Fivetran named 2024 Google Cloud Technology Partner of the Year
This marks an incredible three-peat win, solidifying our ongoing commitment to innovation and collaboration within the Google Cloud ecosystem.
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Fivetran
Fivetran by the numbers: A year of innovations
From new data sources to cloud regions and destinations, Fivetran continues to expand what it means to be the leader in data movement.
Everything you need to know about the Fivetran REST API
The Fivetran REST API makes it a breeze to automate the deployment, configuration and management of connectors, saving you time and effort.
Accelerate GenAI apps with Fivetran Google Cloud BQ and Vertex AI
Fivetran and Google Cloud synergize incredibly well, bringing generative AI to your fingertips.
How to A/B test product-led growth in B2B SaaS
We tested a product-led go-to-market motion last year. Here are the exciting results and learnings.
How to prepare for the EU AI Act
New legislation affirms the governance and security responsibilities of all organizations doing business in the EU.
Special Episode: Fivetran and Databricks CEOs reveal the secret to AI
George Fraser, CEO and co-founder of Fivetran, and Ali Ghodsi, CEO and co-founder of Databricks, are building products that power the modern data stack. They offer an insider’s perspective on the hardest parts of building and deploying generative AI in the enterprise.
Episode 5: Data democratization and readiness for AI | Powell Industries
The key to breaking down data silos and fostering innovation goes well beyond having the right technology. It’s the people and processes that truly drive change. Ajay Bidani, Data and Insights Manager at Powell Industries, shares his perspective on how a strong, inclusive data culture is fueling the manufacturer’s global success.
Building a chatbot with Fivetran and LangChain
Build your own generative AI chatbot! This post is a continuation of our earlier blog post, “Assembling a RAG architecture using Fivetran.”
New AI survey: Poor data quality leads to $406 million in losses
Underperforming AI programs/models built using low-quality or inaccurate data cost companies up to 6% of annual revenue on average.