[GTM glossary](/glossary.txt) ETL

Data & Enrichment

# ETL (ETL)

Also known as: extract transform load

In short

ETL, short for extract, transform, load, is a process that copies data out of source systems, cleans and reshapes it, and loads it into a destination, usually a data warehouse. Each letter is a stage in the order the work happens.

For go-to-market teams, ETL typically brings CRM, marketing, product, and billing data into one place for reporting. A related pattern, ELT, loads raw data first and transforms it inside the warehouse, while reverse ETL moves data back out to operational tools.

## The three stages

* **Extract:** Pull data from sources such as the CRM, ad platforms, product database, and billing system.
* **Transform:** Clean, standardize, deduplicate, and join the data so fields line up. For example, convert dates and map currencies.
* **Load:** Write the result to the destination, such as a warehouse table used for dashboards.

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## ETL vs. ELT vs. reverse ETL

ETL transforms before loading. ELT loads first and transforms inside the warehouse, which is common with modern cloud warehouses. Reverse ETL goes the other direction, sending warehouse data back into the CRM or marketing tools.

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## Why RevOps cares

Reports that blend sources, such as marketing spend, pipeline, and revenue, need clean combined data. ETL makes that possible without manual spreadsheet exports.

Say a team wants cost per closed deal by channel. ETL pulls ad spend and CRM opportunities into one table, matches campaigns by a shared ID, and refreshes the table each morning.

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## Common mistakes

* **No data owner:** Without an owner, broken jobs go unnoticed.
* **Silent failures:** Add alerts when a job fails or loads zero rows.
* **Undocumented transforms:** Write down logic such as how a closed deal is defined.
* **Loading everything:** Move only the fields people actually use.

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## ETL FAQs

**What does ETL stand for?**

ETL stands for extract, transform, load. Data is pulled from source systems, cleaned and reshaped, then loaded into a destination such as a data warehouse where it can be used for reporting and analysis.

**What is ETL used for in sales and marketing?**

Teams use ETL to combine data from the CRM, ad platforms, product, and billing into one warehouse. That makes reports such as cost per closed deal or pipeline by channel possible without manual spreadsheet exports.

**What is the difference between ETL and ELT?**

In ETL, data is transformed before it is loaded. In ELT, raw data is loaded first and transformed inside the warehouse. ELT is common with modern cloud warehouses because they handle heavy transformation well.

**How does ETL differ from reverse ETL in a GTM data stack?**

ETL moves data from operational tools into a warehouse for analysis. Reverse ETL does the opposite: it sends modeled data from the warehouse back into tools like the CRM, so reps and marketers can act on it.

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## More related phrases

* [API](/glossary/api.txt)
* [B2B data enrichment](/glossary/b2b-data-enrichment.txt)
* [B2B data platform](/glossary/b2b-data-platform.txt)

## Similar phrases

* [Data pipeline](/glossary/data-pipeline.txt)  
A data pipeline is an automated sequence of steps that moves data from source systems to a destination, usually transforming and checking it along the way.
* [Reverse ETL](/glossary/reverse-etl.txt)  
Reverse ETL copies cleaned, modeled data from a data warehouse back into operational tools such as a CRM, ad platform, or sales engagement tool so teams can act on it.
* [Data warehouse](/glossary/data-warehouse.txt)  
A data warehouse is a central system that stores structured data from many sources, such as CRM and product usage, organized for reporting and analysis.
* [Data lake](/glossary/data-lake.txt)  
A data lake is a central store that holds large amounts of raw data in its original format, structured or not, so teams can analyze it later for many purposes.
* [Data normalization](/glossary/data-normalization.txt)  
Data normalization is the process of converting values into a consistent, standard format so records can be matched, compared, filtered, and reported on reliably.