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.

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.

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.

How SaaS data access restrictions can infringe on the freedom to use your data as you need it.

The future depends on a unified data architecture that can support everything from analytics and reporting to AI agents.