ETL Definition


Everyone working with data warehouse and data integration is familiar with the abbreviation ETL. It stands for “Extract Transform Load”, meaning it encompasses three essential areas of data integration and data processing.

In essence, ETL is a three-step process that organizations rely on to catalyze and combine raw, unprocessed data from multiple sources into a single data warehouse, data lake, data store, or relational database.

ETL is crucial in data engineering as it allows for the formation of a data repository. Such a repository has been unified and can be used for analytical purposes and further processing. Data migrations and cloud data interactions are the most common cases of ETL processing.
To clear the air around ETL and all it encompasses, stay tuned as we reveal the ins and outs of the process. Moreover, we’ll also note the challenges that come with using the ETL process.

How Does ETL Work?

The ETL process comprises three steps – extraction, transformation, and data loading. Now, even though it might sound simple enough, the process is far more complex than it sounds.

Data extraction, transformation, and loading are one thing, but the transportation of data, the overlap occurring in each stage of processing, and the novice technologies used are changing the flow of the process. Let’s have a look at each stage of ETL in more detail.

Step 1: Extraction

The E in ETL stands for Extraction, which is the first step of the process. In the initial phase, extraction entails collecting data from one or more data sources. After data is pulled, it is stored in temporary storage awaiting the two consecutive steps of the process.

While the extraction process is in effect, there are different validation rules at hand. Namely, this ensures the data’s compatibility with the designated destination. Data sequences that fail to comply with the validation parameters are denied and will not complete the cycle.

While extracting data, data management teams can do so from a multitude of data sources, both structured and unstructured, like:
Flat files
SQL or NoSQL servers
Web pages
CRM and ERP systems

Step 2: Transformation

The second phase of data processing encompasses data transformation, also known as data staging. In this step of the process, the processing of raw data begins.

The reason for data transformation is to render the values and structure of data compatible with its designated use. The ultimate goal of data transformation is to convert all data into a suitable form, a unified pattern, before it reaches the last step of the process.

Most common data transformation practices include data masking, aggregators, lookup, rank, router, joiner, expression, union, XML, Normalizer, H2R, R2H, and web service. All aspects aid with the normalization, standardization, and filtering of data.

Data transformation is also a major benefit regarding downstream activities like analytics and business functions, rendering data suitable for consumption. The transformation step includes the following:

Calculating, translating, or summarizing raw data (including but not limited to altering row and column headers to ensure consistency, converting currencies or other metrics, editing strings of texts, and more);

Filtering, de-duplicating, validating, cleansing, and authenticating data;

Performing audits to ensure the quality and compliance of data;

Encrypting, removing, and safekeeping industry-governed data;

Formatting data into charts and tables or merging tables to follow the pattern of the targeted data warehouse.

Step 3: Loading

The final step of data processing ends with the L in ETL, which stands for loading the transformed data, and it wraps up the ETL process. Namely, the transformed, aka staged data, is transported from the staging area to the target data warehouse.

This process entails inceptive data loading, followed by sequential loading of cumulative data changes. Also, although it isn’t frequently practiced, it’s not uncommon for the loading phase to include wholesome data refreshes to erase and replace data in the warehouse.

Most businesses that utilize ETL do so by automating the process, which is also well-defined and perpetual. In most cases, a traditional ETL process occurs outside of working hours when the source systems and data warehouse traffic is brought to a minimum.

The load phase transports the transformed data to a durable and stable designated system. The altered data can be stored in a target database, a data warehouse, a data hub, or a data lake, both in-house or in the cloud.

Once the phase is completed, and all data is moved, the ELT cycle is deemed complete. Numerous businesses tend to use his process frequently to ensure their data warehouse stays up to date.

Why Is ETL Important?

The most significant feature of ETL is that it eases business decision-making by using clear, relevant data. By turning raw data into workable information, the process of ETL turns data into business intelligence that companies can use to their advantage and support their decision-making. By incorporating ELT tools, companies can come to business decisions supported by real-time filtered and formatted data to deliver the right information.

It also allows data scientists to access and analyse data substantially easier. Besides offering a time-efficient method for data processing, ETL is also compatible with businesses of all sizes. Moreover, the process will help managers devise reports and scale the metrics that revolve around the business strategy of the organization. It will become particularly advantageous for companies as they’ll get the upper hand over competitors.

How Is ETL Commonly Used?

Since it helps companies develop a well-rounded business strategy, ETL is most commonly used for cloud mitigation, machine learning, and AI, integrating marketing and sales data, as well as data warehousing.

ETL helps businesses use data as BI (business intelligence) and reach the most beneficial business decisions based on important data parameters.

Challenges With ETL

As abundant in perks for businesses as ETL is, it comes with its own set of challenges. Companies are mostly struggling with tending to massive amounts of data, and ETL tools are known to underperform when processing huge amounts of data, which happens more often than presumed.

In cases of extremely large loads of data, data scientists might be faced with substantial data loss, corrupted data, and even irrelevant data that won’t benefit their end goal. All of the mentioned issues might be due to some processes during the transformation phase not performing properly.

Moreover, ETL might lead to the creation of bottlenecks due to the lack of available memory and/or CPU restrictions. Aside from insufficient memory and data loss, data scientists can also be faced with disparate data.

It isn’t uncommon for the source database and the destination system to be out of sync. In other words, the coded mapping might not be the same for each, leading to serious data-loading mishaps. In such a scenario, data scientists and data managers are advised to employ a host of various data transformations, which ultimately annuls the meaning and intention of ETL.


ELT, ETL…no, it isn’t a typo but two different data processing methods. The most notable difference between the two is the different modus operandi. In other words, the work order is different in both methods.

ETL stands for Extraction, Transformation, Load, whereas ELT is short for Extract, Load, Transform. An ELT method will export or copy data from the source locations, but it won’t load it into a staging area to await transformation. Instead, raw data will be loaded directly into the target data store and only then become transformed as per request.

Even though both data processing methods revolve around data extraction, loading, and transformation, they differ in their ability to handle massive data loads. Namely, ELT is best suitable for large quantities of unstructured data mainly because the loading phase can occur while in the source.

Regarding who can benefit from an ELT data processing method, the ELT tools are most suitable for big data management because they won’t require substantial data extraction and storage planning beforehand.

ETL Tools

There are different kinds of ETL tools that serve different purposes. Even though such tools have been present on the market for more than 30 years, the advancement of technology has rendered different types of ETL tools to develop.

You can find both genuine ETL retailers as well as see household software names offering ETL tools. IBM, Microsoft, and Oracle are some of the industry giants offering ETL tools as part of their repertoire.

Enterprise Software ETL (IBM InfoSphere DataStage, Informatica PowerCenter, Oracle Data Integrator, Microsoft SQL Server Integration Services, Ab Initio, SAP Data Services, SAS Data Manager)

Open-Source ETL (Hadoop, Pentaho Data Integration, Talend Open Studio)

Custom ETL (SQL, Python, Java, Spark & Hadoop)

ETL Cloud Services (Google Cloud Dataflow, AWS Data Pipeline, AWS EMR, Stitch, Azure Data Factory, AWS Glue, Segment)

The Verdict: Can ETL Boost Your Business?

If your business success relies on data, don’t think twice about using ETL tools. From extracting to transforming and loading data, ETL tools will convert raw data into business intelligence that’ll serve the purpose of your business in the best way possible.

Outline the end goal of your business and see how ETL tools can help you stay on top of today’s data-driven world.



More from Data Management & ELT Glossary

Key terms to help you learn more about data management, ELT, and all-things Rivery