What Is ETL? Extract, Transform and Load Explained

Key points

• ETL stands for extract, transform and load. The graphic defines it as a three-step process that consolidates data from multiple sources, prepares it and stores it in a centralised location.
• Transform is broken into four jobs on the graphic: cleaning, deduplication, conversion and enrichment.
• Data sources shown are websites, files (CSV, XML, JSON), databases (NoSQL, RDBMS) and applications (CRM, ERP).
• Four types of ETL are compared: traditional (batch), real-time, cloud-based and on-premise.
• The graphic names 17 tools and technologies and closes with a five-step plan for implementing ETL, from identifying objectives to scaling up.

The ETL infographic in text

This is everything printed on the graphic, from top to bottom, with the original wording kept.

ETL, how data moves from raw to ready

“In 2016, leveraging data to spot trends or discover opportunities was something only a third of businesses pursued.” A half-circle gauge beside this line reads 30%.

A banner reads “Today, the scene is entirely different.” Two bars follow: 40%, “Companies analyze between 37%-40% of their internal data”, and 97.2%, “Nearly 97.2% of these companies have embraced big data.”

“The focus has shifted to finding better ways to extract insights, whether the data originates internally or is gathered from external sources.” A banner then states that ETL “is the go-to solution for handling both internal and external data.”

What is ETL

“Extract, Transform, and Load (ETL) is a three-step process that enables organizations to consolidate data from multiple sources, prepare it, and store it in a centralized location.”

How ETL works

Step	Stage	What the graphic says
01	Extract	Fetch data from the sources.
02	Transform	Cleaning, Deduplication, Conversion, Enrichment.
03	Load	Data is loaded into database or data warehouse.

Data sources

• Website
• Files (CSV, XML, JSON)
• Database (NoSQL, RDBMS, etc.)
• Applications (CRM, ERP, etc.)

Types of ETL processes

No.	Type	Description on the graphic
01	Traditional ETL	The classic ETL approach where data is extracted, transformed, and loaded in batches.
02	Real-time ETL	Data is processed and loaded into the target system as it’s generated.
03	Cloud-based ETL	Leverages the scalability and flexibility of cloud platforms to manage data pipelines.
04	On-premise ETL	This approach offers greater control and security but can require significant resources for setup and maintenance.

Business impact

Figure	Label on the graphic
23 times	more likely to acquire new customers
19 times	more likely to be profitable
30%	improvement in inventory turnover
60%	reduced data management cost
40%	faster customer requests resolution time
30%	improvement in operational efficiency

ETL tools and technologies

The graphic shows 17 logos in three rows.

• Row 1: Apache Airflow, Informatica, Talend (shown with the tagline “A Qlik Company”), Oracle Data Integrator, Nannostomus, Apache Spark.
• Row 2: AWS Glue, AWS Lambda, AWS Step Functions, AWS Data Pipeline, AWS EventBridge.
• Row 3: Hadoop, Kafka, .NET, Confluent, IBM DataStage, Stitch.

How to implement ETL

1. Identify the objectives
2. Plan ETL strategy
3. Build a team
4. Pilot project
5. Scale up

Sources and maker panel

The footer lists three sources: an Intsurfing blog post on the extract, transform, load process, an Edge Delta article on what percentage of companies invest in big data, and a McKinsey article on how customer analytics boosts corporate performance. Beside them is the Intsurfing logo with the line “Big data company with a focus on building efficient data pipelines, ETL processes, and data collection flows.”

ETL compared with ELT

ELT (extract, load, transform) changes the order of the last two steps. Amazon Web Services describes ELT as an extension of ETL that loads data straight into the target system before processing it, so no separate staging area is needed. AWS says ELT has become more popular as cloud data warehouses gained the processing power to run transformations themselves.

In the Intsurfing article that carries this graphic, ETL is suggested where complex transformations are needed early, where the destination has limited processing power, and where compliance requires strict data quality checks before data lands. ELT is suggested for large data volumes and for work that needs flexible transformation later, such as data science and machine learning pipelines.

How the tools on the graphic are grouped

The graphic shows logos only. The Intsurfing article sorts several of them into categories:

• On-premise tools, installed on your own infrastructure: Informatica PowerCenter.
• Cloud-based tools: Talend Cloud and AWS Glue.
• Real-time tools for streaming data: Apache Kafka and Confluent.
• ELT-focused tools that transform data inside the destination: Nannostomus and Stitch.
• Hybrid tools covering on-premise and cloud: IBM DataStage.

The article also places traditional batch ETL as best for periodic reporting and historical analysis, and real-time ETL as suited to live dashboards, fraud detection and IoT applications.

Notes on this infographic

• Maker. The graphic was made by Intsurfing, a data engineering company, and appears in its own article on ETL. Nannostomus, one of the 17 tools shown, is Intsurfing’s own data collection product: nannostomus.com states that its API is provided through the Intsurfing API platform.
• 2016 figure. The graphic gives no source for “only a third of businesses” in 2016, and its gauge reads 30%, slightly under a third. The Intsurfing article puts the same claim as “just eight years ago”. We could not trace it to a study.
• 37% to 40% and 97.2%. These match Edge Delta, which says most businesses analyse 37% to 40% of their data and 97.2% of companies invest in big data solutions. Edge Delta speaks of investing in big data solutions; the graphic says “embraced big data”.
• 23 times and 19 times. These come from McKinsey, whose 2013 DataMatics survey interviewed 400 top managers. The figures compare intensive users of customer analytics with non-intensive users: 23 times more likely to clearly outperform competitors in new customer acquisition, and almost 19 times more likely to achieve above-average profitability. The survey measured customer analytics in general, not ETL pipelines.
• 30%, 60%, 40% and 30%. The Intsurfing article presents these as “up to” figures, reported by its clients or by businesses using ETL-driven analytics. No independent study is cited for them, and the graphic leaves out the words “up to”.
• AWS Data Pipeline. According to AWS documentation, AWS Data Pipeline is no longer available to new customers and is in maintenance mode. Existing customers can still use it.
• Source links. The first source printed on the graphic (intsurfing.com/blog/data-engineering/extract-transform-load-process/) now returns a page not found error. The graphic itself is published at the Intsurfing article linked below.

Questions about ETL

What does ETL stand for in the context of data analytics?

ETL stands for extract, transform and load. AWS defines it as the process of combining data from multiple sources into a central repository called a data warehouse, using business rules to clean and organise raw data so it is ready for storage, analytics and machine learning.

Is AWS Glue ETL or ELT?

Both. The AWS Glue documentation describes it as a serverless data integration service that supports ETL, ELT and streaming workloads in one service, with visual tools for building ETL pipelines.

Is Informatica an ETL tool?

Yes. Informatica describes its PowerCenter product as used for “mission-critical ETL” and now offers cloud data integration as its successor. The Intsurfing article lists Informatica PowerCenter as an example of an on-premise ETL tool.

Sources

1. Intsurfing (Iryna Zub), ETL Essentials, A Guide to Efficient Data Management (original article carrying this infographic)
2. Intsurfing home page
3. Edge Delta, What Percentage of Companies Invest in Big Data (25 March 2024)
4. McKinsey and Company, Five facts, How customer analytics boosts corporate performance (1 July 2014; read via the Internet Archive copy)
5. Amazon Web Services, What is ETL (Extract Transform Load)
6. AWS documentation, What is AWS Glue
7. AWS documentation, What is AWS Data Pipeline
8. Informatica, PowerCenter to Cloud Modernization
9. Nannostomus home page

Last reviewed 29 October 2026