Business data is the fuel that powers smarter decisions, drives growth, sparks innovation and keeps your company ahead of the competition. It has the potential to take your business to new heights, but making sense of it all can feel overwhelming. That’s where data integration comes in — helping you bring together information from leads, customers, employees and SaaS apps.

Extract, Load, Transform (ELT) pipelines move and organize your data quickly and efficiently so analysts can turn it into actionable insights. In this article, we’ll break down how the ELT process works, highlight the key differences between ELT and ETL and show you the advantages of a fully automated ELT approach.

What is ELT? 

The whole purpose of a data pipeline is to extract data from 1st- and 3rd-party data sources, like internal databases or SaaS apps, and then load that data into a 1st- or 3rd-party data destination so it can be used elsewhere. 

The first step is always extraction (E), but depending on the pipeline’s architecture, the second and third steps are either load (L) or transform (T). Historically, the T came before the L so data could be ready for downstream use cases without the computational expense of transformation (back when most compute happened on-premise). But now we have cloud infrastructure with practically-infinite computational capacity and the T can happen on-the-fly.

ELT came about as a modern, cloud-first approach to data integration, making it faster and more efficient for cloud-based workloads. Instead of processing data before it reaches the warehouse like the traditional ETL method, ELT loads it straight into the destination system and handles the transformations on an as-needed basis. There are several advantages, including the ability to preserve data as close to its original data source formatting as possible.

Tools like Fivetran make ELT even easier by automating the entire process, so you can spend less time on logistics and more on strategic data analysis. With streamlined pipelines, you can harness your data more effectively and adapt quickly to changes in the market. In many cases, you don’t even need to bother worrying about ELT or ETL because Fivetran does all the heavy lifting.

How ELT works

The ELT (Extract, Load, Transform) workflow takes full advantage of modern data storage solutions to streamline your data integration. Let's break down each step of the ELT process so you see how it works: 

1. Extract: Your data comes from various sources: applications, websites, APIs, files and databases. It gathers both structured and unstructured data, capturing the wide array of information that modern data analytics rely on.
2. Load: Once your data is extracted, it loads into a centralized storage system like a cloud data warehouse or data lake. ELT focuses on fast data loading to support high-speed processing, crucial for handling peak operational periods or meeting real-time analytical needs.
3. Transform: In ELT, your data is transformed within the storage environment. This step uses the processing power of your data warehouse to perform complex tasks such as cleansing, aggregating and enriching data. The added flexibility lets you swiftly adjust transformations, enabling you to extract customized insights that align with your business strategies.

By flipping the traditional ETL process on its head and prioritizing loading before transforming, ELT speeds up data access and enhances scalability, giving your organization a powerful way to capitalize on its data assets.

ETL vs. ELT: The best data strategy

It would be nice if traditional ETL processes could keep up with the demands of modern data environments, but often, they just can’t. The reality is that ETL can create bottlenecks and both ETL and ELT offer distinct benefits. Understanding how they differ can help you select the method that aligns best with your specific data needs.

Scalability and flexibility

ELT is scalable and flexible, especially in comparison to ETL. It lets the cloud data warehouses you use easily scale up to handle increasing data loads as your needs grow, all without having to re-engineer your systems. ETL is a reliable option, but scaling it up often demands extra planning and resources because the data transformations happen in-flight before the data is loaded into the destination. The additional effort risks slowing you down when you need to keep pace with business growth.

Performance and speed

The old saying “time is money” really hits home here. ELT usually works faster than ETL because it cuts down the time data spends in transit. You transform data as it’s leaving the data warehouse environment, which is especially beneficial for large datasets where you need access to real-time data. In contrast, ETL can slow things down by processing data before it's stored, creating a bottleneck that affects speed.

Cost implications

ETL systems often have higher initial costs because they require extensive data transformation before you can even start loading data. In contrast, ELT tends to be more cost-effective because it uses the cloud data warehouses you already have, which reduces the need for additional hardware and specialized staff. These cost savings make ELT an appealing choice for organizations looking to keep their budgets under control. Instead of transforming all the data all of the time, you only transform it based on downstream use cases after it’s been loaded into the data destination.

Modern data uses

ELT is gaining traction for data environments where quick reactions and real-time analysis are essential. It’s a game-changer when you’re dealing with massive amounts of data and need to make decisions fast. Thanks to its smooth integration with cloud technologies, ELT thrives in dynamic, fast-paced settings. On the other hand, ETL still has its place, especially in on-premise systems or situations where batch processing and thorough data cleansing are more important than speed.

Choosing between ETL and ELT comes down to your unique data needs and business goals. Ultimately, you’ll need to find the right balance of speed, cost, scalability and functionality to match your strategy. Weighing these factors correctly is key to picking the approach that best supports your objectives.

7 key benefits of switching to ELT

ELT boosts the speed and efficiency of how you handle data. Let's explore seven standout benefits that show why ELT has become an essential tool for smarter data management.

1. Speed up insights with faster data availability

ELT speeds up how quickly you can access your data by loading it straight into your data warehouse before any transformations happen. In practical terms, it gives you immediate access to the latest information and can gain quicker insights. Reducing these delays is especially helpful in fast-paced environments that prioritize timely information.

For example, Gill Capital, which operates major retail brands like H&M across Southeast Asia, uses Fivetran to integrate SAP data into Google BigQuery. By gaining near real-time visibility into sales and inventory levels, Gill Capital reduced costly overstock and stockouts, increased sales by 25%, and improved decision-making across its operations. Immediate access to accurate data allows their teams to react faster to changing demand, ensuring shelves stay stocked and sales opportunities aren’t missed. Read the full case study.

2. Future-proof your data ecosystem with ELT

Adopting ELT future-proofs your data infrastructure, integrating smoothly with new tools and adjusting to new data formats so you are always up-to-date. ELT pipelines keep your company ahead of the game by tapping into ongoing advancements in cloud data warehousing.

Dropbox’s use of Fivetran is a great example here. They centralized terabytes of data from disparate sources into Snowflake using Fivetran. As Dropbox acquired companies like HelloSign and DocSend, they easily integrated various data formats and SaaS sources ensured seamless onboarding, future-proofing their analytics infrastructure as the business grew.

3. Cut costs with efficient ELT processes

Switching to ELT lets you take advantage of powerful cloud-based data warehouses, which cuts down on your need for extensive local processing hardware. In addition to reducing operational costs — the cloud transforms data more efficiently — it also helps you save time and money in the long run.

Trinny London, one of Europe’s fastest-growing beauty brands, struggled with siloed data systems that slowed decision-making and required extensive manual effort. By adopting Fivetran’s automated ELT processes and integrating data into Google BigQuery, Trinny London eliminated the need for complex local infrastructure and manual pipeline management. They also save over £260,000 annually with Fivetran.

4. Enhance efficiency with ELT automation

Using cloud-based ELT platforms means you're always up to date. Unlike ETL, ELT platforms allow you to receive regular updates that boost their functionality and security automatically — without messing up your current workflows. This capability helps your data handling get smarter over time, keeping you ahead of the curve with very little extra effort on your part.

5. Stay current with regular ELT updates

ELT offers incredible flexibility with data transformations. You can adjust or expand them as your business needs change. Instead of being stuck with rigid processes, you can tweak data models right in the data warehouse to fit exactly what you need at the moment. This flexibility lets you quickly adapt to market shifts or changes in your strategy.

6. Boost flexibility in data transformation with ELT

ELT's scalability is a major advantage for growing businesses dealing with increasing volumes of data. Your cloud data warehouse can scale up effortlessly to meet increasing demands, so you don't have to worry about piling up physical resources. As your business expands, your data processing capabilities grow right along with it, ensuring everything keeps running smoothly.

Cemex, a global leader in construction materials, uses Fivetran’s ELT processes and Snowflake to seamlessly scale its data infrastructure across 1,800+ facilities worldwide. By eliminating the need for manual scaling or physical hardware, Cemex can effortlessly handle growing data volumes and real-time analytics. This flexibility ensures their operations remain efficient as data demands increase, supporting supply chain optimization and faster decision-making on a global scale.

7. Scale effortlessly with ELT's robust capabilities

Switching to ELT improves your data management capabilities, giving you critical advantages like faster processing, cost efficiency and greater flexibility. As more businesses need timely data to make smart decisions, adopting ELT can give you the competitive edge necessary to excel in a fast-paced, data-driven marketplace. 

If you want to maximize your data management effectiveness, you need to take a look at ELT. It revolutionizes how you interact with your data and helps your business thrive. And if you want to see more companies successfully using Fivetran for ELT, read through our case studies.

Flexibility: The foundation for ELT Success

Switching to ELT gives you the flexibility you need when you need it. But to really make the most of it, you'll need a strong foundation — both in your tool selection and setup. Let's walk through what you need to focus on to build a formidable ELT.

• Assess your data sources: Start by taking a close look at all the data sources you currently use. Get to know their specifics inside and out. Understanding these elements is key to customizing an ELT solution that fits just right with your organization's unique needs.
• Choose the right ELT platform: Make sure to pick an ELT platform that meshes well with your existing systems and meets your needs for performance and security. The right platform will simplify your processes and improve your overall system efficiency.
• Establish data governance policies: Strong data governance is foundational for maintaining data integrity and compliance. Set clear policies to ensure high data quality and secure access throughout your organization. 
• Design scalable data architectures: Build a data architecture that grows with your company. The design should handle increasing data volumes easily without compromising performance.
• Plan for disaster recovery: Have a disaster recovery plan in place to protect your data from unexpected events. A strong strategy helps you quickly recover your data, minimizing downtime and preventing loss.

Creating a strong ELT framework takes careful planning and smart decisions right from the start. By focusing on a few key areas, you can build a data management system that is efficient and scalable and also set up to support your business for the long haul.

Operationalizing ELT to serve your business needs

Moving to ELT can feel like you've just upgraded your data toolkit. Here’s how you can keep those tools sharp and ready to go, turning ELT into a well-oiled machine that serves your business needs. 

• Automate data integration processes: Automating your integration tasks can streamline your operations, cutting down on errors and keeping your data consistently updated. It’s a smart way to keep things running smoothly without the extra hassle.
• Optimize data transformation workflows: Keep tweaking your data transformation steps to get more efficient and cut costs. This ongoing fine-tuning helps you maximize efficiency without sacrificing quality.
• Monitor and maintain your ELT system: Make it a regular habit to check how your ELT system is performing. Quick troubleshooting of any issues not only keeps your operations smooth but also prevents bigger problems down the line.
• Ensure continuous improvement: Always be on the lookout for ways to make your ELT processes better. Adapting to feedback ensures your system evolves to stay relevant and effective for your needs.
• Train your team: Make sure your team knows their way around your ELT system with thorough training. Skilled handling by your team means you get the most out of your ELT investment.

The success of your ELT depends on continuous refinement and proactive management. Automating and optimizing your processes keeps your data operations in line with current demands and ready for future challenges.

Seamless integration: Set it and let automation handle the rest

Setting up integrations for an ELT process with a dynamic data pipeline tool is simple and hassle-free. You just need a user account with the right access levels to start linking your data sources to your data warehouse. In most cases, the entire setup takes just a single step.

Once you’re set up, automated data connectors load all the historical data from your sources and keep your datasets up to date with any new changes. It only takes a few minutes to get everything operational. After that, your integrations run themselves, adapting to changes in your data sources without you having to lift a finger. If you’re interested in seeing how this works, check out the Fivetran Connector Directory. Begin with a free trial or use the free plan.

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