulations, when integrating ETL with data quality control tools, businesses can create records, check and clean data, ensuring that the data has been authenticated. 

Task Automation

ETL helps businesses automate repetitive data processing tasks, thereby increasing analysis efficiency. ETL can automate data migration. In addition, if you do not want ETL to automatically move data, businesses can schedule to integrate data changes periodically or during the run. Employees will have more time for data analysis, less time for data migration or standardization. 

How did ELT develop?

ETL originated from relational databases. The original purpose of ETL was to transform data from transactional to relational format for easier analysis.

Traditional ETL

Traditionally, raw data was stored in transactional databases, which were read and written but not very convenient for analysis. These data were similar to spreadsheets, such as in e-commerce systems, which store information about transactions, customers, and orders, resulting in duplicate data. This made it difficult to analyze popular items and purchasing trends.

To overcome this, ETL tools convert transactional data into relational form, allowing analysts to identify relationships and analyze trends more easily.

Modern ETL 

With the development of ETL technology, the scale of data and data sources increased. Cloud technology was born, allowing the creation of large Data Warehouses, capable of receiving data from many sources and expanding hardware over time. Modern ETL tools are also increasingly sophisticated, capable of transforming data from classic to modern formats. Below are examples of modern data warehousing systems.

A data warehouse is a storage center that can accommodate multiple databases, with data organized into tables and columns. Data warehouse software is compatible with a variety of storage hardware such as SSDs, hard drives, and cloud storage, to optimize data processing.

A Data Lake allows storing all data, including structured and unstructured data, in a centralized place and at scale. Users can store data in its raw form without any prior structuring, which provides the flexibility to mine and analyze data using different methods such as SQL queries, big data analytics, full-text search, real-time analytics, and machine learning (ML). 

What is data extraction?

During data extraction, the ETL (Extract, Transform, Load) tool collects raw data from multiple sources and temporarily stores it in an area called a buffer. The buffer holds this data until the process is complete, but can also be used as a backup source.

The transfer of data from source to repository depends on how changes are captured and tracked. Extraction can occur in three main ways:

1. Update Notification: The source system notifies about record changes, allowing data to be extracted only from those changes.
2. Incremental extraction: Some data sources have the ability to identify data that has been modified over a given time period, allowing support for cyclic extraction.
3. Full Extraction: When the system cannot detect the change, the entire data must be reloaded. This method is usually only applicable to small data tables due to the requirement of full transmission.

What is data transformation?

During data transformation, ETL tools perform the transformation and consolidation of raw data from the buffer to prepare it for the target data warehouse. This process can include many different types of data transformations.

Basic data conversion

1. Data cleaning: Includes removing errors and ensuring that the data matches the target format. For example, a blank data field might be converted to a value of 0, or words like "Parent" and "Child" might be mapped to "P" and "C".
2. Data Deduplication: Focuses on identifying and removing duplicate records to improve accuracy.
3. Modify data formats: Change measurement units and date/month/year formats to be consistent. For example, convert units from kilograms to pounds.

Advanced data conversion

1. Derive: Apply business rules to create new value from existing information, such as calculating profit by subtracting costs from revenue.
2. Aggregation: Connect similar data from different sources to calculate total costs from suppliers and store a single result.
3. Split: Split a column into multiple columns in the target system. For example, split the full name "Jane John Doe" into columns for last name, middle name, and first name.
4. Aggregation: Improve data quality by combining multiple values ​​into a smaller data set, such as summing customer bills to build lifetime value (CLV) metrics.
5. Encryption: Protect sensitive data before it is transmitted to the destination data warehouse, to comply with information security regulations.

This data transformation process helps optimize and prepare data for more efficient analysis.

Some other useful information about ETL

History of ETL Development

ETL became popular in the 1970s when organizations began using multiple data warehouses or databases to store different types of business information. The need to integrate data spread across these databases grew rapidly. ETL became the standard method for taking data from different sources and transforming it before loading it into the destination source.

In the late 1980s and early 1990s, data warehouses emerged. A distinct type of database, data warehouses provide integrated access to data from multiple systems—mainframes, minicomputers, personal computers, and spreadsheets. But different departments often chose different ETL tools to use with different data warehouses. With mergers and acquisitions, many organizations ended up with several different, non-integrated ETL solutions.

Over time, the number of data formats, sources, and systems has expanded dramatically. Extract, transform, and load are now just some of the methods organizations use to collect, ingest, and process data. ETL and ELT are both important parts of an organization's broader data integration strategy.

However, ETL and ELT are two concepts that are easily confused. Let's find out the information below with TipsMake to distinguish them.

Compare the difference between ETL and ELT

1. ETL is the process of extracting, transforming, and loading data. ELT is the process of extracting, loading, and transforming data.
2. In ETL , data moves from data source to data warehouse.
3. ELT leverages the data warehouse to perform fundamental transformations. No data staging required.
4. ETL can help with data security and compliance by cleaning sensitive and secure data even before loading into the data warehouse.
5. ETL can perform sophisticated data transformations and can be more cost effective than ELT. 
6. ETL can help with data security and compliance, cleaning bad data before loading into data destinations, while ELT is simpler and intended for companies with small data needs.

Hope the information about ETL that TipsMake provides is useful to you.

According to TipsMake synthesis

Marvin Fry

Update 12 February 2025