How a Leading Apparel Brand Reduced Their Data Platform Costs by 83% Using Rivery, DBT Cloud, Snowflake, and Power BI
A leading apparel brand faced significant challenges in managing its data operations due to limitations with its previous data platform, Domo. Their data requirements were unique, with nested JSON objects from various APIs and high data volumes that needed to be efficiently processed for deeper insights.
Their team partnered with NuView Analytics a leading data analytics consulting firm to bring data to the forefront of business, creating an easy-to-use and understandable method for making decisions.
The prior platform could not fully integrate with these complex API structures, leading to incomplete data, high operational costs, and inefficient workflows. After extensive exploration, their team turned to Rivery to streamline its data operations, reduce costs, and improve data availability.
The Breaking Point: Domo’s Pricing Increased
The clothing brand’s experience with Domo presented several difficulties. Their primary pain point revolved around API integration. Domo was unable to effectively access critical data nested deep within JSON objects, limiting the company’s ability to retrieve and analyze key metrics. One significant example of this issue was in their Shopify data, where Domo failed to extract discount information from orders, as the data was nested too deeply. This limitation resulted in only 80-90% of the necessary data being accessible, which affected business insights and decision-making.
Additionally, Domo’s pricing model went from a platform fee plus a user fee to a platform fee plus a credit system for any data processing. This meant the whole ELT process was going through a consumption-based pricing model, which led to an unsustainable operational cost of $300,000 per year. The consumption rates, driven by “Magic ETL” processes, were higher than expected.
“Most of the work goes into the transformation and loading of data, and that is why the costs were so high since there was so much data processing going on.” – Stephen Kim, NuView Analytics.
Their data team also attempted to build custom connectors using Python, but the process was overly complex and required additional infrastructure investments, which were not viable in the long term.
Faced with these constraints, their data team recognized the need for a more customizable, cost-efficient, and scalable solution for data integration and management. They required a platform that would allow them to access all fields from their APIs, including those nested within complex structures, while also reducing overhead costs and simplifying data workflows.
Solution: Implementing modern data tools with Rivery as the core data integration layer
After evaluating several options, the data team at the apparel brand chose Rivery for its flexibility, powerful API capabilities, and the ability to provide full JSON responses that enabled deeper analysis of data. This choice was also driven by Rivery’s customer service, cost transparency, and overall ease of use, particularly in handling large volumes of data.
With the aid of NuView, the apparel brand implemented Rivery alongside a tech stack that included Snowflake for cloud data warehousing, dbt Cloud for transformations, and Power BI for visualizations. The switch to Rivery solved their API integration issues, providing access to 100% of the required data from Shopify and other sources. For example, they could now extract the complete set of discount data from Shopify orders that Domo had previously missed.
The Rivery platform also improved operational efficiency. Daily data loads, which had taken upwards of 30 minutes in Domo, were reduced to just 4 minutes with Rivery, thanks to its superior processing speed.
“In some cases inside DOMO, our processes wouldn’t finish until minutes before sending out our daily reports. With Rivery, by the time we open our laptops for the day data is done processing and ready to be sent out.” – Brandon Gaske, NuView Analytics.
This significant improvement, combined with Rivery’s ability to embed Python into integration processes without having to stand up and manage additional infrastructure, allowed their team to streamline its workflows and reduce overall complexity.
Furthermore, Rivery’s cost visibility tools provided a clear view of resource usage and allowed them to manage its costs more effectively. Compared to the $300,000 annual quote from Domo, the new stack with Rivery, dbt, Power BI, and Snowflake cost just a sixth of that amount.
Results: Improved Data Availability
The NuView Analytics team spearheaded the migration from Domo to Rivery for the apparel brand, focusing on transferring historical data to Snowflake and reconfiguring data processes to align with the new tech stack. As they transitioned from a GUI-driven ETL in Domo to SQL-based transformations in dbt, the team took the opportunity to redesign their data architecture, creating raw, staging, and analytics layers for improved data management.
“The biggest impact is confidence in the data we are working with. We are confident that the data is in a state where it can be used for analysis, which was hard to achieve in Domo.” – Brandon Graske, NuView Analytics
Within four months, the apparel brand completed the migration to all the data platforms and saw immediate benefits. Not only did they achieve 100% data availability, but their reporting accuracy and speed dramatically improved. Rivery’s standard REST river functionality allowed for full JSON responses, which resulted in a 10% increase in critical data availability for analysis. In terms of processing speed, specific cases showed an improvement of up to 75% compared to their prior solution.
Rivery’s customizable API integrations, efficient data processing capabilities, and transparent cost management helped the appeal brand transform its data operations. The switch allowed them to capture critical data that was previously inaccessible, reduce their processing times, and save on operational costs. With Rivery’s powerful platform, the appeal brand now enjoys improved data availability, faster reporting, and a scalable solution for future growth.
