ELT in E-commerce Industry: How Crucial is it?

Maithili Saralaya • Last Modified: June 14th, 2023

ELT for E-commerce

Your weekly grocery runs have now become a few quick clicks on a delivery app, and your groceries show up at your door 30 minutes later. You try on virtual glasses while sitting at home and can select the frames that make your face look just right. Since the pandemic in 2020, e-commerce has taken over the traditional brick-and-mortar style of shopping. In the U.S., e-commerce could reach 31% of sales by 2026, up from 23% now, as brick-and-mortar stores close and consumers choose convenience, and the market is predicted to only keep growing. 

Global E-commerce Sales Growth
Image source: Oberlo

This not only means better business but also more data to be harnessed. More customers, more information being collected about them, more channels to spread the word, and more operational moving parts create a vast pool of data waiting for analysis and smarter decision-making. Data engineers and data analysts within the e-commerce industry face multiple unique challenges while understanding how to unlock the potential of the vast amounts of data being collected. Using data processes like ELT in e-commerce could be the key to doing this.

In this blog, we aim to explore these obstacles, uncover the role of ELT in solving many of these, and explore how to build data pipelines and ELT processes in a way best suited for the e-commerce industry. Hope it proves useful to you! 

The E-commerce Data Problem

As an extremely fast-paced and competitive industry, e-commerce has many monsters to face regarding data.

Numerous, Diverse Data Sources

With the need to create a seamless omnichannel experience for the audience, and the many moving parts involved in the process, e-commerce businesses collect a large volume of data from a number of sources. 

Whether it is marketing data like campaign data from various channels, customer behavior data on a website or app, operational and logistical data like vendor or supplier databases, or financial and revenue data, the data involved is large in amount and diverse in quality. Frequent source data changes and pipeline breakages further complicate things.  

Speed of the Essence

With companies across e-commerce sectors offering two-day, same-day, and even same-hour delivery and hundreds of orders being placed per minute, the data pool in the e-commerce industry is constantly being updated and added to. Besides this, analyses that need to be performed are much more time-essential as well. 

While longer-term activities like trend forecasting can take place using data from over a longer period of time, operational and marketing activities happen at a rapid pace, and any delays can lead to a drop in sales. 

Low Margins, High Competition

As an industry with low margins, it becomes more critical for e-commerce businesses to focus on their customer experience to maximize customer loyalty and retention. Personalization for customers has been getting more and more sophisticated, with customer segmentation down to the closest detail. The large amount of customer data required to make this a reality must be dealt with intelligently so that not even a single angle is missing from the 360° customer view. 

Besides this, optimizing ROI also becomes a priority to improve margins. E-commerce marketers are often hyper-focused on increasing ROAS, while operations teams try to improve operational efficiency as much as possible. This task is only possible with a good amount of data to guide teams toward the path of least friction and maximum return. 

Benefits of ELT in E-commerce

The ELT (extract load transform) process can be a boon for e-commerce businesses to address many of these data-related conundrums. With pipelines set up in a centralized data warehouse, the data setup is much more conducive to solving the specific problems faced by teams dealing with e-commerce data. 

As Robin Smith, co-founder and CEO of VL OMNI, said in a conversation with eCommerce Nurse, “As a business scales and as transactional volume increases, the cost of doing manual data entry and the errors introduced start to justify integration and the costs of it.”

Single Source of Truth 

A single source of truth brings all the data from various sources and teams together. By integrating data into a single destination, the data from various departments can be accessible and actionable to each other instead of remaining discrete and disconnected.

Single Souce of Truth
Image Source: Jedox

Comprehensive, Faster Customer Views

Integrating all customer data makes customer segmentation and personalization much easier, allowing a 360-degree view of customers to be formed with ease. With a data stack already set up, this view is formed as soon as the data is generated, meaning it is also acted upon much faster. 

Not only the customer view but integrating data also provides a birds-eye view of the operational flow of the business, making it much easier to identify gaps and places of improvement. 

Data integration provides a much clearer view of the entire business data, collated from various teams across the organization. This makes it easier to make data-backed decisions based on patterns identified within the data. Teams can make timely decisions based on market fluctuations and remain more confident in their actions rather than merely relying on intuition. Predictive analytics can allow e-commerce companies to remain ahead of the trends and variations, allowing them to plan ahead and maximize campaign returns.

Improving Efficiency

ELT in e-commerce can be very helpful for operations management and efficiency, as an automated data flow reduces the risk of human error and increases the speed of integration as compared to manual processes. Crucial data points like orders would enter the warehouse in near-real time and combine with any other required data on customers, inventory, vendors, etc. They can be fulfilled quickly and with minimal effort to collate and manage the data.

Build ELT Manually, or choose an ELT Solution?

The task at hand is huge and can be daunting for a data engineer in an e-commerce business. Data teams within such businesses are often small but must handle vast amounts of structured, unstructured, and semi-structured data in various formats and sources. In such a situation, fully-managed ELT solutions can come to the rescue to reduce the unnecessary burden on the data engineers. 

Savings Everywhere: Engineering Bandwidth and Costs

With the limited resources present within data teams, every minute can be precious. An ELT solution can cut down on time spent on tasks like pipeline maintenance, firefighting for broken pipelines, and spending hours on coding multiple pipelines with different specifications, which are not the most effective use of a data engineer’s time. This clears up that time for tasks with more meaningful outcomes, like analysis and understanding the big picture that’s formed when the dots of the data are joined together. 

Maintenance can be an unforeseen and frustrating aspect of in-house ELT, taking up precious hours. Besides this, when comparing the long-term costs of in-house versus external data pipelines, the latter turns out to be much more cost-effective

With an ELT solution, no time would be lost in maintenance and broken pipelines, which is extremely important, especially in e-commerce companies, where orders are placed every minute. Hence, data teams can support critical data analysis that boosts ROI, optimizes operations, and increases return on ad spending and other marketing activities. 


As an e-commerce company grows, the data teams are often inundated by requests. Marketing teams may want to add new dimensions like acquisition sources for customers or A/B testing flags. New sources would also need to be added all the time, and the time and effort that goes into maintaining custom ELT pipelines often are not worth the effort. 

With the pre-built connectors to hundreds of sources, ELT solutions would prevent this issue, as new sources can be set up within a few minutes with no issues. The rapid, changeable nature of an e-commerce business and its various interconnected components mean they are perfect candidates for such solutions. 

Experimentation and Flexibility

With the assurance of the stability and reliability of the pipelines, it is much easier for business teams to feel independent and secure to explore and answer their questions using the existing data stack. ELT solutions offer a no-code, easy-to-use UI that can be set up based on the team’s expertise and used without fear of breakages. A good data governance strategy in conjunction with the ELT solution would be helpful to support this. Marketing teams, for example, would feel more comfortable experimenting with their ad campaigns if they knew they could very easily look at the data behind it to understand its outcomes and results. 


When the pipelines are broken, or under maintenance, critical, time-sensitive data is often lost. Issues can take a long time to resolve. In such a scenario, business teams lose confidence in the data and turn to other sources. Data becomes a source of chaos instead of insights. 

An ETL tool means fewer breakages and fewer issues with pipelines, increasing the trust and reliability of this data.

What’s Next? Building a Successful E-commerce Data Stack

For ELT in the e-commerce industry specifically, a few aspects can be crucial when it comes to building the data stack. It is imperative to think not only from a technical perspective but also about the end utility of the data and how its value can be unlocked optimally. 

Main Components of an E-commerce Data Stack

The first step when setting up a data stack for e-commerce is to consider the data sources. Data sources for this industry include product data like items, price, stock, etc., marketing data like ad campaigns and website data, data from the operations team like transactions and fulfillment state, customer services tickets and conversations, etc. It is important to consider which of these would be valuable to integrate and how. 

Next, choose a data warehouse that suits your needs. Flexibility, budget, possible integrations, etc., are all important considerations for the optimum data warehouse. 
Finally, build your data pipelines, or choose your ELT tool. Business teams need to be able to rely upon the data to make quick and informed decisions. The data flow and access need to be set up to support all teams in making the customer journey as seamless, personalized, and smooth as possible. Hence, when it comes to pipelines, crucial elements would be the pipelines’ reliability, speed, and accuracy.

Data Modeling and Transformations

Finally, an important step for the data to be unlocked for operational use is data transformations and modeling. Data needs to be present in the warehouse in a way that allows for quick and easy querying and accessible reporting. 

Data would need to be transformed into a suitable format to be stored and accessed within the warehouse. Transformations could include pre-load, before the data loading occurs, or post-load after the data has been loaded. Before data is loaded into the warehouse, these could include standardizing date formats or time zones, masking sensitive financial data, splitting the first and last names of customers, etc. These changes are done on a column level. 

Post-load transformations are done on a table level and could include denormalization, modeling, combining or separating tables to allow for more efficient querying, etc. 

Another challenge in the data for e-commerce is that the data is often present in both structured and unstructured formats, which can be difficult to manage. These formats need to be linked to one another for easy access. For example, a product like a dress needs to be linked to the product image of the dress, the product video of a model walking around wearing the dress, etc. 

For these reasons, e-commerce businesses may often use a dimensional model, like a star or mixed (star + snowflake) schema within the warehouse. The central dimension is often product data, while the various fact tables would include views upon this central dimension like customers, orders, etc. It is important to consider how to partition the data, as bad partitioning can lead to slow queries and high costs.

Else, an e-commerce business may decide to go for a relational model, Here, the crucial tables to be included remain similar. These include sales, payments, shipments, fulfillments, inventory, customers, traffic, advertising, etc.

Data Analytics in E-commerce

Many models can be built upon this data to unlock its maximum potential. Here are a few: 

  • Predictive analysis –  Predictive analytics can be used to stay ahead of the variations and help teams discover the best choice in any given situation based on past data instead of intuition.
    For example, market basket analysis is a data mining technique in which large datasets of past customer purchases are used to understand buying patterns, predict which products can be grouped together for effective marketing campaigns, etc. 
  • Customer Segmentation – Customer segmentation is one of the most valuable activities for an e-commerce business that truly cares about the customer experience, and performing analytics on a warehouse unlocks it to the next level. According to McKinsey, companies that have personal interactions with a large segment of customers observe “a one to two percent lift in total sales for grocery companies and an even higher lift for other retailers, typically by driving up loyalty and share-of-wallet among already-loyal customers.” Customers can be divided based on location, interests, demographics, history, and many other parameters, hence improving ROAS and other key marketing metrics.
  • Operational Optimization – Zulily is an e-commerce retailer that specializes in flash sales and daily deals. The company uses operational analytics to optimize its inventory management and supply chain operations. For example, Zulily uses predictive analytics to forecast demand and adjust inventory levels accordingly, reducing the risk of overstocking or understocking. A forward-thinking e-commerce business could take the vast amount of data at its disposal and use analytics to multiply its operational efficiency, identifying gaps and sealing them as needed.
  • Recommendation Systems – Certain advanced models can be built which can accurately predict customer behavior and needs based on previous data, providing a highly sophisticated recommendation system. This can shoot up audience interaction and engagement. Currently, around 35% of all purchases made on Amazon are based on product recommendations through such algorithms. 

For this to work, the more customer data for the model to be trained on, the better. This makes data integration into a centralized warehouse crucial, as the best place to build a recommendation system atop the entire data present within the business systems.

The Story of E-commerce Startup, Meesho

To understand the journey better, let’s consider the example of Meesho. Meesho is a Y Combinator-backed startup and is India’s #1 social commerce platform. 

Meesho was facing the typical struggles of multiple silos and heavy reliance on the data team for every report. After some research, they discovered that a robust modern data stack, with pipelines connecting all their sources to a centralized warehouse, could solve their issues. 

The option to build a custom ELT solution in-house was ruled out as it came with a lot of overheads. The team did not want to move the focus of engineering from core objectives. Meesho decided to look out for a modern ELT solution that would extract the data from siloed sources, transform them on the fly and move into Redshift. The required pipelines were set up within 10 days, setting the ball rolling immediately. With a front-end reporting tool (Metabase) all teams track their core metrics and create custom reports in real-time on the data now made available. 

Now, the data team focuses on generating extensive reports on customer and seller analytics. The freed-up bandwidth is used to focus on predictive analytics projects that can bring futuristic insights.

Wrapping It Up…

This blog gave an overview of the need for ELT in the e-commerce industry, the advantages provided by ELT solutions over building pipelines manually, the main considerations while creating a data stack for this industry, and the ways in which the integrated data can be used to perform different data analyses and provide a host of subsequent benefits. 

While data engineers in this industry have a lot of things to grapple with, solutions like Hevo Data provide an easier way out. Sign up on Hevo today to unlock a whole suite of benefits for your e-commerce business! 

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