What Is ETL and How Does It Differ From ELT?

ETL and ELT data integration methods help businesses facilitate the integration of diverse information sources into a single place in a consistent and reliable manner. Do not confuse them, though: while both processes are aimed at data integration, their way of work differs.

So what is ETL exactly, and what are the main things to know about it? Read below to find out.

A Brief History of ETL and ELT 

Let’s look back now. ETL is a standardized data integration method that has its roots in 1970. As enterprises adopted multiple computer systems and data sources, ETL became even more critical. Businesses needed to aggregate and centralize information from transactions, payroll systems, and other systems to manage day-to-day business workflows.

Data warehouses emerged in the 1980s, making ETL even more important. Many tools were developed to help load information into the warehouses. Granted, the volume of information they handled was small compared with today’s standards. Thus, ETL processes became affordable for medium-sized businesses, rather than only large companies, at the end of the 1990s.

Nevertheless, data storage and processing changed forever in the 2000s with cloud computing. Data lakes and data warehouses sparked a new evolution: ELT. Businesses could load a limitless amount of raw information directly into a cloud DWH (data warehouse system) using ELT. Therefore, engineers and analysts could perform endless SQL queries and real-time analytics to make data-driven decisions.

How does the ETL process work?

ETL (Extract, Transform, Load) is a traditional method to integrate structured or relational data from multiple sources into a cloud-based or on-premises data warehouse. ETL is used to collect, reformat and store legacy information or to aggregate it for business analysis. Here is how it works.

Extraction

During data extraction, the information is collected from siloed systems within an organization from a variety of sources and formats. Among the most common sources are:

• Databases
• Legacy or existing systems
• Cloud systems
• Sales or marketing apps
• Mobile devices
• Analytics tools
• CRM systems

Upon extraction, data is initially held in a staging area between the data sources and the warehouse. The information is monitored and sorted here.

Transformation

All information in the staging area is raw. Thus, to transfer information to the warehouse, it’s necessary to convert it into a uniform format. The transformation stage consists of many processes, including but not limited to the following:

• Cleansing: removing missing values and inconsistencies;
• Standardization: establishing data format rules;
• Deduplication: removing duplicate or unnecessary information;
• Verification: identifying anomalies and remove unused information;
• Sorting: sorting the information according to its type;
• Enrichment: adding external information or context (metadata) to the existing one. 
• Transformation is an essential part of the ETL process. As a result of it, raw information is fully ready to use (analyze, report, etc.).

Loading

At the end of the ETL process, a large amount of information is transferred to the warehouse in two ways:

• Full: deletes all existing data in a repository and replaces it with new and transformed information. Data from the incoming load may already exist in the repository but is reloaded along with the new one;
• Incremental: transfers only new, unchanged information to the repository, while existing information stays unchanged and relevant. It loads faster and preserves historical data.

The big benefit of ETL is that analysis can begin instantly after information is loaded. As a result, this process is appropriate for small sets of information that require complex transformations. However, modern ETL tools can also handle big data effectively. 

Now that we’ve answered the question “What is ETL” let’s look at its most common use cases. 

How is ETL commonly used?

Organizations deploy this method to:

• Extract information from legacy systems: it grants companies easier access and analysis of information;
• Analyze historical business information:  it allows businesses to quickly analyze historical business information, identify trends and patterns to make more informed decisions;
• Improve data quality and consistency: the ETL process involves the cleansing, standardization, validation, and incorporation of information to ensure accuracy and reliability of information;
• Integrate all information into a single storage: it allows companies to consolidate all information into a unified view;
• Migrate cleansed information to the cloud: companies can update their information and maintain workload scalability by moving cleaned information from on-premises to the cloud.

ETL is a highly effective way to consolidate your information for better business decisions. To make the process more straightforward and less complex, organizations need to implement suitable tools – we’ll discuss them below.

What are the types of ETL tools?

Currently, there are different types of ETL tools available on the market. To decide which ETL tool is best for your company, let’s review the four main categories:

Enterprise ETL tools

Enterprise ETL tools are typically built for large enterprises and are deployed on-premises or in the private cloud. They are the most reliable and usually offer the most features. The complexity of these tools makes them rather expensive and requires extensive employee training, comprehensive support and maintenance services. Examples of enterprise tools:

• SAP Data Services;
• Oracle Data Integrator (ODI);
• Microsoft SQL.

Open-source ETL tools

Open-source ETL tools are free and can be easily customized to meet users’ needs. These tools offer GUIs for creating data-sharing processes and monitoring information flow, cost-effectiveness, flexibility, and community support. However, they may require more technical expertise for their setup and maintenance. Despite the lack of commercial support, these tools are regularly updated, which adds to their security. Examples of such tools:

• Talend Open Studio;
• Pentaho Data Integration (PDI);
• Hadoop.

Custom ETL tools

Companies can develop their own custom ETL tools that are specifically tailored to meet an organization’s specific needs and requirements. They offer greater control, flexibility, and customization options than other tools. However, developing and maintaining custom tools can be time-consuming and expensive. 

Cloud-based ETL tools

Cloud-based ETL tools are deployed on cloud infrastructure (such as AWS). They are popular among businesses that need to integrate information from various cloud-based data sources, such as SaaS apps, cloud databases, and web services. These tools offer flexibility, scalability, and cost-effectiveness compared to on-premises tools. Examples of such tools:

• AWS Glue;
• Azure Data Factory;
• Microsoft Azure.

Now, let’s switch to why companies should choose this process.

The biggest advantages of the ETL process

ETL is primarily used to consolidate and process information. But if we dig deeper, we can also define more specific advantages.

Holistic view

Since ETL helps to combine legacy information with newly collected one, such an approach offers companies a holistic view of their information. This, in term, helps better analyze internal and external processes and make more accurate business decisions.

Rapid analysis

ETL transforms the information into a unified format and thus, allows users to quickly and efficiently analyze it since the data is structured and transformed. The processed information is much easier to visualize and analyze and, thus, users can obtain more value from it.

High quality 

During the transformation stage, the information undergoes such processes as removing duplicates, error correction, formatting, and many more. Such rigorous cleansing leads to high quality of the information in use, which directly impacts decision-making.

Note, though, that despite all the benefits, the process has certain drawbacks too. 

The biggest challenges of the ETL process 

To avoid any potential issues, organizations must also be aware of ETL challenges, such as:

• High cost: processes can be costly to implement and maintain, particularly for organizations that have limited resources;
• Latency: high ETL workloads can lead to network latency, thus limiting your performance;
• Maintenance: for ETL to function efficiently, it requires ongoing maintenance.

Thus, companies should constantly refine and optimize their ETL pipelines to maintain a high level of performance. It is now time to focus on a similar process that differs from ETL, though they share the same function. 

How does the ELT process work?

ELT (Extract, Load, Transform) process is a newer method that allows organizations to integrate information from various sources and load it into a target system for later transformation. This unstructured, extracted information is immediately available to BI systems without the need to stage it. 

One big difference between ELT and ETL is that the information loads into a data lake (instead of a data warehouse). This is due to the fact that data lakes support structured and unstructured information that comes from various sources, including SQL or NoSQL databases, web pages, and IoT systems. Thus, ELT helps handle and utilize enormous amounts of real-time information for business intelligence and analytics and often helps prevent data silos.

How is ELT commonly used?

ELT can be applied in various industries, including finance, ecommerce, marketing, and healthcare, and its most common use cases are:

• Business Intelligence (BI): enables organizations to analyze their information and gain value in their operations by creating data lakes and marts;
• Processing of large data volumes: it allows businesses to speed up information transfers for high-transaction volumes;
• Real-time data access: allows companies to generate large amounts of information in real-time, without delays, for business intelligence purposes.

What are the tools for ELT?

Like ETL, companies can use plenty of ELT tools to manage their information better. Such tools include:

• Cloud-based ELT: available as a service in the cloud;
• ETL/ELT hybrids: support both ETL and ELT processes;
• Data integration platforms: include ELT processing as part of their capabilities;
• Data preparation: although these tools are not strictly ELT tools, they often have ELT capabilities as part of their workflows for preparing information for analysis.

It’s worth considering:

The biggest benefits of ELT

ELT offers several advantages over ETL thanks to its inherent efficiency, reliability, and scalability. Here are the main reasons why ELT is so effective:

Real-time analytics

With ELT, businesses can gain insights into their data quickly, make informed decisions based on the latest information, and get access to real-time information.

Speed of loading and implementation

Since the information is loaded directly in the storage without being transformed first, ELT provides instant access to it as well as fast information capturing.

Scalability

ELT systems can handle large volumes of information and grow with data volumes over time. This is really great considering how quickly data volumes tend to grow within an organization.

Transformation as needed

In the ELT process, data transformation typically happens only when an analysis is needed, in contrast to ETL, which makes the use of resources more efficient.

Data Availability

ELT loads all information onto the data lake, so it’s always available. It lets companies interact with loaded data immediately instead of waiting for it to transform.

The biggest challenges of the ELT process 

ELT has some drawbacks, such as:

• Lack of support: although ELT has a growing number of tools and professionals, there is not as much community support for it;
• Data security risks: since data is stored with minimal processing, you may need to take additional steps to ensure compliance with data security protocols;
• Complex data integration: integrating data requires a complex process, which is challenging to maintain as information volumes increase;
• Complex data transformations: due to dealing with data from multiple sources and formats, ELT can involve complex data transformations.

As information volume has grown rapidly, ELT offers better agility and lower maintenance, allowing businesses of all sizes to leverage cloud-based storage at a lower cost. 

ETL vs. ELT: how do they differ?

In the table below, we summarized the key differences between ETL and the ELT process:

What should an organization choose?

Now that we are clear on the question “What is ETL?”, let’s see what data integration method will suit you the best. Companies need to consider many factors: budget, scenarios, tool capabilities, data resources, and specific business and technical requirements. When companies have identified their requirements, they must choose one of the processes to keep the following in mind. 

Organizations need to implement the ETL process when:

• Data requires complex transformations;
• There are privacy risks since the ETL process removes sensitive data before loading it;
• The organization is focused on historical information to provide a holistic view of the business;
• Data is in structured format as ETL does not support unstructured information;

 ELT is the right choice when: 

• The availability of data is a priority, the organization needs quick access to it;
• Organizations have access to data analysts of ELT experts; 
• Budget isn’t a problem, as finding and onboarding ELT experts can be expensive;
• Debugging and fixing errors is vital because the ELT method facilitates it. 

If a company has a large data warehouse with complex data transformations, ETL is a better option. Nevertheless, ETL is more flexible and requires more resources to clean and transform information.

However, ELT makes more sense if a company deals with big data processing and analytics. The greatest strength of ELT is its speed and support for diverse types of information. As a result, it is efficient and faster, but requires more raw data processing capability from the target system. Also, ELT facilitates automation, outsourcing, and integration with third parties.