What is ETL/ELT? A Guide to Data Integration, Extraction & Replication

June 11, 2024  

ETL Is The Process of Moving & Combining Data from Disparate Sources

What Is Data Integration?

Data integration brings together data from multiple places and combines all the data together. 

For example, many businesses have a CRM for sales, a helpdesk product for customer support, and a billing system for sending invoices. Each tool has its own internal database, and they do not talk to each other. So, asking holistic questions is impossible.

Questions like “Do my customers who drag their feet to make an initial purchase also put a strain on my support team?” are impossible unless all the data comes together.

Sometimes this will take the form of API calls to request data from a specific source, such as in software application development. ETL integration processes are similar but better suited for complex data that requires some reformatting, consolidation, or cleanup before becoming useful.

ETL/ELT data integration is especially useful in data analytics so that you can answer questions across all your business systems.

What is ETL? / What is ETL in data?

ETL stands for Extract, Transform, Load, and it's a process used to manage big sets of data. Here's how it works. First, "Extract" means pulling data from various sources, which could be databases, websites, or other platforms. Then, "Transform" takes that extracted data and cleans it up or changes its format so it all fits together neatly. This makes it easier to work with later. Finally, "Load" means taking that clean, organized data and storing it in one central place, like a data warehouse.

This way, the data is ready and easy to use for making important decisions or running reports. So, ETL helps in data integration by collecting data from many places, making it uniform, and then storing it where it can be easily accessed and used in a data analytics platform.

Extract, Transform, Load

What Is The ETL Process?

Let’s break down each part of ETL - extract, transform, and load.

What Is Data Extraction?

Data extraction is a step in data analytics to get data out of source systems and into a central warehouse. Sometimes it is referred to as “data replication,” because data is copied from the source system, not removed from it.

Extracting data with an ELT tool or ETL tool is the first step in centralizing data for comprehensive analysis. This data movement from the source systems, applications, SaaS products, or data brokers gets data into a central location such as a data lake or data warehouse.

What Is Data Replication?

Data replication is a step in data analytics to get data out of source systems and into a central warehouse. This is also referred to as “data extraction.”

Data movement from source systems into a central repository uses pre-built or custom data extraction software. Often these refer to three distinct steps:

  • Source system
  • Target system
  • The job or flow, that runs the code to move data from the source system to the target system

What Is Data Transformation?

In ETL (Extract, Transform, Load), "T" stands for Transform, representing the phase where data undergoes cleansing, restructuring, or enrichment to meet the requirements of the target system or analytical process. 

During transformation, data may be manipulated, aggregated, or converted to ensure consistency and usability for downstream applications. This stage plays a crucial role in ensuring data integrity and relevance before it is loaded into the destination database or data warehouse.

What Is Data Loading?

The final phase, load, is where transformed data is inserted, updated, or merged into the target database, data warehouse, or analytics platform. 

This step involves efficiently loading the prepared data into the designated storage, ensuring its accessibility and usability for various analytical and operational purposes. The loading process typically includes validation and verification steps to guarantee the integrity and accuracy of the data within the destination system.

What Is ETL In A Data Warehouse?

ETL in a data warehouse is a specialized process designed to move and organize data efficiently.

"Extract" means pulling data from various operational systems or data sources you use in your business.

"Transform" then cleans, formats, and enriches this data, making it uniform and ready for analysis. This is critical because data warehouses are structured to support fast queries and complex calculations.

"Load" adds the data to the warehouse itself, organizing it in a way that makes it fast and easy to retrieve when you need to make data-driven decisions.

The ETL process is essential for creating a robust data warehouse that serves as the backbone for your analytics, reporting, and decision-making needs.

ETL tools and software

What Are ETL Tools & ETL Software?

ETL tools or ETL software automate the process of extracting, transforming, and loading data into a database or data warehouse. Although ETL processes can be programmed from scratch, when it comes to extracting, transforming, and loading data, a purpose-built tool can save a lot of time and reduce errors.

These tools pull data from different sources, clean and format it, and then insert it into a destination database. This automation speeds up complex data tasks and helps ensure accuracy, making it easier to use data for analytics and decision-making. ETL tools & software often come with pre-built connectors for various data sources and offer features like data mapping, scheduling, and monitoring.

What Is Reverse ETL?

Reverse ETL takes data from a data warehouse and pushes it into operational systems, like CRMs or marketing platforms.

“Regular” ETL moves data into a warehouse for analytics; reverse ETL takes those insights and applies them where they can drive action. This enables businesses to better synchronize their data across various tools, improve automation, and make data-driven decisions in real time.

For audiences who need data insights, having those insights pushed into the tools they use day in and out is extremely productive. For example, having data insights pushed back into a CRM that the sales team uses daily. The alternative is going to find answers in reports or dashboards.

Essentially, reverse ETL closes the loop between analytical insights and operational activities.

ELT vs ETL

ELT vs ETL

ELT stands for Extract, Load, Transform. Unlike traditional ETL (Extract, Transform, Load), where data is transformed before it's loaded into a data warehouse, in ELT the data is first loaded into the warehouse. The transformations are done after the data is already in the warehouse.

The ELT approach takes advantage of the robust computing power of modern data warehouses to handle large datasets and complex transformations.

ELT is often faster and more scalable, making it a good fit for big data scenarios. By separating the extraction and loading from the transformations, these can be managed and scaled independently. It has also allowed specialized tools and vendors have come up that focus on pieces of the data analytics process.

What Is ELT In Data Warehousing?

ELT (Extract, Load, Transform) refers to the practice of first loading raw data into your data warehouse and then transforming it there. Unlike ETL, where transformations happen before loading, ELT leverages the power of the data warehouse to handle the transformation work.

Because modern data warehouses are built using the scalability of cloud platforms, this can offer speed and performance benefits. If you're looking for a data warehouse, consider whether it supports ELT capabilities, as this could influence the speed and flexibility of your data operations.

Benefits Of Using an ETL Tool

Manage & Connect to Big Data Anywhere (Cloud, etc.)

With the rise of cloud technology and big data ETL tool offerings, managing and connecting to your data has never been more accessible. Whether it's on the cloud or on-premises, data integration software ensures that you have constant and reliable access to your essential data, fostering seamless collaboration and insight-driven decision-making.

Data Security Considerations

Data security is paramount, and secure data movement and replication are crucial aspects of any data management strategy. Data replication solutions and robust processes ensure that your data is moved securely and replicated accurately across systems, safeguarding its integrity and availability.

Select ETL tools or a vendor that pays attention to how your data flows to the warehouse, where it goes, how it is encrypted, and who can access it. 

Seek Expert Assistance With ETL & ELT Tools

In the complex landscape of data management, many tool vendors promise an easy path. Yet even the most advanced data integration software requires expert hands to unlock its full potential.

In today's data-driven world, you might find tool vendors promising that everything will be easy. Yet, achieving true success with data integration and data extraction goes beyond mere tools. 

It requires a well-coordinated combination of technology, process, and people. That's where the challenge—and opportunity—lies for you. With the right vendor, you can discover a consistent approach that simplifies the complexities. Look for vendors who use the leading commercial and open-source data extraction tools configured to work together. This modern data architecture allows you to focus on what truly matters: driving business value. 

ETL tools and ELT tools are critical in getting data out of operational systems, databases, SaaS products, and APIs. However, understanding how to leverage them effectively can be a challenge. 

Why spin your wheels for months trying to figure out how to apply tools and technology? By seeking out expert assistance, you can have data flowing into a data warehouse immediately, with production-ready data pipelines and managed analytics monitored for you. 

Look for a vendor that manages hundreds of data sources and automated extractions to ensure your analytics are always fresh, accurate, and secure. With proper expertise, you'll find tailored solutions that use top-of-the-line ETL software and ETL platforms to transform, load, and extract data in a way that suits your unique requirements.

Related Article: How Much Do Data Analytics Services Cost?

Data integration platform

Simplify With A Single Data Integration Platform

Simplification is key in today's complex data landscape. Datateer's unified data integration platform provides a one-stop solution for all your data needs. From data extraction to integration, our ETL solution streamlines your data processes, allowing you to focus on deriving meaningful insights and driving real business impact.

We partner closely with multiple vendors who provide ETL/ELT tools that we integrate into the platform. With over 500 connectors at our disposal and an open-source framework to create new ones, you will always have a way to integrate your data sources.

Contact Datateer


Adam Roderick

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About the Author

Adam's tech career spans startups to global firms, touching finance, tourism, e-commerce, and consulting. Spotting a way to give small- and medium-sized companies an advantage with data, he founded Datateer to democratize analytics. He values relentless progress, simplifying complexity, and putting people first.

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