Category: Tech Notes
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Cloud Tagging and Cost Allocation That Actually Holds Up (Cloud FinOps Series, Part 5)
Tags are the primary key of your entire cost model, and they only work forward in time. Here is how to design a tag schema, enforce it at creation, and handle the spend that will never carry a tag.
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How to Read a Cloud Bill on AWS, Azure and GCP (Cloud FinOps Series, Part 4)
The invoice is a summary you cannot act on. The billing export is the real dataset, and it uses four different numbers all called cost. Here is how to read both on AWS, Azure and GCP.
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FinOps Framework Principles, Personas and Phases Explained (Cloud FinOps Series, Part 3)
The FinOps Framework is not a maturity checklist you work through once. It is a set of principles, personas, domains and a three phase loop you run continuously, and knowing which piece to reach for is most of the skill.
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Why Cloud Cost Behaves Differently From Every Other IT Budget (Cloud FinOps Series, Part 2)
Cloud cost is not a budget line, it is the output of thousands of small engineering decisions made by people who never see a bill. Here is the mental model that makes the rest of FinOps make sense.
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What FinOps Actually Is, and What It Is Not (Cloud FinOps Series, Part 1)
FinOps is not a cost cutting project and it is not a dashboard. Here is the actual definition, the three phases, the six principles, and the one thing to build first.
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Data Analyst Portfolio and Landing Your First Job (Data Analyst Series, Part 22)
A hiring panel gives your application about two minutes. Here is how to build three portfolio projects that survive that read, and how to run the job hunt itself like a pipeline you can diagnose.
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Ethics, Privacy and Bias in Data Analysis (Data Analyst Series, Part 21)
Removing names does not make a dataset anonymous, and a technically correct query can still produce a badly biased answer. Here is what an analyst has to check before publishing a number.
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Data Modeling Basics: Fact Tables, Dimensions and Grain (Data Analyst Series, Part 20)
Three people quoted three different revenue numbers for the same month, and none of them had made a mistake. This part covers fact tables, dimensions, grain and slowly changing dimensions, so your numbers stay consistent from one query to the next.
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Presenting Data Analysis to Stakeholders Without Losing the Room (Data Analyst Series, Part 19)
An analyst spent eleven of her fifteen minutes explaining how she cleaned the table, and her recommendation died in the room. This part covers the order, the one sentence and the appendix that get analysis acted on.
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A/B Testing and Experiments Explained Simply (Data Analyst Series, Part 18)
A team shipped a new checkout button, saw a 9 percent lift on day two, and rolled it out. Six weeks later the number was worse than before. This part shows how experiments actually work, and how to stop reading noise as a win.
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Metrics and KPIs: How to Define What Actually Matters (Data Analyst Series, Part 17)
Three dashboards, three different numbers for active users, and all three were right. This part shows you how to define a metric so it means one thing, and how to tell a real KPI from a number that just looks busy.
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Plotting in Python with Matplotlib for Data Analysts (Data Analyst Series, Part 16)
Turn a pandas summary into a chart. Draw a bar, a line and a scatter with Matplotlib, label them, and save them to share, using the same sales data from Part 15.
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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.
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