What Is ETL?

Extract, transform, load is a process in data migration projects that involves extracting data from its original source, transforming it into a suitable format for the target database, and loading it into the final destination. ETL is vital for ensuring accurate and efficient data migration outcomes since it allows organizations to convert all of their existing data into more easily managed, analyzed, and manipulated formats.

In this guide to ETL, learn more about how it works, the impact it can have on business operations, and the top tools to consider using in your business.

SEE: Data Governance Frameworks: Definition, Importance, and Examples (TechRepublic)

How does ETL work?

Here’s how the three-step process works.

Step one: Extract

This involves gathering relevant data from various sources, whether homogeneous or heterogeneous. These data sources may use different formats, such as relational databases, XML, JSON, flat files, IMS, and VSAM, or any other format obtained from external sources by web crawling or screen scraping.

In many solutions, streaming these data sources directly to the destination database may be possible in some cases when intermediate data storage is unnecessary. Throughout this step, data professionals must evaluate all extracted data for accuracy and consistency with the other datasets.

Step two: Transform

Transformations are a set of rules or functions applied to extracted data to make it ready for loading into an end target. They can also be applied as cleansing mechanisms, ensuring only clean data is transferred to its final destination.

Transformations can be tricky and complex because they may require different systems to communicate with one another. This means compatibility issues could arise, for example, when considering character sets that may be available on one system but not another.

Multiple transformations may be necessary to meet business and technical needs for a particular data warehouse or server. Some example types include:

• Encoding free-form values: Mapping “Female” to “F”.
• Choosing to load only specific columns: Selecting only “Name” and “Address” from a row.
• Normalizing data: Joining first and last names into a single column called “Name”.
• Sorting data: Sorting customer IDs by ascending or descending order.
• Deriving new calculated values: Computing average products sold per customer.
• Pivoting and transposing data: Converting columns into rows.

Step three: Load

The last step is loading transformed information into its end target. Loading could involve an asset as simple as a single file or as complex as a data warehouse. Common destinations include on-premises data warehouses, cloud storage solutions, and cloud data warehouses.

This process can vary widely depending on the requirements of each organization and its migration projects.

SEE: What Is Data Quality? (TechRepublic)

Benefits of ETL

There are several advantages:

• Data consistency and quality: It ensures the data from various sources remains consistent after transformation. Cleansing, enrichment, and validation during transformation also improve quality.
• Scalability and performance: Large data volumes are handled efficiently, while the load on databases is reduced by offloading transformation processed from the target system.
• Security and compliance: Data can easily be masked, encrypted, and anonymized during transformation to comply with privacy laws and regulations.

SEE: Data Governance Checklist (TechRepublic Premium)

Drawbacks of ETL

But it also comes with a few disadvantages:

• Latency and batch processing: ETL processes typically use batch processing. This introduces latency and is not ideal for scenarios that require near-instant data updates.
• Complexity and maintenance overhead: The multiple steps often involve several systems, which introduces complexity. Also, ETL workflows must be updated regularly as data sources evolve or business needs change. This leads to an ongoing maintenance overhead.

SEE: How to Measure Data Quality (TechRepublic)

How ETL is being used

ETL is a critical process for data integration and analytics. Some common use cases include:

• Data warehousing: ETL pipelines are used to extract data from source systems such as databases, files, and APIs. It then transforms the data into a consistent format and loads it into a data warehouse.
• Business intelligence: It is used to populate data marts and warehouses used by BI tools.
• Data migration: It is frequently used during migrations when an organization needs to transition from one system to another.
• Data integration: Useful for the seamless integration of data from different sources.
• Data cleansing and enrichment: Pipelines are used to clean and standardize data. They enrich data by incorporating missing information.
• Batch processing: ETL jobs typically run at scheduled intervals and process large amounts of data, ensuring the data warehouse remains updated.
• Data governance and compliance: Data can be encrypted during the transformation process to comply with data laws.
• Real-time ETL: While traditional ETL is mostly done on scheduled intervals (batches), real-time ETL is used for scenarios that require instant updates, such as stock market updates.
• Cloud data pipelines: Tools can facilitate the movement of data between cloud platforms and on-premises storage.

SEE: Best Practices to Improve Data Quality (TechRepublic)

ETL vs ELT

ETL has already been explained.

With ELT, the letters stand for the same words, but raw data extracted from various sources is loaded directly into the target system, such as a data warehouse or lake, and transformation is the final step.

The choice between ETL or ELT comes down to the organization’s needs, data volume, complexity, infrastructure, and performance considerations.

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ETL tools to help your data migration

ETL tools can run in the cloud or on-premises and often come with an interface that creates a visual workflow when carrying out various processes.

Below are our top four picks for cloud-based, on-premises, hybrid, and open-source tools:

• AWS Glue.
• Azure Data Factory.
• IBM DataStage.
• Google Cloud Dataflow.

This article was originally published in January 2023. An update was made by the current author in March 2024. The latest update was by Antony Peyton in June 2025.