How are people discovering your Ecommerce business? What factors make a new buyer most likely to become a recurring customer? Why did a best-selling product on your Ecommerce website suddenly plunge in popularity? The answers to these questions and more can be found when you transform Ecommerce data to extract the insights hidden within.

Of course, to do so, you’ll need a powerful, robust, and production-ready ETL and data integration platform like The platform has been built from the ground up specifically for the needs of Ecommerce businesses, making it simple to get started with better data-driven decision-making.

From juggernauts like Amazon to tiny mom-and-pop stores, nearly every online retailer can benefit from transforming its Ecommerce data. In this article, we’ll discuss the benefits of data-driven Ecommerce, as well as how to use to transform Ecommerce data.

Related Reading: Getting Started with an E-Commerce Integration Platform

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Why Should You Transform Ecommerce Data?

Ecommerce businesses collect more customer data than they often know what to do with, and also more data than any human analyst could hope to examine by hand. However, Ecommerce big data contains tremendously valuable hidden insights for you to discover. Business intelligence and analytics programs can turn your information into dashboards and reports for key decision-makers to examine—but only once the data has been transformed and prepared for analysis.

Below are just a few benefits and use cases of transforming Ecommerce data:

  • Strengthening the customer experience: By understanding customers’ behavior as they navigate your Ecommerce website, you’ll be in a better position to strengthen the user experience. For example, you can analyze purchasing trends and behaviors to see which products are most popular at which times or to identify cross-selling and upselling opportunities. If you notice a worrying trend such as high cart abandonment rates, you can search through the data to see exactly where in the checkout process customers are getting stuck and bailing out.

  • Developing better products and services: Not only can Ecommerce retailers use big data to understand shoppers’ behavior and optimize their websites, but they can also use these insights to make better decisions about developing their products and services. For example, you can parse the text of customer reviews to understand what users like and don’t like about a product, or the features that they wish a product had.

  • Improving inventory management: Out-of-stock products are one of the biggest reasons why shoppers leave your Ecommerce website for a competitor. By tracking the ebb and flow of product stock levels, you can better predict when inventory will be running low and then send in an order, ahead of time to avoid running out.

  • Enhancing customer support: People who are happier with your Ecommerce business are less likely to need customer support, shortening wait times and improving outcomes. Data from your CRM (customer relationship management) platform can help agents keep track of customers’ open support tickets, enabling them to offer better, more targeted advice. Smarter use of customer data will help improve support metrics and KPIs (key performance indicators) such as call length time and resolution rate.

Related Reading: What Are the Benefits of Using Big Data in B2B Ecommerce?

Why is the Best ETL Tool to Transform Ecommerce Data? is a data warehouse integration platform with a single mission: to make it simple for Ecommerce businesses to get more value from their data. With, organizations can automate the ETL (extract, transform, load) process, moving their data into a centralized repository like a data warehouse for easier access. also supports ELT (extract, load, transform), a variant of ETL in which the data is loaded into the destination before being transformed.

There are dozens of ETL tools available for Ecommerce businesses—so what makes the best one? Below, we’ll go over some of’s most valuable features and functionality for Ecommerce businesses.

1. Rich set of connectors

Your choice of ETL tool must be able to naturally integrate into your existing Ecommerce workflow. comes with over 140 pre-built connectors and integrations for the most popular data sources and destinations.

The list of’s data connectors includes:

  • Ecommerce platforms like Shopify, Magento, and BigCommerce

  • CRM and ERP (enterprise resource planning) systems like Salesforce

  • Marketing and analytics tools like HubSpot and Marketo

  • Databases and data warehouses like Amazon Redshift

  • SaaS (software as a service) apps

  • Third-party websites and files

  • REST API (application programming interface) is constantly adding to our list of integrations based on user feedback—so if we don’t already support the connectors you need, there’s a good chance it will be added in the near future.

2. No-code interface

Ecommerce businesses usually need to get their hands dirty with technical issues, but they often don’t have a large in-house team of IT experts available. That’s why many Ecommerce companies need an ETL tool with a no-code or low-code user interface, making it easy to define and modify data pipelines on the go. offers exactly that. Our intuitive, no-code, drag-and-drop interface lets anyone get started linking your data sources with a destination such as a data warehouse. After making the appropriate connections, you can select from a wide variety of transformations so that your data arrives at the target location ready for analysis.

3. User-friendly pricing

There’s no shortage of ETL tools on the market, each one with its own pricing model. Some tools charge based on the amount of data you consume, others base their pricing on the number of connectors you use, and still, others charge an hourly rate. falls into the second category: users are charged based on how many integrations they have in their data pipelines (not the amount of data that flows through the pipelines). This makes an ideal choice for businesses with a stable data integration workflow, or for growing companies that plan to increase their data consumption in the future.

4. Advanced ETL functionality

There are many ETL tools that can transfer your data from one place to another—but far fewer that have a rich array of ETL functionality like For example, offers reverse ETL, which inverts the standard direction of ETL by sending information from your warehouse to a third-party application. This makes it easier to make your data actionable and accessible to a larger audience, rather than staying locked up in a repository to be analyzed only by specialists.

Related Reading: E-Commerce Reverse ETL

Another tremendously useful feature is our FlyData CDC (change data capture). CDC is an ETL feature in which users are notified in real-time when changes are made to a source table or database. These changes can then be promptly extracted and sent to the target location, keeping this centralized repository as up-to-date as possible. CDC helps ensure that your BI and analytics workflows are always operating with the freshest information while preventing you from wasting time by extracting information that has not changed.

Related Reading: CDC: Change Data Capture for E-Commerce

How to Start to Transform Ecommerce Data with

So far, we’ve gone over the benefits of transforming your Ecommerce data, and why is the best ETL tool for the job. With all that said, how can you get started transforming Ecommerce data with

Transforming your Ecommerce data with couldn’t be simpler. The website provides a Getting Started guide that outlines how to begin defining your first components and connections in our data integration platform.

To start, create a new data flow, and then click on the “Add Component” button in the user interface. From here, you can select from a wide range of data sources: file storage, databases, Amazon Redshift, Google BigQuery, MongoDB, Salesforce, REST APIs, and more. You can also define a destination repository such as Redshift, BigQuery, MongoDB, Snowflake, Salesforce, or other databases. Ecommerce Data Transformations

The heart of the platform is the various Ecommerce data transformations that users can perform on their data as it travels from source to destination. Before being loaded into a target repository, like a centralized data warehouse or data lake, information must be transformed and prepared for analysis. This ensures that your source data matches the target location’s schema and that it has been cleansed of redundant or out-of-date information.

Related Reading: Data Transformation: Explained

Below are just a few of the most frequently used transformations:

  • Select: The select transformation allows you to select the fields or records that will continue through your data pipeline, filtering out irrelevant information. For example, if you want to launch a campaign to lure back lapsed customers, you might select a list of users who have not made a purchase in the past 180 days, as well as their most recently purchased product and the date of purchase.

  • Sort: The sort transformation allows you to sort the input based on one or more fields in ascending or descending order. After selecting a list of users, for example, you might decide to sort the list in alphabetical order of their last name.

  • Limit: The limit transformation allows you to limit the number of records in the output of the transformation. For example, you might want to create a list of your company’s “whales,” i.e. the top 100 customers who have spent the most money on your website.

  • Sample: The sample transformation returns a percentage of random records from the given input. For example, you might use the sample transformation to generate a list of users who will be shown your new website redesign for A/B testing purposes.

  • Join: The join transformation combines records from two different inputs and treats them as a single output. For example, you can perform a join operation to combine users’ purchase history with their customer support interactions, helping you create a customer 360 view of your audience.

  • Union: The union transformation combines records from two inputs with the same data schema, essentially acting as a simpler version of the join operation. If you have customer records stored in two different databases, for example, then you can bring them together in a single union operation, without needing to modify the underlying schema or filter the input.

  • Clone: The clone transformation takes a single input and clones it, splitting the data flow in two so that you can apply different transformations along different paths. After extracting your complete list of customer records, for example, you might define one data flow for targeting users with a recent negative customer support experience, and another data flow for targeting users who recently abandoned their shopping cart.

  • Aggregate: The aggregate transformation allows you to group the input by fields and perform a number of functions that operate over aggregate data. For example, you can calculate the sum, average, count, minimum, maximum, variance, and standard deviation of a group of data records.

  • Distinct: The distinct transformation removes duplicate records that have the same value in all fields, ensuring that the output contains only unique records. For example, you might use the distinct operation to remove any duplicate accounts that users create on your Ecommerce website, ensuring that each record maps to a unique individual.

  • Assert: The assert operation is a sanity check on the given input, ensuring that it fulfills a given condition. If the input records do not meet the specified conditions, then the data integration job fails and the user is notified.

For the complete list of Ecommerce data transformations, check out the ETL Knowledge Base.

Performing transformations on your Ecommerce data as it travels from source to destination is crucial to ensure that your information is accurate, unique, and up-to-date and that it can fit the data schema of the target location. This process is also known as data cleansing. By automating the data cleansing and transformation process, Ecommerce companies can dramatically simplify their ETL workflows.

How Can Help with Transforming Ecommerce Data

Transforming your Ecommerce data is essential in order to turn it from raw bits and bytes into valuable, cutting-edge insights. is the real-time data integration solution that your Ecommerce business has been looking for. Thanks to, it’s never been easier to build production-ready data pipelines, extracting information from your data sources and migrating it to a centralized repository like a data warehouse or data lake.

With’s no-code, drag-and-drop interface, even non-technical users can get started building ETL workflows—no data engineers required. comes with more than 140 pre-built connectors to make the integration process as painless as possible.

In addition, is packed with highly useful functionality for the needs of Ecommerce companies. For example,’s FlyData CDC (change data capture) tool can automatically identify the data that has changed since your previous data integration job, so that you save time and effort by only extracting this information. Another powerful feature is’s reverse ETL capabilities, which allow you to transfer data out of a centralized data warehouse and into a third-party application for easier access and analysis.

Ready to learn how can help you make the most of your Ecommerce data? Get in touch with our team of Ecommerce data experts today for a chat about your needs and objectives.