ETL (Extract Transform Load): What You Need to Know

What is ETL?

ETL stands for extract, transform, and load.

ETL is a three-step data integration process used to synthesize raw data from a data source to a data warehouse, data lake, or relational database. Data migrations and cloud data integrations are common use cases for ETL.

ETL process flow diagram. Step 1: Extract. Step 2: Transform. Step 3: Load | Informatica

With cloud ETL, you can extract data from multiple sources, consolidate and transform that data, then load it into a centralized location where it can be accessed on demand. ETL is often used to make data readily available for analysts, engineers, and decision makers across a variety of use cases within an organization. (What are the differences between ETL and ELT?)

What does ETL do?

ETL moves data from a source to a destination (e.g., data warehouse) in three distinct steps. Here’s a quick summary:


Extraction is the first phase of ETL. Data is collected from one or more data sources and held in temporary storage where the next two steps of ETL will be executed.

During extraction, validation rules are applied to test whether data conforms to its destination’s requirements. Data that fails validation is rejected and does not continue to the next steps of ETL.


In the transformation phase, data is processed to make  its  values and structure  conform  consistently  with  its  intended  use case.  The goal of transformation is to make all data fit within a uniform schema before  it  moves on to the last step of ETL. 

 Typical transformations include formatting  dates, resorting rows or columns of data, joining data from two values into one, or, conversely, splitting data from one value into two.


Finally, the load phase moves the transformed data into a permanent, target database – on-premises or in the cloud. Once all data has been loaded, the ETL process is complete.

Many organizations regularly perform ETL to keep their data warehouse updated with the latest data.

Benefits of ETL

ETL tools work in concert with data integration tools we support many data management use cases – including data quality, data governance , virtualization, and metadata. Here are the top ETL benefits:

Get deep historical context for your business

When used with an enterprise data warehouse (data at rest), ETL provides historical context for your business by combining legacy data with data collected from new platforms and applications. 

Simplify cloud data migration

ETL helps you transfer your data to a cloud data lake or cloud data warehouse to increase data accessibility, application scalability, and security. Businesses rely on cloud integration to improve operations now more than ever.

Deliver a single, consolidated view of your business

Ingest and synchronize data from sources such as on-premises databases or data warehouses, SaaS applications, IoT devices, and streaming applications to a cloud data lake to establish one view of your business.

Enable business intelligence from any data at any latency

Businesses today need to analyze a range of data types – including structured, semi-structured, and unstructured – from multiple sources, such as batch, real-time, and streaming.

ETL tools make it easier to derive actionable insights from your data, so you can identify new business opportunities and guide improved decision-making. 

Deliver clean, trustworthy data for decision-making

Use ETL tools to transform data while maintaining lineage and traceability throughout the data lifecycle. This means all data practitioners – from data scientists to data analysts to line-of-business users – will have access to reliable data, no matter their data needs.

By automating critical data practices, ETL tools ensure the data you receive for analysis meets the quality standard required to deliver trusted insights for decision-making. ETL can be paired with additional data quality tools to guarantee data outputs meet your unique specifications.

Automate data pipelines

ETL tools enable time-saving automation for onerous and recurring data engineering tasks. Gain improved data management effectiveness and accelerate data delivery. Automatically ingest, process, integrate, enrich, prepare, map, define, and catalog data.

Replicate your database

ETL is used to replicate data from various source databases, such as MySQL, PostgreSQL, Oracle, etc. to a cloud data warehouse.

Operationalize AI and machine learning (ML) models

Data science workloads are made more robust, efficient, and easy to maintain. With cloud ETL tools you can efficiently handle the large data volumes required by data pipelines used in machine learning, DataOps, and MLOps .

Greater business agility via ETL for data processing

Teams will move more quickly as ETL reduces the effort needed to gather, prepare, and consolidate data. ETL automation improves productivity because it  allows  data professionals to get the data they need, where they need it;  without requiring technical skills to write code or scripts – saving valuable time and resources.

What are the different types of ETL data pipelines?

Data pipelines are categorized based on their use case. The most common forms of data pipelines employ either batch processing or real-time processing.

1. Batch processing pipelines

Batch processing is used for traditional analytics and business intelligence use cases where data is periodically collected, transformed, and moved to a cloud data warehouse.

Users can quickly deploy high-volume data from siloed sources into a cloud data lake or data warehouse and schedule jobs for processing data with minimal human intervention. With ETL in batch processing, data is collected and stored during an event known as a “batch window”, to more efficiently manage large amounts of data and repetitive tasks.

2. Real-time processing pipelines

Real-time data pipelines enable users to ingest structured and unstructured data from a range of streaming sources, such as IoT, connected devices, social media feeds, sensor data and mobile applications. A high-throughput messaging system ensures the data is captured accurately.

Data transformation is performed using a real-time processing engine (e.g., Spark streaming) to drive application features like real-time analytics, GPS location tracking, fraud detection, predictive maintenance, targeted marketing campaigns, or proactive customer care.

Informatica’s cloud ETL for data integration

As organizations proceed along the data-driven digital transformation journey, they are centralizing their data and analytics in cloud data warehouses and data lakes to drive advanced analytics and data science use cases. A transformation with such high impact potential requires an enterprise-scale, cloud-native data integration solution to rapidly develop & operationalize end-to-end data pipelines, and modernize legacy applications for AI.

Informatica’s industry leading solutions, offer the most comprehensive, codeless, AI-powered cloud-native data integration. Create your data pipelines across a multi-cloud environment consisting of AWS, Azure, GCP, Snowflake, Databricks, etc. Ingest, enrich, transform, prepare, scale and share any data at any volume, velocity and latency for your data integration or data science initiatives.

ETL in industry

ETL is an essential component in data integration projects across a variety of industries. Organizations may use ETL to ultimately increase operational efficiencies, improve customer loyalty, deliver omnichannel experiences, and find new revenue streams or business models.


Healthcare organizations use ETL in their holistic approach to data management. By synthesizing disparate data across their organization, healthcare businesses are accelerating clinical & business processes while improving member, patient, and provider experiences.

Public sector

Public sector organizations use ETL to surface the insights they need to maximize their efforts working under strict budgets. Tight budgets mean more efficiency is vital to providing services with limited available resources. Data integration makes it possible for governmental departments to make the best use of both data and funding.


Manufacturing leaders transform their data to optimize operational efficiency, ensure supply chain transparency, resiliency, and responsiveness, and improve omnichannel experiences while ensuring regulatory compliance.

Financial services

Financial institutions use ETL in their data integrations to access data that is transparent, holistic, and protected to grow revenue, deliver personalized customer experiences, detect, and prevent fraudulent activity, realize fast value from mergers and acquisitions while complying with new and existing regulations. They need to understand who their customers are and how to deliver services that fit their specific needs.

Get started with ETL

Informatica tools are easy to integrate and simple to use, whether you need single-cloud, multi-cloud, or on-premises data integration. Informatica’s Cloud Data Integration trial is free. See for yourself how broad out-of-the-box connectivity, codeless, prebuilt advanced transformations and orchestrations can help accelerate your data pipelines.