Systems | Development | Analytics | API | Testing

Like many companies, Fivetran has recently piloted a number of projects and internal tools using AI. AI offers the potential to augment or accelerate a huge range of day-to-day business tasks, including better understanding and prioritizing customer requests, surfacing trends across multiple business units, and automating customer communications. The key to delivering value using AI is to ensure that it has access to quality data and a data infrastructure capable of providing it.

As organizations shift from analytics to AI, the modern data stack breaks under tightly coupled, warehouse-centric architectures that limit flexibility and scale.

Most teams work across dozens of tools, and not all of them connect to their reporting workflows out of the box. There are always sources that fall outside the native integrations list: an internal tool your team built, a platform specific to your industry, or a piece of software that a vendor hasn’t prioritized supporting yet. When that data isn’t directly available, teams get it in however they can.

Analysts, data engineers, ML engineers, and data scientists don’t work the same way; they shouldn’t have to. Today’s data ecosystem includes more roles, more tools, and more specialized workflows than ever before. The days of limiting access to a single warehouse or lake — controlled by a small group of data engineers or analysts — are over.

Enterprises are pouring millions into agentic AI. New findings from Fivetran's “Agentic AI readiness index 2026” reveal why most won’t see the return.

Conversational analytics leveraging Fivetran, Jira, and Claude saves analytical effort and boosts productivity.

Data integration tools are designed to move and join data. But what they’re not designed to do is burn half their capacity cleaning up what arrives at the input. When a source exposes a schema built for application performance rather than analytics, the pipeline must compensate: Anything typed as a string because it was easier at build time gets cast into numbers or dates before a calculation can touch it. The difficult truth is this is cleanup and not value-added integration work.

It has never been faster or easier to translate between different SQL dialects so that you can use different query engines.

What the new policy means, what it doesn't, and why Open Data Infrastructure matters more than ever.

An open, unified data foundation is critical for reliably and cost-effectively supporting future data use cases, especially AI.