Five Things to Know About ETL

  1. ETL (extract, transform, load) is a data integration process that helps businesses extract data from various sources, prepare it for consumption, and ship it off to a data warehouse or another tool for further processing. 
  2. When using ETL, data extraction can take place using a variety of source systems. Plus, source data can be processed in either structured or unstructured form.
  3. Some of the transformations that can be applied to data include filtering, authentication, encryption, and formatting.
  4. The best integration platforms create a low-code ETL solution by offering pre-built connectors for the most popular business tools. 
  5. In addition to ETL, your business may want to use ELT for large volumes of data or reverse ETL to keep data updated and enriched in downstream apps. 

Without data integration processes like ETL, today's businesses would hardly be able to make sense of the constant streams of data flowing into their tools. Of course, even though ETL is widely recognized as mission-critical to data management and business intelligence (BI) initiatives, that doesn't mean it's the most straightforward process to implement.

For those looking to better understand how ETL can help their business, or what similar integration methodologies exist, this is the guide you need. Here at, we provide low-code solutions that allow you to use data for critical business operations and everyday decision-making, so let's dive into how they work. 


Table of Contents

  1. Common Data Integration Methodologies
  2. Primary Use Cases for ETL
  3. Considerations and Components of ETL
  4. How to Build an ETL Process
  5. Unlock Low-Code ETL With

Common Data Integration Methodologies

When most people think about data integration today, ETL is the exact process that comes to mind. However, before there was ETL, most companies used a process known as ELT, which takes a different approach when it comes to the order of steps.

Both ETL and ELT have their use cases for modern businesses, and deciding which one is best for your situation requires you to first understand how they differ. Meanwhile, reverse ETL is yet another modification to the ETL process, and it can prove very valuable to enrich and aggregate your data in downstream apps. 

With these things in mind, let's dive deeper into how ETL, ELT, and reverse ETL differ, along with how you can use each of these data integration methodologies to better utilize the data that your business is collecting. 

Extract, Transform, Load (ETL)

Extract, transform and load (ETL) is one of the most common data integration methods as it allows you to extract data from anywhere, prepare it for use or storage in another tool, and then load it up for use. The exact steps in the ETL process vary from one tool to the next, but it actually consists of five parts: extract, clean, transform, load, and analyze. 

The extraction process allows you to pull data from somewhere in your tech stack, usually from a database of raw data (like your invoicing system). From there, the data is cleaned to ensure it is valid while removing duplicate and outdated entries. The transformation process then takes place in which the data can be encrypted, unencrypted, reorganized, or otherwise changed. Once the data is transformed, it is loaded into your data warehouse, which your data analysis tools can pull from to generate reports. 

Looking at ETL from a practical standpoint, here are some important distinctions to note:

  • The ETL process sends data to a staging server first, where data transformations take place, before sending it to the target system.

  • If you're using ETL, you're generally working with smaller amounts of data. For very large amounts of data, you should consider ELT.

  • The best ETL platforms offer speed and ease, with pre-built connectors to get you up and running faster without bogging down your engineering team. 

Extract, Load, Transform (ELT)

Extract, load, transform (ELT) is similar on paper to ETL, but because the order of operations is different, so are the underlying infrastructure and ideal use cases. The key difference between ETL and ELT is that the latter loads your data into the target system before any transformations take place. 

On the plus side, ELT leads to faster transfers since your data does not need to load into a staging server. However, it also leads to a myriad of concerns—like data privacy concerns about loading large amounts of raw data into your data store and relying on encryption and other transformations to take place after the fact. Of course, depending on the type of data you're working with, ELT might be the ideal solution. 

Generally, both ELT and ETL processes pull data from the same types of sources (such as databases, CSV files, and SaaS applications) for data warehousing purposes. The primary difference is that ELT is intended for cloud-based data warehouses where it is possible and efficient to transform data after loading using code such as Python scripts. 

With these things in mind, here's what you should remember about ELT tools: 

  • Since transformations happen after data is loaded, there is no staging server and your data is only moved once.

  • In most cases, ELT is used for big data purposes in instances where you need to load data in very large quantities into cloud data warehouses. 

  • Unlike ETL, the ELT process does support data lakes, but implementing ELT requires deep knowledge of the data source, target system, and what needs to be accomplished. 

Reverse ETL

ETL or ELT can help you move data into a data warehouse for easier consumption, but what if you need to move data out of your data warehouse and into another tool in your tech stack? That's where reverse ETL comes into play. Reverse ETL allows you to extract data from within your data warehouse, transform it for further consumption, and then load it into a third-party tool.

As to why it's referred to as reverse ETL and not reverse ELT, it comes down to the technical process. It is known as reverse ETL as the data is transformed before it is loaded. However, it is different from traditional ETL processes in that the data is not moved to a staging server for transformation, but instead, it is transformed within the data warehouse before it leaves. 

With that cleared up, reverse ETL is what most businesses are thinking about when they're considering all the use cases for their data. For instance, using reverse ETL, you can set up multiple pipelines that take data out of your warehouses and feed it into BI tools that use machine learning to create real-time data-powered dashboards showing website performance, live traffic, open support requests, and daily sales totals all in one place. 

Aside from business intelligence tools, other common destinations for reverse ETL processes include CRMs, ERPs, and marketing platforms. So, you should note that: 

  • Reverse ETL allows you to extract data from your data warehouses and send it to third-party tools, such as business intelligence dashboards.

  • Since data is transformed before it is loaded, it is known as reverse ETL and not reverse ELT. However, data is transformed within the source warehouse instead of on a staging server.

  • Top use cases for reverse ETL include syncing internal support channels with sales data to improve customer support or combining support and sales data for marketing personalization. 

Primary Use Cases for ETL Tools

As you can imagine, there are endless ways to use ETL, ELT, and reverse ETL to move data around and put it to work for your business. However, reviewing some of the primary use cases for these methodologies can help you begin to understand just how powerful they are and how they can help your business with its specific goals.

Business Intelligence

Regardless of your industry or the size of your business, unlocking a business intelligence (BI) dashboard that can pull data from over 200 different sources and effortlessly stitch them into visual data analytics is immensely valuable.

With a low-code platform, can help you pull it off without requiring your engineering team to manually code out your integrations. The result? You can break down your data silos and easily combine and use the data stored across your repositories. 

Data Security

While processes like ELT might make you worry about vulnerabilities associated with transferring raw data, securETL from gives you peace of mind by supporting data security compliance and helping you encrypt data before it's moved.

Through partnering with Amazon's Key Management Service (KMS), ensures your data is protected by Field Level Encryption (FLE) before it ever leaves your systems. In order to de-encrypt your data, a secret key is required, and that key will always be held by your business, ensuring the ultimate safeguards for your business. 

Sales and Service

Today's businesses have a wealth of data about their customers in the form of both transactional data and event data collected by advertising platforms, support ticket systems, and your on-site analytics. The trouble is, this data ends up obscured and stored in a dozen different places, which stands in the way of you gaining a complete view of your customers.

With, you can easily unify information from all of your customer data sources, like Hubspot, Salesforce, and Zendesk, ultimately unlocking new insights about who your customers are, how you can better serve them, and what they need from your business the most. 

Considerations and Components of ETL

Data integration is a crucial process for modern business operations, but whether you intend to use ETL, ELT, reverse ETL, or a mixture of all three, it's crucial that you take a well-planned approach to avoid issues and inefficiencies down the line. Here's a rundown of some of the most important things to remember. 

  • Latency: Some of your ETL pipelines will be more tolerant of latency than others. For instance, you might only care that your marketing platform is updated once a day, but you might need a business intelligence dashboard to have the latest information at all times. Plan out your pipelines to ensure data freshness where it matters most.

  • Scalability: Gone are the days when implementing an ETL solution required months of work from your data teams. While you still need to plan out your data integration process, platforms like help you execute quickly while creating an ETL process that's highly scalable, so you won't be penalized for business growth.

  • Accuracy: Ensuring data completeness and quality is essential to actually use ETL in a way that's beneficial for your business. During the transformation and loading process, you need to pay attention to how data will be consumed and used at the other end, and make sure you aren't losing or changing information that could lead to misinterpretations. 

  • Logging: As you collect, change, and move data throughout your tech stack, you need to keep proper logs so that the data trail can be audited later on. This ensures data quality, but it also helps you comply with regulations when it comes time to remove certain data from your systems.

  • Fault Tolerance: The last thing you need is an ELT pipeline that causes duplicate entries or lost information when the first run fails. Problems will occur, so ensuring that your ETL process is fault-tolerant and able to get past common problems without breaking them down is essential to accuracy, completeness, and timeliness. 

If you're new to the world of ETL, it can be easy to feel overwhelmed. However, with a platform like in front of you, you'll find that achieving most of these goals becomes easier thanks to a great support team, robust documentation, a user-friendly interface, and low-code solutions that allow even non-technical users to get started forming an ETL pipeline that can supercharge your business operations.

How to Build an ETL Process

Data science teams used to spend countless weeks creating data pipelines, but with modern solutions like, your data engineers can get a new data pipeline up and running in mere hours without being bogged down by the backend technical aspects of it. With that said, you still need to plan out your pipelines to ensure you're making the most of your data.

Before you construct your ETL process, you should follow these steps to make sure nothing is overlooked. 

  1. Make sure you choose the right platform. The best data integration solution is the one that requires the least manual setup and maintenance. With, you can use over 200 pre-built connectors and quickly create your own using a built-in Python editor.

  2. Figure out what your business needs to connect. Identifying your data silos and where you need information to start flowing is crucial to unifying your business. With, you can easily connect on-premises, legacy, and cloud systems.

  3. Avoid maintenance and rework. By choosing a data integration tool like that handles the backend operations for your business, you will have little to no maintenance tasks and you can easily follow the documentation to get things set up right the first time, eliminating rework. 

  4. Set goals and benchmarks. More than seven in 10 users get their pipelines up and running in the first hour. Plus, the total time it takes to load their data sets can be reduced by up to 95 percent. By setting benchmarks and goals like these, you'll ensure that the ROI of your ETL project is on track to success.

Once your pipeline is running and you are tracking your benchmarks, it's just a matter of counting the hours you've saved with the help of a low-code solution like By tracking additional metrics down the line, like how the support dashboard you created is helping speed up ticket resolutions, you can even come to realize just how much revenue is generated with the help of your data pipelines. 

Unlock Low-Code ETL With

Now that you understand the most popular data integration methodologies and can begin to imagine all the possibilities of working with ETL, ELT, and reverse ETL, the next question is simple: How do you get started? The best path forward is to get hands-on with a platform that can do it all, without weighing down your business. 

If you're interested in learning more about how works or how our low-code solutions can support your business goals, it's time to talk with our team. Schedule an intro call today or reach out to to learn more.