ETL stands for Extract, Transform, Load —the process that takes data from its source systems, cleans and shapes it, and deposits it in a destination where the business can analyze it with confidence. It is one of the cornerstones of any serious data strategy and the foundation on which reports, dashboards, and analytics rely.
What problem does ETL solve?
In most organizations data lives scattered: one system for sales, another for finance, spreadsheets in operations, a separate marketing platform. Each one stores information with its own format, its own rules, and its own quality.
When the time comes to answer a business question —how much we sold by region, which customers are at risk, how margin evolved— that fragmentation becomes an obstacle. The numbers do not match because each source defines things differently.
ETL solves this by gathering data from all those sources into a single place, with a consistent and error-free format, so that whoever analyzes it always works on a reliable version of the truth.
The three stages of ETL
The process is divided into three steps, which is where its name comes from:
- Extract: data is pulled from its source systems: transactional databases, APIs, files, SaaS platforms. Extraction can be full or incremental, capturing only what changed since the last run.
- Transform: the data is cleaned and shaped. Here errors are fixed, duplicates removed, formats standardized (dates, currencies, units), tables combined, and the business rules that give the information meaning are applied.
- Load: the prepared data is deposited into the final destination —usually a data warehouse or a data lake— where it becomes available for reporting and analytics.
ETL versus ELT
For years the order was always the same: transform and then load. The cloud changed that logic and gave rise to an alternative pattern, ELT (Extract, Load, Transform).
| Aspect | ETL | ELT |
|---|---|---|
| Order | Transform before loading | Load raw and transform in the destination |
| Where it transforms | In an intermediate engine | Inside the data warehouse or data lake |
| Best for | Structured data with clear rules | Large volumes and varied formats |
| Typical context | Traditional systems | Modern cloud architectures |
Neither is better in absolute terms: the choice depends on data volume, the type of sources, and the compute power of the destination. In modern cloud architectures, with elastic data warehouses and data lakes, ELT is gaining ground because it leverages the destination’s own power to transform at scale.
How ETL is built on AWS
AWS offers a set of managed services that cover the entire data lifecycle without having to administer servers:
- AWS Glue: AWS’s serverless ETL service. It discovers and catalogs data, prepares it, and moves it between sources and destinations, scaling automatically with the workload.
- Amazon S3: the storage that usually acts as a data lake, where data lands raw before and after being transformed.
- Amazon Redshift: the data warehouse for high-performance analytics on structured data.
- Amazon Athena: SQL queries directly on the data in S3, without moving anything.
The big advantage of the managed approach is that the team focuses on business rules and data quality, rather than operating and sizing infrastructure.
Why a good ETL matters for the business
- A single source of truth: every report starts from the same trusted data, which reduces arguments about which number is correct.
- Faster decisions: with data already unified and clean, analytics delivers answers in hours, not weeks.
- Foundation for data analytics and AI: predictive models and AI agents are only as good as the data that feeds them; a solid ETL is the prerequisite.
- Scalability: a well-designed pipeline grows with the business without being rewritten every time a new source appears.
ETL as part of a data strategy
An ETL process is rarely an end in itself: it is the first piece of a data platform that enables trustworthy reporting, advanced analytics, and artificial intelligence. At Caleidos we design and operate these pipelines as part of our data engineering practice on AWS, with production cases documented in our case studies.
Frequently asked questions
What does ETL mean in simple terms? It is the process of extracting data from its sources, transforming it to clean and format it, and loading it into a destination where the business can analyze it.
What is the difference between ETL and ELT? In ETL you transform before loading; in ELT you load raw and transform inside the destination itself, which is common in the cloud.
How do you do ETL on AWS? With AWS Glue as a serverless ETL service, supported by Amazon S3 as a data lake, Amazon Redshift as a data warehouse, and Amazon Athena for queries.
Want to organize your data so the business decides better?
Let’s talk about your current data platform and we will give you a concrete recommendation on how to build your pipelines on AWS.
