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Spreadsheet Formulas and Pivot Tables That Do Real Work (Data Analyst Series, Part 4)
Two spreadsheet features do most of an analyst’s real work: conditional formulas like SUMIFS and pivot tables. Here is how to use both to answer a question in seconds instead of scrolling for an hour.
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How to Think in Data: Questions, Metrics and Hypotheses (Data Analyst Series, Part 3)
Analysis starts with a sharp question, not a spreadsheet. Here is how to turn a vague request into a clear question, the right metric, and a hypothesis the data can actually test.
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The Analyst Toolkit and a Skills Roadmap for Beginners (Data Analyst Series, Part 2)
The four tools a data analyst actually uses, spreadsheets, SQL, a BI tool, and Python, and the order to learn them in so you become employable in a few months.
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What a Data Analyst Really Does, vs Data Scientist and Data Engineer (Data Analyst Series, Part 1)
A plain, beginner-friendly breakdown of what a data analyst actually does day to day, how the job differs from data scientist and data engineer, and what the three roles pay.
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IBM watsonx vs the Field, the Cost Recap and the Verdict (IBM Gen AI Series, Part 24)
The final part of the IBM watsonx series puts the platform against Bedrock, Azure, and Vertex on the axes that decide a regulated purchase, recaps what a governed deployment really costs, and gives a clear verdict on where watsonx wins and where to walk away.
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watsonx Reference Architectures for RAG, Agentic, and Regulated Workloads (IBM Gen AI Series, Part 23)
The three watsonx reference architectures that recur in real builds: enterprise RAG on watsonx.data, agentic systems on watsonx Orchestrate, and a watsonx.governance overlay for regulated work. Which to build first, what each costs, and where they break.
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watsonx LLMOps on OpenShift, from Notebook to Promotion Gate (IBM Gen AI Series, Part 22)
How to run generative AI features in production on the IBM stack: watsonx.ai for models and governance, Red Hat OpenShift AI for pipelines and GPUs, and a promotion gate that refuses to ship a regression.
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EU AI Act Readiness and Responsible AI on watsonx (IBM Gen AI Series, Part 21)
The 2026 Digital Omnibus pushed high-risk deadlines to December 2027, but GPAI and transparency duties still bite this year. Here is how I classify a watsonx system and build the evidence pack before an auditor asks.
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watsonx Cost Governance and FinOps, from Resource Units to a Real Budget (IBM Gen AI Series, Part 20)
watsonx charges in Resource Units, capacity unit hours, and a flat instance fee. Here is how those meters add up, and the plan choice that keeps your generative AI bill honest.
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watsonx.governance Risk Monitoring and AI Factsheets (IBM Gen AI Series, Part 19)
How watsonx.governance tracks models, logs AI factsheets, monitors drift and bias in production, and maps controls to the EU AI Act. Part 19 of the IBM watsonx series.
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Docling and Multimodal Document Understanding on watsonx (IBM Gen AI Series, Part 18)
How IBM Docling and the Granite vision models turn PDFs, scans, and DOCX files into clean structured chunks for RAG on watsonx, and where a vision model is worth the cost.
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watsonx Model Evaluation and Benchmarking (IBM Gen AI Series, Part 17)
How to evaluate and benchmark models and prompts on IBM watsonx using watsonx.governance, Evaluation Studio, and the ibm-watsonx-gov SDK. Score faithfulness, answer relevance, and context relevance on your own golden set, not a public leaderboard.
Architect’s Toolkit
PJ’s Tools
VMware Cloud Foundation
- VCF Documentation
- VCF 9 Planning & Preparation Workbook
- VCF Bill of Materials (BoM)
- VMware Compatibility Guide
- VMware Interoperability Matrix
- VMware Configuration Maximums
- VMware Ports & Protocols
- VMware Hands-on Labs
- RVTools Download
Nutanix
AI & Cloud-Native Platform
- NVIDIA Build (Model Catalog)
- NVIDIA AI Enterprise Reference Architecture
- NVIDIA NIM Performance Benchmarking
- NVIDIA NGC Catalog
- NeMo Microservices Helm Chart
- Helm Charts Repository
- Hugging Face Models
Architecture & Design
About the Author
Dr Pranay Jha
Dr. Pranay Jha is a Cloud and AI Consultant with 18+ years of experience in hybrid cloud, virtualization, and enterprise infrastructure transformation. He specializes in VMware technologies, multi-cloud strategy, and Generative AI solutions. He holds a PhD in Computer Applications with research focused on Cloud and AI, has published multiple research papers, and has been a VMware vExpert since 2016 and a VMUG Community Leader.
Recommendations & Endorsements
Pranay has very deep technical knowledge and exceptional capabilities. He has strong ability to well-manage complex situations and deliver the customer expectations with high standards of satisfaction. We worked together in one of most complex migration projects and Pranay delivered flawlessly.
Pranay was my VMware Architect on one of our financial accounts at IBM. He was instrumental in documenting and recommending our reference architecture. As a Chief Architect, you are only as good as your team that supports you. Pranay was top notch.
Pranay is proactive, result oriented, responsible and technically sound. Always ready to put all his energy to get the job done. Exceptional troubleshooting skills in Windows Server, virtualization and VMware — a great asset to any company.
Pranay is one of the most talented and dedicated persons I have ever worked with. His vast knowledge from different domains enables him to discuss and help on almost every topic in IT. Needless to say, he is expert of experts in VMware.
DrJha