Learn how to use BigQuery to analyze Gemini Enterprise usage logs, audit compliance, and build Looker dashboards at scale for your organization.

Last April FloQast, an American maker of accounting software, published something unusual: a detailed engineering post on Amazon Web Services’ machine-learning blog, co-authored with AWS personnel, explaining precisely how its AI-powered transaction-matching feature works under the hood. The post described cloud infrastructure, model selection, and the specific technique (generating matching rules from user-supplied examples) that powers its AutoRec product.

The fastest way to replace custom Python or PowerShell scripts for client data ingestion is to move each script's logic into a reusable, config-driven pipeline that stores connection details, field mappings, and schedules as metadata instead of code. This guide is for data integration managers and engineers who currently maintain a script per client or per source system. After following it, you'll have a repeatable pattern for onboarding new clients without writing a new script for each one.

One thing has become increasingly clear to me: the businesses that thrive in the AI era won't be the ones with the most data, they'll be the ones where every employee knows how to use it. The World Economic Forum's 2025 Future of Jobs Report names analytical thinking as the top core skill companies need today, and a 2024 Gartner survey found poor data literacy to be one of the top five obstacles to analytics success.

Amazon’s recent pivot on the Selling Partner API is an encouraging reminder that developer pushback can breach walled gardens.

Looking back on 2025, one idea anchors everything we set out to accomplish: make data accessible and trusted so organizations can generate better decisions for their business, their customers, and the world.