July 29, 2022
10 minutes read
Information Insight

ETL, ELT, and More: 6 Ways to Manage Multiple Data Sources

ETL, or extract, transform, load, is not just another random business acronym. ETL is a process by which multiple data sources are brought together in one centralized—or a few disparate—databases. This ETL process involves three quintessential steps: 

  1. The extraction of data from its original source
  2. The transformation of data through deduplication, combination, and quality assurance
  3. The loading of data into the final, central database.

The ETL process might sound overly complicated, but rest assured that it is a necessary process for businesses that want to capitalize upon their data. Furthermore, tools exist that help companies undergo the ETL process in the smoothest way possible. Alongside tools, often it is best to also seek out expertise from a partner that specializes in data management and integration. These partners get to know an organization’s unique needs and culture in a way that allows them to craft strategies that best fit a business’s goals.

Before detailing any of these tools and partners, however, let’s dive a little deeper into what ETL means in the context of business. We’ll also cover the difference between ETL vs. ELT and other methods of integrating data from multiple sources.

What is ETL in Business?

ETL is crucial for businesses that want to optimize their ability to analyze their data. Not only does ETL take multiple sources of data and consolidate them into one spot, but ETL also can enable multiple types of data to work together. 

ETL tools make this consolidation possible by enabling the migration of data between a variety of sources, destinations, and tools. But how exactly does this occur?

The Steps of ETL


In this initial step, the desired data—whether structured or unstructured—is imported from a data warehouse or a data lake to a single repository. Some common data sources included in this stage are:

  • Databases and legacy systems
  • Cloud, hybrid, and on-premise environments
  • Sales, marketing, and other business applications
  • Mobile devices and apps
  • CRM systems
  • Data warehouses, lakes, and storage platforms
  • Analytics tools

After the various desired sources of data are identified, data extraction can happen in one of the following ways:

  • Update notification – notifies you when a change to a record has been made.
  • Incremental extraction – identifies which records have been modified after they have been changed.
  • Full extraction – reloads all the data altogether, regardless of changes made.


The half-way point of ETL is the process of transformation. It is the actual cleansing and deduplication necessary for data to undergo to prepare it for effective and accurate analysis. This is the most important and often the most arduous step of ETL, including several key stages similar to these:

  • Cleansing – resolving inconsistencies and missing values in data.
  • Standardization – applying formatting paradigms to datasets.
  • Deduplication – discarding redundant data.
  • Verification – removing of unusable or anomalous data.
  • Sorting – organization of data according to type.

Transformation is a particularly crucial step of the ETL process to get right because it significantly improves data integrity by ensuring that various types and sources of data reach their end destination in viable and ready-to-use forms.


How a load occurs in the ETL process is dependent mostly on how a company plans on utilizing their data. It is crucial to take into account the end host system’s functionalities and how it operates to ensure the function of the system isn’t negatively impacted.

Depending on what host system a company uses, there are generally two different ways to load data into a data warehouse:

  • Full load – the first time a data source is loaded into the warehouse, all data is dumped at the same time.
  • Incremental load – in order to minimize time spent and maximize effort, incremental loading is designed based on when data was created or modified. In order to identify any change in data, incremental loading compares the data in a target system with the original data source.

ETL is just one approach (with different microvariations) to integrating data from multiple sources.


ELT is similar to ETL, but the order of operations is different. Instead of transforming data before exporting it to the final host system, the data is imported raw to be transformed as needed later on.

ETL vs. ELT Pros and Cons

When deciding whether to choose ETL vs. ELT,  several factors must be considered. Both approaches have their pros and cons. 

ETL has been in use longer than ELT, so there are numerous well-established processes and tools to implement it. ETL is also more flexible, since it can be implemented in both on-premise and cloud environments. Because data is transformed before being loaded, it allows you to remove or encrypt sensitive data before it reaches the target ETL database

The downside to ETL is that data is unavailable while undergoing the transformation process. It’s also not suitable for large volumes of data since the transformation stage takes so much time. 

ELT, on the other hand, allows for immediate access to data. All data, whether it’s unstructured or not, is immediately transferred to a data lake, where it can be transformed as needed. While ELT can be more efficient than ETL, if the process involves large volumes of unstructured data, it can be hard to analyze and report on that data. There are also limited tools available to support ELT, and it’s ideally suited to the cloud. 

Because ELT can offload some processing from the ETL infrastructure, it is often used for transfers that involve big data and modern cloud-based data warehouses that have powerful processing capabilities. ETL, on the other hand, is often used in scenarios where data requires significant cleansing and manipulation before being loaded into the target system.

Other Data Integration Methods

Other ways to facilitate data integration include:

  • Change Data Capture (CDC) – identifies and captures just the source data that has been changed and moves that data to the target system.
  • Data replication – copies the changes in multiple data sources in real time or periodically to a central database. 
  • Data virtualization – removes the need to have a transformation step at all. Instead, data is displayed in a software abstraction layer that alters how the data is viewed without altering the actual data itself.
  • Stream data integration (SDI) – continuously consumes, transforms, and loads data streams, which creates a data store for powering things like machine learning that can be used to improve customer experience, fraud detection, etc.

Whichever data integration method a company uses, the same truth applies: Companies need to consider integrating their data in order to gather and utilize profit-boosting and efficiency-improving analytics.

What are ETL Tools?

ETL tools are various software programs designed to make data implementation processes easier. There are four common type of ETL tools:

  • Enterprise software ETL tools – these tools are created and maintained by commercial enterprises. They usually come with a unique interface that improves navigation and allows for a more robust structure, but also come with a heftier price tag and require special employee training to run. An example of this type of ETL tool would be Azure Data Factory.
  • Open-source ETL tools – open source tools allow anyone to access the source code of the tool and extend its capabilities. This can be a benefit when companies want a tool that can be easily customized, but open source ETL tools vary drastically in things like upkeep, documentation, and functionality, since they don’t have a company that supports them. An example of an open-source ETL tool would be Talend OpenStudio.
  • Cloud-based ETL tools – many cloud-service providers have started offering ETL tools that work with their infrastructure. These ETL tools are particularly efficient because cloud technology has the benefit of being available anywhere and very elastic, allowing computing resources to scale to meet of-the-moment data processing demands. An example of a cloud-based ETL tool is Informatica Cloud Data Integration.
  • Custom ETL tools – companies with the resources to do so can produce their own, custom ETL tools in general programming languages. The only potential roadblock with this approach is that it requires a multitude of internal resources and manpower, whereas the other tools are more readily available.

What ETL Tools Should You Be Using?

What ETL tools a company needs to quickly, easily, and successfully complete their data transition process depends on the needs of a company. Evaluation factors for a company to consider before choosing a tool or suite of tools include, but are not limited to:

  • Use case – how is your company going to use this tool?
  • Budget – how much can your company afford to spend on ETL tools so as to balance costs and ROI?
  • Capabilities – what are the capabilities that your company needs performed?
  • Multiple data sources – what tool can extract information from all of your company’s various sources?
  • Technical literacy – can your developers and end users understand the tool and use it effectively?

How Kenway Can Help

Kenway is a data management consulting company that offers expertise in the areas of data warehouse modernization, data governance framework, and data storage and migration.

Our firm has a steady history of providing companies of all sizes and industries with comprehensive data management and governance solutions. Reach out to us today about taking your first steps towards centralization of data through ETL or another data integration method.


Which is better ETL or ELT?

ETL and ELT both have their advantages and disadvantages, and neither is better than the other. ETL is typically preferred for projects that require significant data cleansing and manipulation. ELT is typically preferred when the target environment has the capacity to transform the data after it has been loaded.

What is the key difference between ETL and ELT?

The key difference between ETL vs. ELT is the order of the steps in the process. With ETL, data is transformed before it’s loaded into the ETL database. With ELT, the data is loaded into the target database, and then transformed. 

Is ELT replacing ETL?

While ELT is better suited to cloud-based environments and allows for faster access to data, there are still use cases for ETL. ETL is still preferred for moving from on-premise databases to the cloud.

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