Here are five things to know about ETL and how it benefits your Ecommerce business:

  1. ETL stands for extract, transform, and load. It's one of the oldest and most popular data integration methods and allows you to move data from different sources to a centralized target system for data analysis. 
  2. During ETL, data management teams extract data from a source, transform it into the correct format for data analysis, and load it into a system like a data warehouse. This process requires the completion of complex big data pipelines and involves lots of programming and code.
  3. ETL tools automate the above process, allowing Ecommerce organizations of all sizes to move data between locations with little or no code. 
  4. After loading data into a target system, Ecommerce organizations can run that data through business intelligence tools and generate insights about sales, marketing, inventory, and customer service processes. That helps decision-makers solve problems and grow their business.
  5. is a data warehousing integration tool for Ecommerce that comes with out-of-the-box connectors for data warehouses like Snowflake, Google BigQuery, Microsoft BI, and Amazon Redshift. 

Think about all the data that exists in your Ecommerce business. That might include customer data, inventory data, sales data, advertising data, and social media data. 

Now think about all the software and systems that store that data. These might include transactional databases, relational databases, customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, and SaaS tools.

With so much data and so many software and systems, making sense of Ecommerce-related information can be challenging. ETLing Ecommerce data to a unified repository like a data warehouse provides a solution. In this guide, learn about ETL and why Ecommerce enterprises like yours should use it. 

Table of Contents is a data warehousing integration platform built for Ecommerce retailers. It ETLs data from multiple sources and moves it to a data repository of your choice for deep data analysis. That allows you to generate business intelligence and make smarter decisions about day-to-day Ecommerce operations. Email for a 7-day demo. 

ETL, Explained

Extract, transform, and load (ETL) is a data integration method that's been around in one form or another since the 1970s. It's now the most popular technique for integrating Ecommerce data from different data sources. 

The best way to define ETL is to break down each stage involved in the process: extract, transform, load.


During the extract phase of the ETL process, data management teams extract data from various systems and software and place it into a staging area. Extraction involves locating and identifying the relevant data from a data source. Some of the most common data sources include:

  • CRMs
  • ERPs
  • Relational databases
  • Transactional databases
  • Web pages
  • Social media platforms
  • SQL/NoSQL servers
  • SaaS tools
  • Other Ecommerce software and systems


The transform stage of ETL happens immediately after extraction. Data management teams take raw data from a data source and change it to the correct format for data analysis. This part of the process is a complicated one. Teams might filter, cleanse, or de-duplicate data and turn it into an acceptable format for a centralized repository like a data warehouse. Sometimes, teams might change rows and column headers in raw data sets or format data into tables. That allows them to match data to the schema of the target repository. 

More recently, data management teams have considered data governance legislation like GDPR, HIPAA, and CCPA during data transformation. Depending on the jurisdiction or business niche, teams might not be allowed to process certain types of sensitive customer data for analysis. Moreover, the organizations they work for could face expensive fines for not complying with the law. Abiding by data governance legislation can make data transformation difficult.


Load is the final step in the ETL process. Data management teams load transformed data from the staging area to a centralized repository like a data warehouse. From here, data can be used in business intelligence (BI) tools like Looker, Tableau, and Microsoft BI, allowing Ecommerce organizations to generate real-time data analytics about their operations. 

Read more: Top 7 ETL Tools for 2022

The Problem With Manual ETL

ETL typically needs a data management team of data engineers and other professionals. These professionals build complex big data pipelines from scratch to facilitate ETL, which can take weeks or months to create. That's because creating data pipelines requires knowledge of code and programming languages like SQL. Many Ecommerce retailers lack these skills.

Many smaller Ecommerce companies can't afford to assemble a data management team, especially when the average salary of a data engineer in the United States is around $117,000 (correct as of July 2022). Still, these companies often can't analyze data and generate insights about Ecommerce without ETL. 

ETL tools solve the above problems. These platforms automate all of the tasks associated with the ETL process, allowing Ecommerce enterprises, regardless of their size, to extract, transform, and load data to a central repository with little or no code. 

ETL vs. Other Data Integration Methods

ETL isn't the only data integration method Ecommerce retailers use:


Extract, load, transform (ELT) is a method that involves:

  1. Extracting data from a data source
  2. Loading that data into a data repository
  3. Transforming the data into the correct format for data analysis

As you can see, ELT reverses the "load" and "transform" stages of ETL.

Many Ecommerce retailers use ELT to store unstructured data in another type of repository called a data lake. ETL, on the other hand, better suits structured data. 


ReverseETL essentially reverses ETL and moves data from a repository back to the data source it originally came from. That benefits team members in your Ecommerce organization who are already familiar with a particular data source and prefer to use it in their jobs. For example, you can use ETL to move data from Salesforce to a warehouse and ReverseETL to move that data back to Salesforce.


Change data capture (CDC) is a relatively new data integration method that identifies changes made to two or more databases. It enables Ecommerce enterprises to track database modifications and take quick action if those modifications are unwarranted. is a data warehousing platform that handles ETL, ELT, ReverseETL, and super-fast CDC. Choose the integration method that suits your data management integrations, and forget about building complex data pipelines or learning code. Email for a 7-day demo. 

What Are the Benefits of ETL?

Here are some of the benefits of integrating ETL into your Ecommerce data management strategy:

Handle Big Data

The average Ecommerce company uses multiple digital tools that contain data. The problem is that these tools are often separate from one another, causing the existence of data silos—data repositories controlled by one department or unit that are isolated from the rest of the Ecommerce organization. When silos exist, it can be difficult to compare data from two or more sources on a like-for-like basis. For example, data analysis can't take place if data exists in a transactional database and that database can't "communicate" with data in a separate relational database. 

ETL solves the issue of data silos by consolidating information from disparate sources in your Ecommerce organization and placing it into a centralized repository. At this point, data has become standardized and transformed into the correct format for analytics, allowing BI activities to take place. Without ETL, you might not be able to identify patterns and draw conclusions from all the data in your enterprise. 

Visualize Data

Even if data doesn't exist in silos, it can be hard to visualize how data sets from separate systems interconnect. One of the benefits of using an ETL tool is that it produces a visual flow of data, enabling you to visualize different data points at each stage of the data integration process. ETL tools have a graphical user interface (GUI) that maps data flows as they carry information from one location to the next. 

Once you have moved data to a repository like a warehouse, you can run that data through BI tools and visualize data even further. The best BI platforms produce dynamic visualizations that let you visualize trends and patterns in data, helping you make sense of all the information in your Ecommerce organization. For example, you can view data about customers or inventory processes on colorful charts, graphs, heatmaps, and reports. Share these visualizations with sales and marketing teams in your enterprise and make more informed business decisions. 

Get More Value From Data

When data moves through the ETL process, several processes occur that improve the quality of that data. For example, data sets are "cleansed" during the transformation stage, which involves removing inaccuracies and duplicated information. 

ETL tools will also ensure data conforms to specific rules and flag missing values, helping you achieve data consistency in your Ecommerce organization. As mentioned earlier, ETL can also support data governance and prevent the government from issuing financial penalties for not complying with frameworks like GDPR and HIPAA. 

Single Source of Truth for Data

Moving data to a centralized repository provides you with a single source of truth (SSOT) for all the data in your organization. Instead of relying on several systems, you can keep data in one secure location, making it easier to visualize that data and identify patterns and trends. An SSOT reduces the time it takes to identify data sets in your Ecommerce organization and removes inaccurate or incomplete data points. 

Improve Transparency

Conventional data integration tools, including data wrangling tools, don't record the steps involved in moving data from one location to another. That makes it difficult to know about any edits or updates to data sets. 

ETL tools, on the other hand, standardize data integration, allowing you to identify the different stages involved in moving data from a source to a target location. That can improve transparency and compliance in your enterprise. 

Get Unparalleled Ecommerce Insights

When moving data to a repository and then on to a BI tool, you can produce insights about your Ecommerce operations you won't find anywhere else. Here is some of the information you might generate when ETLing your data for analysis:

  • Find out which customers are most interested in your products and services. You can view insights about different customer demographics and segments in one place and learn more about the people who visit your online store. 
  • Identify supply and demand issues in your production facility or warehouse before bottlenecks occur. When analyzing inventory data, you can gain more insights into your supply chain, transportation, and logistics processes. That can help you improve shipping issues and enhance customer service processes.
  • Learning more about customers through BI tools can improve marketing campaigns and make it easier to target lucrative consumers. BI tools can help you find customer segments who might be interested in a specific product or service, helping you generate revenue and move more prospects through your marketing funnels. 
  • You can share BI insights with customer service teams and improve the customer experience. By learning what customers expect from your Ecommerce company, you can create deeper connections with those customers and improve engagement. 

Read more: ETL & Data Warehousing Explained: ETL Tool Basics

What Statistical and Modeling Techniques Can You Use After ETLing Ecommerce Data?

Here are some of the statistical and modeling techniques Ecommerce retailers use after ETLing data to a target location and running that data through BI tools:

Predictive Analytics

Predictive analytics helps you forecast future outcomes for your Ecommerce organization based on historical data. For example, you can predict whether you might experience a decline in sales in the future. Predictive analytics identifies business risks and opportunities, making it one of the most effective statistical techniques in Ecommerce data management.  

Prescriptive Analytics

If predictive analytics forecasts the future, prescriptive analytics helps you prevent scenarios from occurring and impacting your business. BI tools that use prescriptive analytics will create a model of your business and validate it against existing and historical data. You can use this model to make smarter decisions about sales, inventory, marketing, and customer service processes. 

Machine Learning

Machine learning can interpret complex data sets in your organization. This technology might predict whether a certain customer will purchase a product from your online store, for example, or whether you need to hire more customer service support staff at specific times of the year. 

ETL Use Cases for Ecommerce Retailers

Here are some use cases for Ecommerce retailers that prove the benefits of ETL:

  • A fashion retailer wants to create a marketing campaign for a new product line but doesn't know which customer segments to target. The retailer has customer demographic information in a CRM and customer-related data in several other databases. Using ETL, the retailer creates an SSOT for customer data and uses BI tools to learn which segments are likely to be interested in its new product line. 
  • An online retailer selling consumer electronics wants to manage inventory levels but has difficulty interpreting inventory data because of silos. The retailer ETLs data to a warehouse and uses BI tools to identify trends and patterns in inventory data, making it easier to manage supply and demand at busy times of the year.

Read more: How Does ETL Work?

How Simplifies ETL has a simple philosophy: to make ETL less of a chore. 

While manual ETL requires the completion of complicated big data pipelines and knowledge of code, simplifies the entire process, enabling all Ecommerce retailers to move data to a repository of their choice for analysis. extracts, transforms, and loads data into a target system with out-of-the-box no-code/low-code connectors. There's no need to master a programming language like SQL or build data pipelines. With its jargon-free environment and drag-and-drop point-and-click interface, moving data between a source and target location has never been simpler. You can find in-built connectors for all the major data warehouses, including Snowflake, Google BigQuery, IBM Db2 Warehouse, Azure Synapse Analytics, and Amazon (AWS) Redshift.

If ETL isn't the right data integration method for your Ecommerce company, handles ELT, ReverseETL, and super-fast CDC. 

Here are some other benefits: 

  • World-class customer service. Contact an team member by phone, email, or live chat.
  • A simple pricing model that provides ongoing value for money. 
  • Enhanced data security and compliance, including adherence to data governance frameworks like GDPR, HIPAA, and CCPA.
  • Salesforce-to-Salesforce integrations, allowing you to move data from Salesforce to a target system and then back to Salesforce again. is the data warehousing integration platform for Ecommerce that makes it simple to move data to a central repository. This no-code/low-code point-and-click platform automates many of the processes associated with data integration, removing the need for complex data pipelines. Schedule an intro call now or email for a 7-day demo.