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

ETL Benefits	Disadvantages of ETL
Consistency and quality of data. Scalability and performance. Security and compliance.	Latency and batch processing. Complexity and maintenance overhead.

ETL is a process in data migration projects that involves extracting data from its original source, transforming it into a format suitable for the destination database, and loading it into the final destination. It is vital to ensuring accurate and efficient data migration results, as it allows organizations to convert all their existing data into formats that are easier to manage, analyze and manipulate. The ETL process moves data from its source(s) to another system or database, where it can be used for analysis and decision-making purposes.

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

How does ETL work?

The three-step ETL process is a crucial piece of data migration projects. Here's how it works, broken down into each of its three main components.

Step one: 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 use different formats, such as relational databases, XML, JSON, flat files, IMS and VSAM, or any other format obtained from external sources through web spidering or screen scraping.

PREMIUM: Consider implementing a cloud data storage policy.

In many solutions, it is possible to stream these data sources directly to the destination database in some cases when intermediate data storage is not necessary. Throughout this step, data professionals must evaluate all extracted data for accuracy and consistency with other data sets.

Step two: transform

Once the data is extracted, the next step of the ETL process is transformation. Transformations are a set of rules or functions that are applied to extracted data to prepare it for loading to a final destination. Transformations can also be applied as data cleansing mechanisms, ensuring that only clean data is transferred to its final destination.

Transformations can be complicated and complex because they may require different systems to communicate with each other. This means that 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 the technical and business needs of a particular data warehouse or server. Some examples of transformation types include the following:

• Free-form value encoding: Mapping from “Woman” to “F”
• Choose to load only specific columns: Select only “Name” and “Address” from a row
• Normalizing data: Join first and last names in a single column called “Name”
• Sort data: Sorting customer IDs in ascending or descending order
• Deriving new calculated values: Calculate the average number of products sold per customer
• Pivot and transpose data: Convert columns to rows

Step three: upload

The last step of ETL is to load the transformed information into its final target. The load could involve an asset as simple as a single file or as complex as a data warehouse. Common destinations include local data stores; 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 widely depending on the requirements of each organization and its data migration projects.

ETL Benefits

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

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

Disadvantages of ETL

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

• Latency and Batching: 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: Multiple steps often involve multiple systems, which introduces complexity. Additionally, ETL workflows must be updated periodically as data sources evolve or business needs change. This leads to continuous maintenance overhead.

How to use ETL

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

• Data storage: ETL pipelines are used to extract data from source systems such as databases, files, and APIs, transform the data into a consistent format, and then load it into a 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 migrations when an organization needs to transition from one system to another.
• Data integration: ETL makes it possible to seamlessly integrate data from different sources.
• Data Cleansing 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 often run at scheduled intervals and process large amounts of data, ensuring that the data warehouse remains up to date.
• Data governance and compliance: ETL is a critical tool for enforcing data governance policies. Data can be encrypted during the transformation process to comply with data laws.
• ETL in real time: While traditional ETL is primarily performed in scheduled intervals (batches), real-time ETL is now used for scenarios that require instant updates, such as stock market updates.
• Cloud Data Pipelines: ETL tools are used in cloud environments to facilitate the movement of data between cloud platforms and local storage.

ETL vs. ELT

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

SEE: For more information, see our comparison of ETL and ELT.

Consider ETL tools to help you with your 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 a visual workflow by carrying out various extraction, transformation and loading processes.

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