The Diary
Writing on data, privacy & the craft.
A digital diary — a fusion of professional insight and personal narrative. Expert notes on data engineering, cloud, and privacy, with the occasional detour into books and life.
What I Learned at Databricks Data + AI Summit 2025: A Data Engineer's Perspective
A personal journal from four packed days in San Francisco — declarative pipelines, Lakeflow, Spark 4.0, zero-bus ingestion, and the mindset shift reshaping data engineering.
Read the postBuilding a Reading Habit: Mindset, Purpose, and the Journey
For anyone who has ever struggled to read consistently — practical lifestyle tweaks, mindset shifts, and simple metrics to make reading a fulfilling part of life.
Enforcing and Validating Tags While Creating Resources Using Azure Policy
A practical guide to using Azure Policy to enforce required tags, validate their values, and create exemptions — the backbone of clean cloud governance and billing.
Differential Privacy
A beginner-friendly walk through differential privacy — epsilon, privacy budgets, dataset sensitivity, the Laplace and exponential mechanisms, and how it plays out in real data publishing and analysis.
Our Initiative to Help Local Businesses Featured on Douglas Magazine
Our University of Victoria research initiative to support local businesses through COVID-19 recovery was featured in Douglas Magazine.
Blockchain Analytics
Blockchain and private distributed ledgers through the lens of privacy-preserving data science — analytics techniques, visualization, data modelling with BlockchainDB, and benchmarking Ethereum, Parity, and Hyperledger.
Data Scientist Nanodegree from Udacity
Reflecting on graduating from Udacity's advanced Data Scientist Nanodegree — a program co-created by Airbnb, Starbucks, IBM, and Figure Eight.
Executing Long Running Tasks in Google App Engine — How To Do It?
Running background tasks for hours or days on Google App Engine using Task Queues — and the production scaling gotcha that catches most developers off guard.
Data Lake — Why Should We Use One?
As data volumes exploded, storage became the question. A look at what a data lake is, where the term came from, and how it complements — not replaces — the data warehouse.
Data Science — Understanding the Concept and Why It Is Important
What does a data scientist actually do? A simple, candy-shop analogy for the four core tasks — cleaning, analysis, statistics, and engineering — that make data science matter.
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