Food & Agriculture Firm Accelerates API Integrations with Airbyte

Company Size

~20,000

Region

North America

Industry

Food & Agriculture

Sources

Carrier, Thermo King, Bureau of Labor/Transportation, Microsoft SQL, Various Fleet Management APIs

Destination

Snowflake

Tech Stack

• Source(s): Microsoft SQL, Various API sources (fleet management, logistics, time tracking)
• Destination(s): Snowflake
• ELT: Airbyte 
• Transformation: Dataiku
• BI: Power BI

Key Results

• Reduced API connector development time from weeks to days with Airbyte's Connector Builder
• Eliminated the need for additional development headcount despite managing 150+ software systems across the enterprise, by moving some of the key systems to Airbyte
• Accelerated time-to-value for critical purchasing and logistics analytics projects with 6-week implementation timeline
• Enabled a small team to support diverse, niche data sources without custom code maintenance burden
• Successfully onboarded within weeks of signing (July 31st contract, productive within 2 weeks)

About the Company

This U.S. food and agriculture company operates a complex enterprise with refrigerated transportation fleets, multiple production facilities, and sophisticated supply chain operations. With over 150 different software solutions deployed across the organization and a fleet of tractor trailers hauling temperature-sensitive products, the company requires robust data infrastructure to support analytics across purchasing, logistics, fleet management, and operations.

The company adopted a federated analytics model, with "player coaches" embedded throughout the business in sales, manufacturing, and live operations, teaching business users to build their own analytics and reporting using tools like Snowflake, Power BI, and Dataiku.

Legacy tool created API integration bottlenecks

Johnathan T. has been at the company for 19 years. He is currently the Data Services Manager and Solutions Architect, and has been building data capabilities at the company for 10 years. When the company brought in a new analytics director four years ago to modernize their approach, they initially chose Dataiku for data transformation and some ingestion work.

The company's analytics had historically been finance-focused and Excel-based, with limited self-service capabilities for operational teams. The new analytics stack (Snowflake, Power BI, and Dataiku) represented a significant evolution. But as adoption grew, the team quickly discovered there were limitations to their current technology stack.

Johnathan notes that there were significant issues with the ingest toolset they had been using. “The manual nature of its DAG-based pipeline development created significant overhead. Bringing in just 10 tables required dragging and dropping 10 different pipelines on screen, along with all the accompanying manual configuration.”

The breaking point came with the explosion of API-based data sources. As the company increasingly relied on SaaS vendors for critical business functions, Johnathan's small team faced a daunting challenge.

"We had more and more cases where the business is using SaaS vendors, and we need to get data out of those tools," Johnathan explains. "Building Python-based API ingest workflows is not something I wanted our team constantly having to build and maintain."

The team structure compounded the challenge. Johnathan's data services group consisted of just himself, one engineer focused on platform maintenance, one person handling new ingestion, and recently added consulting resources. When he started evaluating solutions, the offshore resources hadn't yet been added, and he knew the existing team couldn't handle both development and ongoing maintenance of custom API connectors.

Making matters worse, many of the company's data sources were highly specialized—including Carrier and Thermo King (providers of refrigeration units for the fleet), along with various fleet management, time tracking, and logistics systems. 

"We've got a ton of custom and niche SaaS providers," Johnathan notes. "That’s a challenge because most ingest tools won’t have pre-built connectors for those."

Reddit community and Connector Builder drove vendor selection

Johnathan's evaluation process was methodical and pragmatic. Rather than relying solely on traditional analyst reports, he turned to the engineering community for real-world perspectives.

After reviewing the Gartner Magic Quadrant to identify the usual suspects, Johnathan relied heavily on Reddit to understand what real data engineers were actually doing in the field. "I cross-referenced opinions from Reddit with Gartner, and Airbyte really came out of the Reddit community, with strong reviews."

The team evaluated several vendors, but one capability set Airbyte apart from all competitors: the Connector Builder.

Johnathan's philosophy on data engineering shaped his evaluation criteria. He draws a parallel to enterprise software evolution: decades ago, companies might have custom-coded their own purchase order systems, but now they tend to buy specialized SaaS tools to work more efficiently.

"Data engineering is going the same way. Why should we spend time building custom pipelines and writing code by hand when there's a tool that helps me do it faster?"

With limited development resources and numerous niche API sources to connect, Airbyte's Connector Builder solved the team’s most pressing challenge.

Connector Builder enables rapid development without code

The company signed with Airbyte on July 31st. Within two weeks, the team was already productive with the tool and starting to build connections.

The impact on development speed was immediate—development that would have taken a week or two could now be completed in a day or two. The timing proved crucial, as a major purchasing and logistics analytics project had just kicked off with tight timelines. The project involved a consulting company building a digital platform to help the business better understand spend and identify improvement opportunities.

Airbyte enabled the team to quickly integrate the diverse data sources needed:

• Carrier and Thermo King API data for refrigeration unit monitoring and temperature tracking
• Fleet maintenance data and associated costs for tractor trailers
• Capital asset cost information
• Fuel costs and logistics data
• Bureau of Labor and Bureau of Transportation statistics

The specialized nature of these sources—like getting temperature data from refrigeration units on trucks—exemplifies why the Connector Builder's flexibility proved so valuable.

Small team manages 150+ systems without expanding headcount

The Connector Builder's value extends beyond just speed. It fundamentally changed the team's capacity.

When asked if Airbyte enables them to achieve more with a smaller team, Johnathan is emphatic: "Yes, 100%, we can do more with less people. And the hardest part about hiring another employee is the amount of varying subject matter expertise we would need. Airbyte takes away that challenge."

With approximately 150 different software solutions deployed across the enterprise, the potential for API integrations is vast. While not all are API-based, many require connections to databases or other systems. Airbyte isn’t used for all of these systems, but it has been used to reduce the overall operational burden for the team. 

The Connector Builder proved especially valuable for these diverse sources. Even vendors advertising thousands of pre-built connectors don't have connections for the company's niche providers. And before Airbyte, Johnathan worried about the maintenance burden of custom code.

Writing authentication logic and pagination logic for 15 different endpoints from a single system meant either building custom libraries or repeating code—creating opportunities for mistakes and building what Johnathan calls "a maintenance nightmare."

The challenge is compounded by how different each API implementation can be. "From a distance, it all looks the same. You zoom in, and it's a whole different implementation."

Enabling federated analytics across the enterprise

The data infrastructure powered by Airbyte supports the company's federated analytics model. Player coaches partner with business users throughout the organization—in sales, manufacturing, and live operations—teaching them to build their own analysis and reporting using the analytics ecosystem.

This self-service approach means the data services team must be highly responsive to new data source requests from across the organization. With Airbyte, they can quickly evaluate and implement connections to new systems without becoming a bottleneck.

More insights, less maintenance

Today, the company's small data services team confidently supports a growing portfolio of data sources feeding into Snowflake, enabling analytics across purchasing, logistics, fleet management, operations, and other business domains. Where they once worried about becoming overwhelmed by custom code maintenance, they now have a scalable solution.

"That's what you're solving for us," Johnathan reflects. "Not having to have a massive team of developers and being able to connect to these sources with quick development speed." 

The team estimates that Airbyte has allowed them to avoid expanding their headcount by 1.5x and reduces development time by 75-80% for many API pipelines.

As the company continues to expand its analytics capabilities and onboard more business users to the self-service model, Airbyte's Connector Builder ensures the data services team can keep pace without expanding headcount, enabling them to focus on strategic initiatives rather than maintaining fragile custom pipelines.