For years, companies built their data architectures around a simple assumption: data existed primarily to support human decision-making through dashboards and reports. But today, data also powers operational workflows, machine learning systems, and, increasingly, AI agents that need timely, trustworthy data at scale.
Those new demands expose the limits of traditional data architecture. If companies want data infrastructure that is both reliable and cost-effective, they need a new organizing principle. That principle is Open Data Infrastructure.
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Why traditional architectures force a bad tradeoff
Older architectures typically made organizations choose between two imperfect options.
The key option for analytics was the data warehouse: structured, reliable, and optimized for analytics. The traditional data lake, on the other hand, was used whenever bulk and unstructured data were priorities: cheaper and more scalable for raw storage, but often harder to govern and less dependable for high-quality analytical use cases.
That tradeoff created a split between analytical and operational architectures. Teams extracted, loaded, and transformed data in one system for BI and reporting, then did the same in another system for large-scale storage, data science, or application support. Over time, this led to sprawling environments with multiple copies of the same data, fragmented governance, and growing operational complexity.
This cost problem is not just infrastructure spend, but also engineering time. Every duplicate pipeline, every handoff between systems, and every exception in governance creates more work to maintain, troubleshoot, and secure.
An additional issue arising from reliance on data warehouses for analytics is the tight bundling of storage, compute, and access. That makes data warehouses easy to adopt, but expensive to scale. When storage and compute are coupled, organizations can end up paying premium rates for workloads that do not require premium infrastructure.
Vendors have added another layer of difficulty. As data becomes more valuable for AI and automation, some providers are trying to protect their margins by monetizing access to customer data and making it harder to move data freely across tools. Such lock-in makes it harder to control costs, optimize performance, and adapt to new use cases.
The result is a system that gets more expensive and less efficient as it grows. Reliability starts to degrade under the weight of complexity, while infrastructure, engineering, and administrative costs grow. That is not sustainable for organizations that expect to rely more heavily on AI, automation, and real-time operations.
What Open Data Infrastructure changes
Open Data Infrastructure offers a cleaner path forward.
At its core, it combines the low-cost scalability of the data lake with the structure and reliability traditionally associated with the warehouse. Open table formats such as Apache Iceberg and Delta Lake bring important capabilities to lake-based architectures, including relational structure, schema enforcement, and ACID-style transactional reliability. That makes data in the lake far more usable and dependable for production analytics and AI workloads, while the data lake still retains its ability to handle unstructured data.
Just as importantly, Open Data Infrastructure decouples storage from compute. Organizations can store data once in low-cost, commodity object storage and then choose the best compute engine for each use case. That could mean one engine for BI, another for data science, and another for operational applications.
This flexibility improves both reliability and cost management.
Reliability improves because teams can build all data operations around a single data architecture with consistent structure, governance, and semantics rather than moving data across disconnected systems. Cost management improves because storage stays inexpensive, and compute can be selected based on performance and price for the specific job at hand.
Interoperability is the other essential piece. Open table formats are valuable not only because they improve lakehouse-style functionality, but because they reduce dependence on any single vendor. A data foundation built on open standards can support many downstream tools without forcing teams to duplicate data or pipelines. Fivetran’s own positioning around modern data lakes emphasizes this “move once, query as needed” approach and the value of vendor-neutral storage with downstream interoperability.
To fully realize that vision, vendors also need to cooperate. Customers should be able to access and use their data with minimal friction. Open Data Infrastructure works best when the surrounding ecosystem supports relatively unimpeded access, rather than putting proprietary barriers around storage, compute, or metadata choices.
When all the pieces come together, the result is a unified data architecture that is reliable because critical data is structured and governed, and cost-effective because it runs on scalable, commodity storage with flexible compute.
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Why this matters now — and even more in the future
No matter the industry, companies are increasingly using more data from more sources and supporting a wider range of analytical and operational use cases. They are also actively exploring how AI can enhance analytics, automate workflows, and create new ways of working. All of these use cases require large volumes of current, trustworthy data.
AI systems are especially demanding because they amplify both scale and consequence. They also create more automated downstream actions, which means data quality and availability matter even more. A brittle, high-cost architecture may not only block innovation but also amplify the negative consequences of AI gone awry.
That is why managing cost is about more than lowering the cloud bill. It is also about reducing the engineering and administrative burden required to keep the system running. The best architecture is not just cheaper to store and query. It is easier to operate, easier to govern, and easier to adapt.
Open Data Infrastructure is built for that reality. It is interoperable, flexible, and agnostic to the exact shape of the workload. Whether a team is supporting dashboards, data products, machine learning pipelines, or agentic AI systems, the same open foundation can scale to meet the need.
That is the real promise: not simply a cheaper or more reliable architecture, but one that delivers both at the same time.
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