Unlocking Low-Code ELT with Cyclr

Updated on 25th March 2025 by Susanna Fagerholm

Data is the backbone of modern businesses, and how it moves between systems matters. Traditionally, “Extract, Transform, Load” (ETL) has been the dominant method for structuring and cleaning data before it reaches its destination. However, “Extract, Load, Transform” (ELT) has emerged as a powerful alternative, particularly in cloud-native environments, as it allows raw data to be loaded into storage first and transformed only when needed.

So let’s take a look at some of the low-code ELT functionalities that are readily available at your fingertips in Cyclr.

What is ELT?

ELT, or Extract, Load, Transform, is a data integration method where raw data is first extracted from a source, loaded into a storage system, and transformed later as needed. Unlike traditional ETL (Extract, Transform, Load), where data is cleaned and formatted before being loaded, ELT stores raw data first and applies transformations dynamically.

ELT is particularly useful when working with data lakes, analytics platforms, and cloud-based integrations. Instead of performing transformations upfront, businesses can store raw data and process it later, only when needed.

How Cyclr Enables Low-Code ELT

As an embedded integration platform (iPaaS), Cyclr offers several low-code functionalities and tools that facilitate ELT workflows. It allows users to handle data integration and transformation easily with low development overhead. 

Let’s break down how Cyclr facilitates each stage of the ELT pipeline.

Extract: Connecting Data Sources

The first step in ELT is extracting data from various applications, databases, and APIs. Cyclr provides a 600+ connector library, making it easy to pull data from different sources.

How Cyclr Handles Data Extraction:

• Pre-Built Connectors: with minimal configuration, users can extract data from popular platforms.
• Custom API Connectors: for niche applications, Cyclr enables the creation of custom API-based connectors.
• Scheduled & Event-Driven Extraction: Trigger data retrieval based on time intervals or real-time updates.

Load: Moving Data to its Destination

Once extracted, data needs to be loaded into its destination, for example, another application, a cloud storage system, or an internal database. 

Field Mapping in Cyclr

Cyclr’s field mapping allows users to define how extracted data is loaded into a target system. 

Options include:

• Previous Step Mapping: Pull data from earlier steps in the integration.
• Variable-Based Mapping: Use predefined variables to dynamically load data.
• Lookup Values: Retrieve values from destination applications (e.g., fetching customer IDs).
• User Configurable Fields: Allow end-users to define mappings.

With these mapping capabilities, Cyclr ensures that data is correctly structured before being passed to a target system, reducing errors and manual intervention.

Storage Connectors for Holding Data

For cases where data needs to be held before transformation, Cyclr provides storage connectors, including:

• Data Storage (Global & Cycle): Stores simple key/value pairs of data persistently
• Object Storage (Global & Cycle): Supports structured JSON data, ideal for complex data relationships.

These storage options allow businesses to hold extracted data before applying transformations, making Cyclr a powerful tool for ELT pipelines.

Transform: Data Processing & Manipulation

The final step in ELT is transformation – ensuring that data is in the correct format before being used by the target system. Cyclr provides multiple low-code transformation options via the use of Mergefields in mappings, as well as JavaScript-based scripting.

• Inline Script: for small data modifications, inline scripts allow direct transformations within field mappings.
• Step Script: for more advanced transformations, step scripts allow users to modify data before or after an integration step runs.
• Connector Script: for custom connectors, transformation logic can be embedded at the connector level, ensuring consistency across multiple integrations.
• Event Handlers: Cyclr allows event-driven transformations where data can be modified before sending or after receiving it.

With these transformation options, businesses can ensure that data is cleansed, enriched, and properly formatted before it is used in the destination system.

Use Case: Email Marketing Contacts to Google Sheets Reporting

To demonstrate the different ELT functionalities in Cyclr, we built a simple integration that utilizes some of these features. 

In this use case, we extract basic subscriber data from the email marketing platform Sentori and enrich it with more detailed contact information. Then add it into a Google Sheet report for further analysis and action. We are leveraging field mapping, mergefields and inline scripting, to transform raw data into a structured and actionable format.

Extracting Data: Retrieving Mailing List Subscribers from Sentori

First, let’s extract our data, to set this up, we need to set steps to pull the data that we want for our report.

We will use the following Methods from the Sentori Connector:

• Get Mailing List Subscribers – gives us the initial data we want to work with, but Sentori’s API only provides the ID and Email Address of each Contact here
• Get Contacts by ID – this gives us a Contact’s full data from their ID.

On the Get Mailing List step, in its step setup popup, we use the Lookup option to select the appropriate mailing list in our Sentori account.  

Loading Data: Enriching Subscriber Data with Additional Contact Details

Our integration can now retrieve the mailing list subscribers we want, so the next step is to enrich the data by obtaining additional details for each. 

This is achieved by passing the ID of each subscriber to another Sentori Method: “Get Contact by ID“. This step allows the integration to pull more granular information about each subscriber, including their title, first name, last name, and the timestamp indicating when they were first added to the system.

This intermediate loading step is critical in the ELT process because it ensures that the required raw data is up to date and available to use at a moment’s notice at any point down the integration. 

In traditional ETL systems, data transformation would need to occur before it’s loaded, but with Cyclr, data can be progressively enriched at different stages of the flow.

Transforming Data: Restructuring Raw Data to Actionable Insights

Now we are ready to Transform our data. To draw in the multiple pieces of data extracted from the previous steps for our report in Google Sheets, let’s use a combination of Mergefields and in-line scripting.

Since we are creating a new report in Google Sheets, we use the Google Sheets Connector’s Append Rows Method to write new rows of data into a specified Google Sheet. In the step setup, after the Spreadsheet ID and Sheet Name have been specified, we select what kind of data to append and in which columns.

Using the “Type a Value” option, we’ve added multiple Mergefields to combine values from previous steps into one.

For example, we can format the contacts’ names into a more readable format from the data loaded by the Get Contact by ID step. Utilizing Mergefields, we combined the following into a single column that represents the full name of the contact in the report:

• Title
• First Name
• Last Name

The “Type a Value” option also supports short scripts. So in addition to formatting names, you can use inline scripting to create a tailored format for your data.

In our report, we wanted to add a column that would let the team know a date when it would be appropriate to follow up with each new subscriber. By pulling in the Created Timestamp from Sentori, and applying a script that adds six months to the date, we can add a date in the report that lets the team know when a follow-up should take place.

The inline script used looks like this:

=new moment(`[Get Contact by ID: Created Timestamp]`).add(6, 'month').toISOString()

Once the integration is run, the extracted, loaded and transformed data ends up neatly in a spreadsheet, already formatted, structured, and optimized for use.

Conclusion

Cyclr’s low-code ELT capabilities streamline data workflows, making it easy to automate extraction, enrichment, transformation, and storage without requiring deep technical expertise.

In the above use case we have seen:

• Extraction: Data is pulled from Sentori using pre-built methods, ensuring seamless access to mailing list subscribers.
• Loading: Additional details are retrieved from Sentori and mapped dynamically to provide richer insights.
• Transformation: Mergefields and inline scripting are used to format names and calculate follow-up dates, as the data is appended into Google Sheets.

By adopting Cyclr’s low-code capabilities for ELT, businesses can automate complex data workflows while ensuring that information remains up-to-date, actionable, and structured for decision-making. This use case showcases how low-code ELT eliminates manual processing, enabling businesses to focus on using data effectively rather than managing its movement.

Whether you are using Mergefields to combine values, JavaScript for lightweight data processing, or leveraging conditional logic for mappings, Cyclr offers the tools you need to streamline your workflows.

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