Tredence has been recognized as Fivetran's Consulting Rising Star Partner of the Year for 2026, an award that honors partners demonstrating rapid growth and measurable impact within Fivetran’s global ecosystem.
For Tredence, this recognition reflects the momentum of a partnership built around solving one of the more persistent challenges in enterprise data programs: bridging the gap between data availability and data usability for analytics and AI.
"The Fivetran Rising Star Partner of the Year recognition celebrates partners who are setting a new benchmark for how enterprises operationalize data and AI. Tredence has consistently demonstrated the ability to extend the value of Fivetran’s automated data integration into end-to-end AI foundations and business outcomes, enabling enterprise clients to accelerate their data-driven transformation at scale." - Stephen Campbell, Senior Director of Global Channel Sales, Fivetran
Solving the Pipeline Problem That Quietly Stalls AI at Scale
Most data engineering teams in large organizations face a similar pattern: as cloud data platforms and AI initiatives scale, the number of upstream source systems grows with them. CRM platforms, ERP applications, operational databases, marketing tools, and customer-facing applications all need to be continuously synchronized and available downstream. Each connection requires the construction and continuous maintenance of pipelines as source systems change. In summary, the average enterprise is looking to make 400+ data sources AI available and ready in the shortest time possible.
Schema changes in upstream systems are a particularly common point of failure. When a SaaS vendor updates its data model or an internal application team refactors a table, the ingestion logic built against the previous structure breaks. Someone on the data engineering team has to track down the failure and restore correct data flow before anything downstream recovers. Multiplied across dozens of source systems in a large enterprise, that pattern consumes a significant share of data engineering capacity.
It crowds out the analytics and AI work the business is actually asking for, too.
Traditional reporting environments could tolerate some pipeline fragility. A dashboard that refreshed a few hours late was, at most, a scheduling inconvenience. Machine learning and generative AI applications are considerably less forgiving. A model scoring customer behavior on stale or incomplete data produces outputs that erode trust in AI-driven decisions, and eventually in the teams responsible for them. The data layer for reporting is often inadequate for AI at scale.
What Fivetran Changes at the Infrastructure Layer
Fivetran addresses the ingestion side of this problem through automation. The platform provides more than 700 pre-built connectors spanning enterprise applications, databases, SaaS platforms, and APIs. It automatically handles schema drift and pipeline upkeep as source systems evolve.
The practical effect is that data engineering teams can replace a substantial volume of custom-built ingestion pipelines with a managed layer. This layer absorbs the variability of enterprise source systems.
Custom pipelines are inherently brittle. They encode assumptions about how a source system behaves that become liabilities the moment that system changes. Fivetran's managed connectors are built to handle that variability. Schema changes that previously caused downstream failures are resolved automatically rather than escalated to an engineering queue.
This speeds deployment, but also decreases operational risk. Here, the managed connector layer absorbs what would otherwise be recurring break-fix work. As a result, engineering teams gain back capacity that was previously locked into infrastructure maintenance.
"Being named Fivetran’s Rising Star Partner of the Year is a strong validation of the impact we’re delivering together. As enterprises scale AI, the quality and reliability of the underlying data infrastructure becomes the factor that determines how fast they can actually move. By combining Fivetran’s automated data integration with Tredence’s data engineering and AI expertise, we’re helping organizations accelerate from fragmented data to production-scale outcomes." - Rakesh Sancheti, Chief Growth Officer, Tredence
Where Tredence Extends the Partnership
Automated ingestion is a necessary foundation, but enterprise analytics or AI requires more. Getting data into a cloud warehouse quickly and reliably solves the movement problem. It does not, on its own, solve the modeling, governance, or domain structure problems that determine whether that data is actually usable for the business cases running on top of it.
Tredence brings expertise and purpose-built accelerators to the layer that comes after ingestion. These include reusable ingestion frameworks designed to complement Fivetran pipelines. In addition, clients can leverage standardized analytics-ready data models, pre-built pipeline templates, and domain-specific data products for AI and advanced analytics use cases. In practice, these accelerators reduce the implementation work required to move from automated data ingestion to a production-ready analytics or AI platform.
That reduction is critical for large enterprises. Data platform implementations in complex environments tend to expand in scope and timeline at the transformation and modeling layer, once the ingestion problem is solved. That work requires both technical depth and domain context. Accelerators reduce the custom build required at that layer, while implementation teams bring the architecture expertise to make data governance and AI readiness achievable in practice.
The partnership addresses adjacent but distinct problems. Fivetran reduces the cost and engineering overhead of keeping enterprise data moving reliably. Tredence's accelerators and domain expertise reduce the cost of making that data fit for analytics and AI production workloads.
Together, they compress the path from raw enterprise data to outcomes that are in production.
When the Data Layer Becomes the AI Differentiator
Based on internal project benchmarks and enterprise deployments across multiple industries, organizations working with Tredence and Fivetran have reported meaningful acceleration across the data platform lifecycle.
Pipeline deployment has run three to five times faster compared to conventional approaches. Teams have seen pipeline maintenance effort drop by 70 to 80 percent as custom ingestion scripts are replaced by Fivetran's managed connector layer. Implementation timelines for modern data platforms have compressed by 50 to 60 percent.
The maintenance reduction, in particular, has a compounding effect. Engineering capacity freed from pipeline upkeep goes toward building the analytics capabilities and AI-ready data products that represent the actual return on a data platform investment. That reallocation tends to accelerate the broader data program, not only the infrastructure layer.
For organizations still managing pipeline complexity at the expense of AI progress, the path forward is cleaner than it may appear. The combination of automated data movement and implementation-complete data engineering expertise removes two of the most persistent friction points between data investment and business outcome. Tredence's data engineering practice is built to close that gap.
To learn more about how Tredence and Fivetran can accelerate your data platform modernization, connect with our team.
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Editorial Team
Tredence
