ABOUT
Hi, I am Naveen Vuppula
Senior Data Engineering Consultant based in Ontario, Canada.
I build data pipelines by day and teach data engineering, analytics, and AI/ML by night.
What I Do
I work with Azure Data Factory, Synapse Analytics, Databricks, Microsoft Fabric, PySpark, Python, and SQL every day — building production data pipelines, designing lakehouse architectures, and helping teams move from legacy systems to modern cloud platforms. I also explore AI/ML, data analytics, and data science — from model evaluation and feature engineering to fine-tuning LLMs.
Outside of my consulting work, I am also an indie app developer — I have built and published mobile apps using React Native, AWS Lambda, and DynamoDB.
Why I Started DriveDataScience
When I was learning data engineering and data science, I found plenty of documentation — but very few tutorials that explained why things work the way they do. Most guides showed you the steps without the context. “Create a linked service” — but why? “Use a metadata-driven approach” — but how does it actually fit together? “Apply feature engineering” — but which technique for which problem?
I started DriveDataScience to fill that gap. Every tutorial on this site comes from real project experience — broken down with real-life analogies so the concepts stick, complete code examples you can run immediately, and interview Q&As at the end so you are prepared for what comes next. The scope covers the full data stack — from engineering pipelines to analytics, data science, and AI/ML.
Learn · Build · Teach
I learn by building real things. I understand by teaching what I built. And when I teach, I learn even more. That cycle — learn, build, teach — is what drives every post on this blog. If I cannot explain something with a real-life analogy, I have not understood it well enough.
What You Will Find Here
170+ tutorials organized across 9 categories, covering data engineering, analytics, and AI/ML:
☁️ Azure — 37 posts on ADF, Synapse, ADLS, CI/CD, SCD pipelines
🔷 Microsoft Fabric — 38 posts on Lakehouse, Warehouse, Spark, KQL, DP-700
🧱 Databricks — 23 posts on Delta Lake, Unity Catalog, AutoLoader, DABs
🗃️ SQL — 15 posts from execution order to transactions and interview practice
🐍 Python — 31 posts covering foundations and intermediate series
⚡ PySpark — 10 posts on joins, window functions, UDFs, APIs, cleaning
🤖 AI/ML — 9 posts on algorithms, feature engineering, LLMs
🟠 AWS — 4 posts on S3, Glue, Lambda, Amplify
📖 Concepts — 7 posts on medallion architecture, file formats, interview prep
What Readers Are Saying
“I passed the DP-700 exam and drivedatascience.com was one of the resources I used for preparation. It has really detailed posts.”
— Reddit user on r/MicrosoftFabric
“Cleared DP-700 last week. drivedatascience.com — amazing blog posts. Some even cover more than what is in MS Learn.”
— Reddit user on r/MicrosoftFabric
Certifications
🎓 DP-900 — Azure Data Fundamentals (Microsoft Certified) ✅
🎓 AWS CCP — AWS Certified Cloud Practitioner ✅
🎓 DP-700 — Fabric Data Engineer Associate ✅
🎓 DP-750 — Azure Databricks Data Engineer Associate (in progress)
🎓 Databricks — Data Engineer Associate (in progress)
🎓 AI-103 — Azure AI Apps and Agents Developer Associate (in progress)
Tech Stack I Work With
Cloud: Azure (ADF, Synapse, Databricks, Fabric), AWS (S3, Glue, Lambda)
Languages: Python, SQL, PySpark, KQL, M (Power Query)
Data: Delta Lake, Parquet, Medallion Architecture, Star Schema
DevOps: GitHub Actions, Azure DevOps, CI/CD, ARM Templates, DABs
Apps: React Native, Expo, AWS SAM, DynamoDB, RevenueCat
Connect
Have a question about a tutorial? Want to suggest a topic? Found an error?
Connect with me on LinkedIn.