Quick answer? ETL stands for "Extract, Transform, and Load."

In the world of data warehousing, if you need to bring data from multiple different data sources into one, centralized database, you must first:

  • EXTRACT data from its original source
  • TRANSFORM data by deduplicating it, combining it, and ensuring quality, to then
  • LOAD data into the target database

ETL tools enable data integration strategies by allowing companies to gather data from multiple data sources and consolidate it into a single, centralized location. ETL tools also make it possible for different types of data to work together.

A typical ETL process collects and refines different types of data, then delivers the data to a data lake or data warehouse such as Redshift, Azure, or BigQuery.

ETL tools also makes it possible to migrate data between a variety of sources, destinations, and analysis tools. As a result, the ETL process plays a critical role in producing business intelligence and executing broader data management strategies. We are also seeing the process of Reverse ETL become more common, where cleaned and transformed data is sent from the data warehouse back into the business application.

How ETL works

The ETL process is comprised of 3 steps that enable data integration from source to destination: data extraction, data transformation, and data loading.

Step 1: Extraction

Most businesses manage data from a variety of data sources and use a number of data analysis tools to produce business intelligence. To execute such a complex data strategy, the data must be able to travel freely between systems and apps.

Before data can be moved to a new destination, it must first be extracted from its source — such as a data warehouse or data lake. In this first step of the ETL process, structured and unstructured data is imported and consolidated into a single repository. Volumes of data can be extracted from a wide range of data sources, including:

  • Existing databases and legacy systems
  • Cloud, hybrid, and on-premises environments
  • Sales and marketing applications
  • Mobile devices and apps
  • CRM systems
  • Data storage platforms
  • Data warehouses
  • Analytics tools

Although it can be done manually with a team of data engineers, hand-coded data extraction can be time-intensive and prone to errors. ETL tools automate the extraction process and create a more efficient and reliable workflow.

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Step 2: Transformation

During this phase of the ETL process, rules and regulations can be applied that ensure data quality and accessibility. You can also apply rules to help your company meet reporting requirements. The process of data transformation is comprised of several sub-processes:

  • Cleansing — inconsistencies and missing values in the data are resolved.
  • Standardization — formatting rules are applied to the dataset.
  • Deduplication — redundant data is excluded or discarded.
  • Verification — unusable data is removed and anomalies are flagged.
  • Sorting — data is organized according to type.
  • Other tasks — any additional/optional rules can be applied to improve data quality.

Transformation is generally considered to be the most important part of the ETL process. Data transformation improves data integrity — removing duplicates and ensuring that raw data arrives at its new destination fully compatible and ready to use.

Step 3: Loading

The final step in the ETL process is to load the newly transformed data into a new destination (data lake or data warehouse.) Data can be loaded all at once (full load) or at scheduled intervals (incremental load).

Full loading — In an ETL full loading scenario, everything that comes from the transformation assembly line goes into new, unique records in the data warehouse or data repository. Though there may be times this is useful for research purposes, full loading produces datasets that grow exponentially and can quickly become difficult to maintain.

Incremental loading — A less comprehensive but more manageable approach is incremental loading. Incremental loading compares incoming data with what’s already on hand, and only produces additional records if new and unique information is found. This architecture allows smaller, less expensive data warehouses to maintain and manage business intelligence.

ETL use case: business intelligence

Data strategies are more complex than they’ve ever been; SaaS gives companies access to data from more data sources than ever before. ETL tools make it possible to transform vast quantities of data into actionable business intelligence.

Consider the amount of raw data available to a manufacturer. In addition to the data generated by sensors in the facility and the machines on an assembly line, the company also collects marketing, sales, logistics, and financial data (often using a SaaS tool).

All of that data must be extracted, transformed, and loaded into a new destination for analysis. ETL enables data management, business intelligence, data analytics, and machine learning capabilities by:

Delivering a single point-of-view

Managing multiple data sets in a world of enterprise data demands time and coordination, and can result in inefficiencies and delays. ETL combines databases and various forms of data into a single, unified view. This makes it easier to aggregate, analyze, visualize, and make sense of large datasets.

Providing historical context

ETL allows the combination of legacy enterprise data with data collected from new platforms and applications. This produces a long-term view of data so that older datasets can be viewed alongside more recent information.

Improving efficiency and productivity

ETL Software automates the process of hand-coded data migration and ingestion, making it self-service. As a result, developers and their teams can spend more time on innovation and less time managing the painstaking task of writing code to move and format data.

Finding an ETL solution

ETL can be accomplished in one of two ways. In some cases, businesses may task developers with building their own ETL. However, this process can be time-intensive, prone to delays, and expensive.

Most companies today rely on an ETL tool as part of their data integration process. ETL tools are known for their speed, reliability, and cost-effectiveness, as well as their compatibility with broader data management strategies. ETL tools also incorporate a broad range of data quality and data governance features.

When choosing which ETL tool to use, you’ll want to consider the number and variety of connectors you’ll need as well as its portability and ease of use. You’ll also need to determine if an open-source tool is right for your business since these typically provide more flexibility and help users avoid vendor lock-in.

ELT — the next generation of ETL

ELT is a modern take on the older process of extract, transform, and load in which transformations take place before the data is loaded. Over time, running transformations before the load phase is found to result in a more complex data replication process. While the purpose of ETL is the same as ELT, the method is evolved for better processing.


Traditional ETL software extracts and transforms data from different sources before loading it into a data warehouse or data lake. With the introduction of the cloud data warehouse, there was no longer the need for data cleanup on dedicated ETL hardware before loading into your data warehouse or data lake. The cloud enables a push-down ELT architecture with two steps changed from the ETL pipeline.

  • EXTRACT Extract the data from multiple data sources and connectors
  • LOAD Load it into the cloud data warehouse
  • TRANSFORM Transform it using the power and scalability of the target cloud platform

If you are still on premises and your data isn't coming from several different sources, ETL tools still fit your data analytics needs. But as more businesses move to a cloud data architecture (or hybrid), ELT processes are more adaptable and scalable to evolving needs of cloud-based businesses.

ETL process vs ELT processes

ETL tools require processing engines for running transformations prior to loading data into a destination. On the other hand, with ELT, businesses use the processing engines in the destinations to efficiently transform data within the target system itself. This removal of an intermediate step streamlines the data loading process.

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