Data isn't like a box of old National Geographic magazines you put in the attic and forget about until you move (and inexplicably decide to bring them with you). It has to be trimmed, transformed, and transported on at least a semi-regular basis if you want to maintain its quality and relevance for analysis.
Say your business is updating its storage solution from a legacy database to a cloud warehouse. To get all your data from point A to point B—and clean it up in the process—you'll want to use ETL.
In this guide, I'll explain what ETL is and how it works, and then show you common use cases and tools that automate it.
Table of contents:
ETL meaning
ETL—or extract, transform, load—is a data management process that brings together data from multiple sources and houses it in a data warehouse or other storage solution. If you're new to this whole framework, it'll help to see each stage of ETL broken down in detail.
Extract
The first step in the ETL process involves pulling (or extracting) raw data from various sources. These could be individual databases, JSON or XML files, Excel spreadsheets, emails, SQL servers, websites, or APIs. Extracted data can be either structured or unstructured, depending on how and where it's being stored.
Any data copied or exported at this stage will be in its original form. To use an admittedly strained mining metaphor, you can think of data extraction as collecting raw materials from a number of different deposits.
Transform
In the second stage of ETL, raw extracted data gets processed, cleaned, formatted, filtered, enriched—transformed, in other words. At a base level, transformation makes data from disparate sources usable and consistent, so it isn't a mess when it's brought together in a central data warehouse. It's kind of like refining raw materials, to keep the mining analogy going.
This could mean removing duplicates, converting file types, correcting errors, encrypting, or even generating new data from existing values. How your data changes will depend on its initial state and the parameters of your target warehouse or system.
Load
Once the data's been extracted and transformed, it can be loaded into a target data warehouse, where it can be stored and analyzed. Your data's endpoint may be a relational database (like MySQL), a non-relational database (like MongoDB), a cloud data warehouse (like Google Cloud's BigQuery), or another storage solution.
This step can be seriously time-consuming, depending on your upload volume. If you're migrating to a data warehouse for the first time, you'll likely need to do a full load during off-hours or scheduled maintenance downtime as part of your initial setup process. Otherwise, you can perform an incremental or batch load whenever new or updated data is added to your data sources.
How does ETL work?
Like the lopsided cups I churn out in pottery class, no two ETL pipelines will be exactly alike. Yes, it's a fairly standardized process, but how it actually looks in practice depends on your data sources, volume, target warehouse, and security compliance regulations, among other factors.
Let's say you run a business that sells art prints across multiple eCommerce platforms. ETL can be used to pull sales data from all your storefronts (e.g., TikTok Shop, Etsy, and Shopify), clean it up, and then move it to a central data warehouse. Here's what each step might look like:
Extract: First, you'll collect raw sales data from your storefronts via APIs or CSV files. This could include customer details, product SKUs, order amounts, and purchase timestamps.
Transform: Different eCommerce platforms might not format sales data the same way, so you'll need to standardize and clean it up by doing things like removing duplicates, fixing missing values, and categorizing based on customer region.
Load: Last, you'll upload the transformed sales data into a data warehouse like Google Cloud's BigQuery or Snowflake. From here, you can analyze it as a whole instead of navigating separate data sources.
Note that all three of these steps can be automated with Python scripts, ETL tools, or Zapier's no-code data management solutions. This is pretty much a necessity if you're working with complex datasets.
ETL use cases
You can see how ETL might work in the real world, but I've barely scratched the surface of its potential applications across different industries. Here are some other examples of common ETL use cases:
Business intelligence (BI): BI software helps you perform data analysis at scale, and you can use ETL to centralize all the data you feed into your BI app.
Data migration: When moving data from one system to another—like from a legacy database to the cloud—ETL pipelines can extract your existing data and transform it based on the new system's parameters.
Data integration: ETL can be used whenever you need to combine data from disparate sources and store it in a single repository.
Real-time analytics: You can configure an ETL pipeline to load new data incrementally into a database, allowing for real-time monitoring, insights, and alerts.
Machine learning and LLM prep: Data may need to be cleaned, normalized, and filtered before being uploaded to a machine learning or large language model, and ETL pipelines can automate this process.
Regulatory compliance: In sensitive industries like healthcare or defense, you can use ETL to anonymize or otherwise transform data to meet compliance standards.
ETL vs. ELT
ETL and ELT might look like an acronym and a typo, but they're actually two slightly different data management processes. While ETL pipelines extract data and transform it before loading it into a target system, ELT pipelines extract data, load it into the target system, and then transform it. So, the letters stand for the same things, but the order shifts.
With ELT, you'll load raw extracted data directly into the target system. From here, you can transform it whenever (or if) you choose using the processing power of the warehouse it's stored in. This method is commonly chosen for moving massive quantities of unstructured data into data lakes or cloud warehouses, and it's generally quicker than ETL.
ETL is the pipeline of choice if you need to transform data before storing it—for example, to ensure a dataset meets security compliance standards before it lands in a warehouse. In reality, though, whether you use ETL or ELT will depend on contextual factors: the industry you work in, your data's volume and complexity, and what kind of system it'll end up in. One isn't necessarily better than the other, but ELT has shot up in popularity due to the increased storage capabilities of cloud systems.
ETL tools
While it's possible to write your own ETL code from scratch, there are tons of tools out there that do most (or all) of the work for you. Here are some of the more popular low- to no-code solutions available.
Fivetran is an automated data movement platform that can extract and transfer data from 650+ sources.
Airbyte is an open source data movement solution with over 300 integrations and a high level of extensibility.
Coalesce is an AI-powered data transformation tool for use with Snowflake, Databricks, or Microsoft Fabric.
Informatica is a platform-neutral ETL tool that specializes in cloud data integration.
Hevo Data is a no-code tool that lets you set up and automate data pipelines using a drag-and-drop interface.
IBM DataStage is an enterprise-level data integration tool for building ETL and ELT pipelines that includes 24/7 access to IBM technical support.
Zapier (whose blog you're reading now) is an AI orchestration platform that can transfer and transform data from 20+ popular enterprise tools.
Automate data management with Zapier
I'm not going to blow any minds by saying this, but the more data management tasks you can automate, the better. ETL and ELT are key data movement processes, but what you do with your data once it's transferred and stored is like a bigger, shinier key that opens lots of doors.
With Zapier Tables, you can set up automated workflows that send data from every app you use to a centralized database. Once your data's in a Table, you can make it work for you with actions that trigger when updates or changes are made—like sending lead info directly to your CRM with a single click. And you won't have to fiddle around with manual software integrations, as Zapier connects with more than 7,000 apps instantly. Learn more about how to use Zapier Tables for data management.
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