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What is ETL? (Extract, Convert, Load): The Ultimate Guide

Benefits of ETL Disadvantages of ETL
  • Data consistency and quality.
  • Scalability and performance.
  • Security and compliance.
  • Delay and batch processing.
  • Complexity and maintenance overhead.

ETL is a process in a data migration project that involves extracting data from the original source, converting it into a format suitable for the target database, and loading it to the final destination. It is critical to ensuring accurate and efficient data migration results, as it allows organizations to convert all existing data into a format that is easier to manage, analyze, and operate on. The ETL process moves data from its source to another system or database for analysis and decision-making purposes.

In this brief guide to ETL, learn more about how it works, its impact on business operations, and the top ETL tools to consider for your business.

How does ETL work?

The three-step ETL process is a critical part of a data migration project. Here’s how it works, broken down into three main components.

Step 1: Extract

The extraction step is the first part of ETL. It involves collecting relevant data from various sources, whether homogeneous or heterogeneous. These data sources can be in different formats such as relational databases, XML, JSON, flat files, IMS and VSAM, or any other format obtained from external sources through web crawling or screen scraping.

Advanced: Consider implementing a cloud data storage strategy.

In many solutions, these data sources can be streamed directly to the target database without the need for intermediate data storage. Throughout this step, data professionals must evaluate all extracted data for accuracy and consistency with other data sets.

Step Two: Transformation

After extracting the data, the next step in the ETL process is transformation. A transformation is a set of rules or functions applied to extracted data to make it ready for loading into the final destination. Transformation can also be used as a data cleaning mechanism, ensuring that only clean data is transferred to its final destination.

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

Multiple transformations may be required to meet the business and technical needs of a particular data warehouse or server. Some examples of conversion types include:

  • Encode free-form values: Map “Female” to “F”
  • Choose to load only specific columns: Select only “Name” and “Address” in one row
  • Standardized data: Concatenate first and last name into a single column called “Name”
  • Data sorting: Sort customer IDs in ascending or descending order
  • Export new calculated values: Calculate the average products sold per customer
  • Rotate and transpose data: Convert columns to rows

Step 3: Load

The final step of ETL is loading the transformed information into its final destination. Loads may involve assets as simple as a single file or as complex as a data warehouse. Common destinations include on-premises data warehouses; cloud storage solutions such as Amazon S3, Google Cloud, and Azure Data Lake; and cloud data warehouses such as Snowflake, Amazon Redshift, Google BigQuery, and Microsoft Azure Synapse Analytics.

Premium: Check out this cloud data warehouse guide and checklist.

This process can vary greatly depending on the requirements of each organization and its data migration project.

Benefits of ETL

ETL offers several benefits to data management professionals. They include:

  • Data consistency and quality: ETL ensures that data from different sources remains consistent after transformation. Purification, enrichment, and validation during transformation can also improve quality.
  • Scalability and performance: Process large amounts of data efficiently while reducing load on the database by offloading transformations processed by the target system.
  • Security and Compliance: Through ETL, data can be easily desensitized, encrypted, and anonymized during the conversion process to comply with privacy laws and regulations.

Disadvantages of ETL

Although ETL is a powerful and useful data migration process, it also has some disadvantages, namely:

  • Delay and batch processing: ETL processes typically use batch processing. This introduces latency, which is not ideal for scenarios that require near-instantaneous data updates.
  • Complexity and maintenance overhead: Multiple steps often involve multiple systems, which creates complexity. Additionally, ETL workflows must be regularly updated as data sources evolve or business needs change. This results in ongoing maintenance overhead.

How to use ETL

ETL is a key process for data integration and analysis. Some common use cases include:

  • Data warehousing: ETL pipelines are used to extract data from source systems such as databases, files, and APIs, convert the data into a consistent format, and then load it into the data warehouse.
  • Business Intelligence: ETL is used to populate data marts and data warehouses used by BI tools.
  • data migration: ETL is often used during data migration when an organization needs to transition from one system to another.
  • Data Integration: ETL enables seamless integration of data from disparate sources.
  • Data cleaning and enrichment: ETL pipelines are also used to clean and standardize data. They also enrich the data by incorporating missing information.
  • Batch processing: ETL jobs typically run at scheduled intervals and process large amounts of data to ensure that the data warehouse remains updated.
  • Data governance and compliance: ETL is a critical tool for executing your data governance strategy. Data can be encrypted during conversion to comply with data laws.
  • Real-time ETL: While traditional ETL is mostly done at scheduled intervals (batch), real-time ETL is now used in scenarios that require immediate updates, such as stock market updates.
  • Cloud data pipeline: ETL tools are used in cloud environments to facilitate data movement between cloud platforms and local storage.


It is important to distinguish between ETL and ELT. In ELT (Extract, Load, Transform), raw data extracted from various sources is loaded directly into the target system, such as a data warehouse or data lake, with transformation being the final step. The choice between ETL or ELT depends on the organization’s needs, data volume, complexity, infrastructure, performance considerations and any required workflows.

See: For more information, check out our comparison of ETL and ELT.

Consider ETL tools to help you with data migration

ETL tools are used to migrate data from one system to another, whether it is a database management system, a data warehouse or even an external storage system. These tools can run in the cloud or on-premises and often come with an interface that creates visual workflows while performing various extraction, transformation, and loading processes.

Here are our top five picks for cloud-based, on-premises and hybrid, and open source ETL tools:

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