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phData is an AI and data services leader that builds intelligent systems for enterprises ready to turn AI ambition into working business advantage.
The Sigma Computing API wants a workbook ID. The ID in your workbook URL is the wrong one.
Use it anyway, and you get a 404 with no hint why. Rodrigo Finguer shows how to find the right one in the clip.
Why bother? Sigma's Workbooks-as-Code API (currently a private beta) exports a workbook as a YAML file, so Git can diff it, review it, and keep the history. "Who changed the revenue filter in March?" becomes a git log query.
Rodrigo wrote up the rest in the blog: the 4 GitHub secrets and 2 shell scripts, the regional API host your browser URL can't always tell you, and a scheduled sync that commits about every 30 minutes, but only when the workbook has changed.
He also covers where the beta falls short today. Write-back into Sigma only works for the simplest workbooks.
Full walkthrough: https://www.phdata.io/blog/sigma-api-to-git-integration/
If your company is struggling with agentic AI in the enterprise, Dominick Rocco has a theory on why.
Better prompts, embedded AI features, and the newest LLM model may improve individual work volume, but don’t necessarily improve the work. None of them touch the workflows where people are stuck moving the same information between the same systems all day.
That gap between "we use AI" and "AI changed how we work" is the difference between prompts and systems.
Dominick walks through it below. Full breakdown here: https://www.phdata.io/blog/agentic-ai-enterprise/
Meet Agnit Chatterjee, a Solutions Architect on our team based in India.
Read what keeps him challenged, the highlight he's most proud of, and the perk that's helped him grow the most.
Curious what it's like to build your career at phData? Check out open roles here: https://www.phdata.io/careers-india/
95% of companies can't show a return on their AI spend. The reason has nothing to do with the technology.
In this clip, phData's Vincent Yates, Dustin Dorsey, Rami Heera, and Dominick Rocco get to the root of that number.
The full conversation goes further. Why centralizing data isn't enough if you never centralize what it means, how "AI slop" spreads when companies skip building targeted agents, and how structured data pushed one team's agent accuracy from 21% to 95%+.
Catch the full video here: We Bought AI. Now What? Building an Enterprise AI Strategy That Works
09/23/2026
Claude Opus 5.5, released today, brings a meaningful shift in the economics of enterprise AI work.
We expect Opus 5.5 to reduce the cost per task by about 40% compared to Opus 5, while producing outputs about 30% faster. For the work that is moving from experimentation into day-to-day operations, like code changes that need review, research across long documents and spreadsheets, and agents handling multi-step tasks, the impact will be significant.
One capability worth watching closely is clearer communication. When an agent can show what it did, what it found, and where it needs help, teams have a better path to using it with confidence.
The question for enterprise leaders is simple. Which workflow is ready to test against this new capability, with the right measures of success?
phData helps organizations evaluate new models on enterprise-scale work and build the foundations for reliable production use.
Let’s build with Opus 5.5. 👉 https://www.phdata.io/partners/anthropic/
09/21/2026
Companies are deploying AI agents faster than they can answer a basic question.
Every new employee who touches enterprise systems gets scoped access, a tracked identity, and a manager who signs off. Most AI agents get none of that. They're moving into the same systems, making the same kinds of decisions, and nobody ran the onboarding ritual.
Adoption is outpacing governance, and most organizations don't have a single owner closing that gap.
Rami Heera, VP and Practice Lead at phData, breaks down why human-in-the-loop review isn't enough on its own, and what it takes to close it before it becomes an incident.
Read the full blog: https://www.phdata.io/blog/enterprise-ai-governance-agents-as-employees/
We're hiring!
phData is growing fast, and we need engineers, analysts, and advisors who go deep into the tech and deliver client results.
Open roles across Data Engineering, Applied AI, Analytics, Advisory, Elastic Operations, Engagement Leadership, and Sales, based in the US, Latin America, or India.
Ready to help build what's next in data and AI? Check out our open roles: https://www.phdata.io/careers/
09/17/2026
dbt Labs handed phData two wins this week.
Earlier this week, dbt named phData their 2026 Partner of the Year. Today, they named one of our own the Ambassador of the Year.
Congratulations to Bruno for the recognition. Ambassador of the Year goes to the people who show up for the dbt community again and again, answering questions, sharing what they've learned, and making other practitioners better at their jobs.
Two awards, one team. That's not a coincidence.
Read the full story on phData's dbt Partner of the Year win: https://www.phdata.io/blog/phdata-dbt-partner-of-the-year-2026/
Congratulate Bruno in the comments.
09/17/2026
Welcome to the team, Arturo Pena!
We are thrilled to announce that Arturo has joined phData as our new Chief Marketing Officer.
In his own words, Arturo joined a leadership team with "vision, deep expertise, and real urgency," and he's ready to bring that same energy to growing the phData brand.
Join us in congratulating Arturo on the new role!
phData just won dbt Labs Partner of the Year for the fourth consecutive year.
Dustin Dorsey, our Sr. Director of Data Engineering says, "This award isn't about one person. It's a team award. We wouldn't have it without the people doing the work for our customers every day, their technical excellence, their curiosity, and their commitment to outcomes."
Customers keep asking the same question: how do we build a data foundation solid enough for AI? dbt gives teams a consistent, tested, governed transformation layer, exactly the kind of foundation AI applications need to work from trusted data.
Huge thanks to Bruno Lima, Dustin Dorsey, Tejaswini Gaikwad, Carlos Rodríguez Leandro, Dakota Kelley, and the entire data engineering team, for the work that made this happen four years running.
Thank you to dbt Labs for the recognition and the partnership.
Read more about how phData builds AI-ready data foundations with dbt: https://www.phdata.io/blog/phdata-dbt-partner-of-the-year-2026/
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