I share my learning journey into Data Science with my amazing LinkedIn friends, follow me and let's grow together!

For downloadable ML/ AI/ Data Science learning materials, please check my previous posts.

** Opinions are my own and not the views of my employer **

Alex Wang

Data Scientist

GenAI.Works

Driving the strategic vision for education at GenAI.works, working with teams to develop innovative AI-powered learning initiatives to empower professionals and align with the evolving needs of the workforce.

Head of Education Strategy

AI4Diversity

AI 4 Diversity (AI4D) is a nonprofit global initiative that engages and educates diverse communities about AI to benefit global society.

Member of Operation Team

Quantium

Global Market - ML Algorithms

Deloitte Australia

Business Algorithms

University of Technology Sydney

Courses: Advanced Machine Learning, ML Fundamentals

Casual Academic

Hackmakers

Pactera

Data Analyst

I share my learning journey into AI & Data Science here, click 'follow' and let's grow together!

For open access resources, productive tools, and learning materials in AI/ ML/ Data Science, please check my previous posts.

** Opinions are my own and not the views of my employer **

Member of Global Committee

It is voluntary role helping the global management team. 
AI 4 Diversity (AI4D) is a nonprofit global initiative that engages and educates diverse communities about AI to benefit global society.

AI and information-processing technologies are expected to transform 86% of businesses - more than any other category.

The World Economic Forum’s recent report shows how quickly things are shifting:
▪️By 2030, 39% of today’s core skills will be outdated, and 59% of workers will need reskilling.

▪️And alongside that, roughly 170 million jobs will be created, while 92 million will disappear.

It’s a lot of change happening at once - and AI sits right in the middle of it.

If you’re thinking about upskilling, Coursera is offering 40% off Coursera Plus Annual until 12/1 🔥

Check it out before ends
https://lnkd.in/gGuZTv7g

Here are some practical AI courses for professionals (all available on Coursera):

1----- AI for everyone from DeepLearning AI
 imp.i384100.net/Wy1zaO

2----- AI Essentials Specialization from Google
 https://lnkd.in/gGDFt3Uw

3----- Prompting Essentials Specialization from Google
 https://lnkd.in/gEWQeYSv

4----- Building with LLMs from DeepLearning AI
 https://lnkd.in/gh3qcVGY

5----- IBM AI Developer
 https://lnkd.in/g_bMxGVY

6----- GenAI Engineering from IBM
https://lnkd.in/g5xCkqj9

7----- Prompt Engineering Basics from IBM
https://lnkd.in/g-TxdUvV

Happy learning!
__________

For more on AI and learning materials, plz check my previous posts. 

I share my journey here. Join me and let's grow together. Alex Wang

#artificialintelligence #technology #machinelearning #datascience #business

Learn AI Together - I share my learning journey into AI & Data Science here, 90% buzzword-free. Follow me and let's grow together!

McKinsey's latest AI report 2025 - 
the gap between “using AI” and “getting value from AI” is bigger than most think.

A few highlights + some thoughts:

𝟭) 𝗔𝗜 𝗶𝘀 𝗲𝘃𝗲𝗿𝘆𝘄𝗵𝗲𝗿𝗲, 𝗯𝘂𝘁 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗶𝘀𝗻’𝘁

88% of organisations say they use AI somewhere…
…but nearly two-thirds are still stuck in pilots.
Only one-third have scaled AI across the business.

This matches what I see with teams: the tech is ready, but workflows, processes, and culture usually aren’t.

𝟮) 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝗿𝗶𝘀𝗶𝗻𝗴, 𝗯𝘂𝘁 𝘀𝘁𝗶𝗹𝗹 𝗲𝗮𝗿𝗹𝘆

62% are experimenting with agents.
Only 23% have reached any level of scaling.
And usually in just one or two functions.

Lots of buzz, but the reality on the ground is: most orgs are still figuring out what “agentic AI” actually means in practice.

𝟯) 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲 𝗶𝘀 𝘀𝗵𝗼𝘄𝗶𝗻𝗴 𝘂𝗽 𝗶𝗻 𝗽𝗼𝗰𝗸𝗲𝘁𝘀

Innovation ↑
Customer satisfaction ↑
Engineering & ops costs ↓

But only 39% report any EBIT impact, and most say it’s <5%.

𝟰) 𝗧𝗵𝗲 𝘁𝗼𝗽 𝟲% (𝗠𝗰𝗞𝗶𝗻𝘀𝗲𝘆 𝗰𝗮𝗹𝗹𝘀 𝘁𝗵𝗲𝗺 “𝗔𝗜 𝗵𝗶𝗴𝗵 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗲𝗿𝘀”) 𝗹𝗼𝗼𝗸 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲𝗹𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁

What separates them?
Not models. Not tools. Not vendors.

They:

- Set bold ambitions (not just efficiency, but growth + new business models)
- Redesign workflows end-to-end
- Have leaders deeply involved, not just sponsoring from a distance
- Invest meaningfully (often >20% of digital budget goes into AI)
- Scale agents 3–5× faster across functions

I’ve seen this too: tech matters, but ambition, workflow, and ownership matter more.

𝟱) 𝗧𝗵𝗲 𝗻𝗲𝘅𝘁 𝟭𝟮 𝗺𝗼𝗻𝘁𝗵𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗺𝗲𝘀𝘀𝘆

More orgs expect workforce shifts, both reductions and new hiring, especially for AI-adjacent skills like data engineering, MLOps, and product.

Generative AI adoption is now mainstream.
Agentic AI is on the rise.
But true enterprise transformation has barely started.

2023–24 was about experimenting.
2025–26 will be about rewiring.

Drop link in the post. 
__________

For more on AI and learning materials, plz check my previous posts.

I share my journey here. Join me and let's grow together. Alex Wang

#bussiness #technology #generativeai #aiagents

The state of AI in 2025

Google is on a roll. After Gemini 3, Google just dropped an AI idea that challenges scaling itself.

💡Overestimated or the real deal?

Let’s start with what it’s about (and why it might be one of Google’s most interesting theoretical advances in years):

▪️This paper challenges a core assumption in deep learning:
that stacking more layers and scaling models is the main path to intelligence.

▪️Instead, Google researchers propose Nested Learning - a paradigm where neural networks are treated as systems of nested optimization problems, each with its own memory, update frequency, and context flow.

They also introduce a prototype architecture called HOPE, built around these ideas to improve memory, continual learning, and adaptability.

It’s a rare paper that asks:
“How should models learn, not just how can they get bigger?”

🔗Btw, an awesome live workshop on Accelerating JSON Array Queries is coming soon; hands-on, free to join

Details here: https://lnkd.in/eQpFiD6E

*Hosted by SingleStore, could be helpful useful if you work with structured data. 
----------
✔️My take:

1) --- An exciting theoretical direction; super valuable for research, but not production-ready yet.

Nested Learning offers a clearer way to understand in-context learning and model adaptation - closer to how humans process and store knowledge.

But current results are on small-scale benchmarks.
We don’t yet know how it performs at real-world scale or under production demands.

2) --- A glimpse into the “post-Transformer” era

Multi-timescale memory, self-modifying components, optimizers as slow knowledge, these ideas may influence future architectures.

3) --- Is it worth watching? 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗲𝗹𝘆

It’s not a replacement for the LLMs we use today,
but it’s an early hint at what might come after scaling.

Image: Google page, Pete Reese
__________

For more on AI and learning materials, plz check my previous posts. 

I share my journey here. Join me and let's grow together. Alex Wang

#Technology #AI #DeepLearning #LLMs #Transformers

Building AI agents = 5% AI + 95% software engineering
Not exactly accurate, but you get the point.

We don’t really build AI agents - we architect systems, and let the AI sit inside them.

This diagram from Microsoft Azure shows what real, enterprise-ready AI infrastructure looks like.

It’s software architecture, system design, and reliability engineering:

✅ Identity management
✅ Document filtering (ACLs, redaction, PII masking)
✅ Schema mapping
✅ Human-in-the-loop escalation
✅ Infra that scales across both vector + SQL

Think of AI agents like APIs that reason, not magic.
They still need:

- Fine-grained access control
- Separation of structured vs unstructured knowledge
- Observability, fallback routing, and data traceability
- Flows connecting document pipelines with model orchestration

That’s why I find tools like Zapier interesting right now -
They’re quietly turning automation flows into orchestration layers for agents.
Connecting APIs, reasoning steps, and human review - all without code.

If you want to experiment with it yourself, try it here:
 🔗 https://lnkd.in/gKpEGnNG 

Before you tune the prompts, build the foundations.
__________

For more on AI and learning materials, plz check my previous posts.

I share my journey here. Join me and let's grow together. Alex Wang

#aiagents #agetnicai #llms #artificialintelligence

"Which AI role suits you?"

An interesting matrix, and I like the idea.

It might simplify reality a bit, but it’s directionally accurate and still useful as a reference to plan your learning path and get a quick sense of:
- What AI roles exist
- What core skills each role requires

If you're aiming for a specific role, this helps you understand what’s critical vs “nice to have.”

Here are a few thoughts I’d add, especially from a GenAI lens (understandably hard to cover all in one graph):

▪️In the matrix, LLMs, RAG, and Agents are grouped as one row -
but in practice, these are completely different skill sets (imo):

→ Prompting ≠ Retrieval-Augmented Generation ≠ Multi-Agent Orchestration
(And most teams today need all three)

▪️Worth noting a few other key skills that often come into play in production GenAI systems:
→ Evaluation and observability
→ Guardrails, tool-calling, tracing
→ Feedback loops, context management, orchestration logic

▪️Business roles are much more dimensional in practice -
For example, AI PMs and Ethics leads often need deep understanding of compliance, risk, model lifecycle… not just “soft skills.”

📍Also, there’s an awesome 2‑day AI camp happening this weekend, hosted by Outskill.
Upskilling‑oriented | 16 hours | free to join:
🔗 https://lnkd.in/gBuXuqJ7

Curious:
Does this reflect your current AI role?
Any skills you’d add or move?

Image: SuperDataScience
__________

For more on AI and learning materials, plz check my previous posts. 

I share my journey here. Join me and let's grow together. Alex Wang

#artificialintelligence #machinelearning #aiagents #generativeai

One of the clearest explanations I’ve seen on how transformers actually work.

LLMs like ChatGPT are built on this breakthrough architecture - the idea that unlocked modern AI.

This one’s by 𝟑𝐁𝐥𝐮𝐞𝟏𝐁𝐫𝐨𝐰𝐧, basically my best online “teacher” back when I was studying NLP at uni. Still one of the best free resources for understanding deep learning fundamentals.

(YouTube channel has the same name.)

Also, if you want a hands-on intro to how modern AI really works in practice, 𝗢𝘂𝘁𝘀𝗸𝗶𝗹𝗹 is running a live program this weekend.

Live session | Free to join
🔗https://lnkd.in/gdKQFYBk
__________

For more on AI and learning materials, plz check my previous posts. 

I share my journey here. Join me and let's grow together. Alex Wang

Open source is winning again.
𝗞𝗶𝗹𝗼 𝗖𝗼𝗱𝗲, built by the co-founder of GitLab, with a real engineering mind behind it.

Explore here: https://lnkd.in/gASAa6zq

Already the  #𝟭 𝗚𝗿𝗼𝗸 𝟭 𝗰𝗼𝗻𝘀𝘂𝗺𝗲𝗿 -
and growing fast: 20+ updates in the last 30 days, 400K+ downloads in under 6 months.

• 𝗕𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝘁𝗲𝗮𝗺𝘀
 Shared model access, persistent memory, consistent context

• 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁 𝘁𝗼 𝘁𝗵𝗲 𝗰𝗼𝗿𝗲
 Model-agnostic and fully self-hostable

Created in partnership with Kilo Code, a tool I’ve been meaning to explore.

Connect once. Use 9,000+ tools right in ChatGPT Atlas.

Every new AI tool means another integration to build and maintain.

With Apify MCP connected to OpenAI (ChatGPT Atlas), you get instant access to 8,000+ web scrapers, APIs, automations, and agents through a single connector with no endless integrations.

---- What you can do

🔹 𝗢𝗻𝗲 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗼𝗿 → thousands of actions (scrapers, APIs, automations, agents)

🔹 𝗪𝗼𝗿𝗸 𝘄𝗵𝗲𝗿𝗲 𝘆𝗼𝘂 𝗮𝗿𝗲 → trigger tools from any tab in Atlas

🔹 𝗖𝗵𝗮𝗶𝗻 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 → mix research, enrichment, search, and more

🔹 𝗥𝗲𝘀𝘂𝗹𝘁𝘀 𝗳𝗹𝗼𝘄 𝗯𝗮𝗰𝗸 𝗶𝗻𝘀𝘁𝗮𝗻𝘁𝗹𝘆 → into your doc, sheet, or dashboard

---- Real wins

• Research ops: scrape, clean, summarize, and drop into Sheets
• GTM & RevOps: on-demand company/contact enrichment
• Travel & e-com: live inventory/price checks with links to act
• Competitive watch: monitor pages and pipe insights to your workspace

One setup. Thousands of actions. No integration backlog.

𝗖𝗿𝗲𝗮𝘁𝗲 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗠𝗖𝗣 𝘀𝗲𝗿𝘃𝗲𝗿 → https://lnkd.in/gkGibMun

#mcp #generativeai #llms #artificialintelligence

No more prompts, just command - Rocket made it happen.

If you’ve ever built with AI, you know the cycle:
write a prompt → AI misinterprets → fix → retry → clarify → hope nothing breaks.

Half the time is 'babysitting' the model.

Rocket just launched something flips that:
Precision Mode — 100+ structured commands for building full apps.

I tested it this week. Here’s how it plays out:

𝟭) 𝗔𝗱𝗱 𝗽𝗮𝘆𝗺𝗲𝗻𝘁𝘀 (𝗦𝘁𝗿𝗶𝗽𝗲)

Goal: add a one-time checkout
Command: /Add Stripe Payments
Result: Rocket generated the full payment flow instantly, checkout screen, integration, test-ready.

𝟮) 𝗙𝗶𝘅 𝗺𝗼𝗯𝗶𝗹𝗲 𝗹𝗮𝘆𝗼𝘂𝘁

Goal: clean up spacing and responsiveness
Command: /Fix Layout Issues
Result: Everything snapped into place — aligned, device-responsive, production-ready.

𝟯) 𝗔𝗱𝗱 𝗮𝗻 𝗔𝗜 𝗰𝗵𝗮𝘁𝗯𝗼𝘁

Goal: embed an assistant powered by OpenAI or Anthropic
Command: /Add AI Chatbot
Result: Fully functional chatbot added, configured, and embedded, no manual setup.

𝟰) 𝗠𝗮𝗸𝗲 𝘁𝗵𝗲 𝗮𝗽𝗽 𝗺𝘂𝗹𝘁𝗶𝗹𝗶𝗻𝗴𝘂𝗮𝗹

Goal: prepare the entire app for multiple languages
Command: /Add Multiple Languages
Result: Rocket structured the project for full localization across screens, instantly.

It’s fast, predictable, and removes the ambiguity.

📍Test it yourself for free here https://lnkd.in/gPc_rRWu

𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗰𝗼𝗺𝗺𝗮𝗻𝗱𝘀 > 𝗴𝘂𝗲𝘀𝘀-𝗯𝗮𝘀𝗲𝗱 𝗽𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴
This might be what the next chapter of app development looks like.

Stop making PowerPoints - your next deck can start with a Word doc!

Plus AI ‘s latest AI feature takes your existing docs and turns them into well-formatted, professional presentations - automatically.

You can:
- Select from dozens of professionally-designed templates
- Create advanced slide types with native charts, tables, and case studies
- Translate existing presentations into any language
- Best part: Plus AI creates actual PowerPoint files

It’s easy to get started, and you can try it for FREE here: https://lnkd.in/gj54VYuM 

#aitools #productivity #bussiness #generativeai

SOC 2 is one of the most underestimated revenue blockers for teams.

It hits every company:
If your SOC 2 isn’t ready, your product demo doesn’t matter — the deal stalls right there.

If you want to clear that bottleneck fast, Delve is running a Thanksgiving offer this week:

$𝟭,𝟱𝟬𝟬 𝗼𝗳𝗳 𝗦𝗢𝗖 𝟮 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 + 𝗮 $𝟱𝟬𝟬 𝗩𝗶𝘀𝗮 𝗴𝗶𝗳𝘁 𝗰𝗮𝗿𝗱

💡How they help:

SOC 2 is normally a time sink.
Evidence buried across tools, endless spreadsheets, manual risk checks… everything slows down.

Delve’s AI agents take over the grunt work — pulling evidence, mapping controls, running checks, prepping your audit — so teams can go from “blocked” to “compliant” in days.

🔥Some recent wins:

▪️Lovable → SOC 2 in 20 hours

▪️WisprFlow → Closed Mercury + Superhuman in 2 weeks

▪️11x → $1.2M ARR unlocked

If SOC 2 is slowing your pipeline, this is a good week to get it done — quickly, cleanly, and without losing your sanity.

📍Link in the comments.
Code: 𝗧𝗛𝗔𝗡𝗞𝗦𝗚𝗜𝗩𝗜𝗡𝗚, offer ends 11/27 at 11:59 PM PST.

This movie-quality video started with just six images.
Everything in between, the motion, lighting, and movements…was generated by AI.

Tried Freepik recently and it was smoother than expected.

The flow is simple but surprisingly flexible:
 → Pick key moments as stills
 → Let the AI build transitions — movement, mood, pacing
 → Fine-tune with tools like 𝗘𝘅𝗽𝗮𝗻𝗱, 𝗥𝗲𝘁𝗼𝘂𝗰𝗵, and 𝗨𝗽𝘀𝗰𝗮𝗹𝗲

What stood out most: you still have a high level of creative control.
It’s not a black box: you guide the direction, and the AI fills in the story.

This rabbit scene went from a simple concept to a full cinematic mini-sequence in minutes.

If you’re building visuals for content, ads, or quick storytelling, this kind of workflow really speeds things up - without sacrificing quality or vision.

📍Create smarter with Freepik https://lnkd.in/g-73QFgz

#aitools #productivity #generativeai

Wavicle Data Solutions

Lead Data Engineer

LinkedIn

LinkedIn Learning Instructor

Globant

Senior Data Engineer

CRISIL Limited

Senior Analyst - Data Engineer

Quantiphi

Data Engineer

Tata Consultancy Services

System Engineer

Assistant System Engineer

Assistant System Engineer - Trainee

InsideSherpa

KPMG Data Analytics Consulting Virtual Internship

Monisha Enterprises

•	Data mining and Extraction Transformation Loading using R programming.
•	Data cleaning and processing for Data analysis done using Python using NumPy, pandas, SciPy, Scikit. Data modelling and prediction was done using algorithms – Logistic Regression, Time Series Forecasting, Clustering and Classification.
•	Data Visualization done using Tableau for generating reports to enhance the productivity of the products.
•	Built a Data Warehouse on MSSQL Server for ERP System of the enterprise to organize the data for future processing.

Data Analyst Intern

Open to Collaborate | Storyteller | Lead Data Engineer@Wavicle| Linkedin Top Voice 2024 | Globant | Linkedin Learning Instructor | 2xGCP & AWS Certified | LICAP'2022

Data Engineer with 7+ years of experience working on data engineering, analysis, and modelling projects for various domains, such as banking, healthcare, sales, and e-commerce. I leverage my strong statistical and analytical ability to explore business ideas and provide innovative solutions. 

Skilled with technologies and strong intellect with enthusiasm as an avid learner with challenging opportunities. I’m working as a LinkedIn Learning Instructor to bring change as a data evangelist.

Post Graduated in Master of Technology in Data Science and Engineering from Birla Institute of Technology and Science, Pilani in 2023, where I learned advanced concepts and techniques in data science and engineering, machine learning, cloud computing, and visualization. 

I hold multiple certifications in GCP, AWS, and Azure, and I enjoy working with cloud technologies and big data platforms.

I am passionate about data science and women empowerment, and I have been recognized as the 
- Linkedin Top Voice 2024 
- Data Science and Women Empowerment Thought Leader by Global AI Hub. 

I am also a proud participant of the LinkedIn Creator Accelerator Program 2022, where I share my insights and experiences with the data science community. 

I am open to collaborate and share ideas, or explore various opportunities in the data field.

Pooja Jain

When everyone talks about data… but only one person actually fixes it. 

👉 It’s always the Data Engineer who climbs into the pit and makes the system work.

If you’re starting your journey as that person — the quiet builder behind every AI success — here’s a structured reading list and GitHub resources to build rock-solid foundations.

1. 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀
 • Tools: Excel, Power BI, SQL, Python
 • Focus: Automate reports, clean data, build dashboards
 • AI Boost: Use Copilot or ChatGPT to write Python scripts, generate SQL queries, and debug faster

2. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺
 • Tools: Pandas, dbt, Git, Jupyter
 • Focus: Write modular code, version control, transform data
 • AI Boost: Let AI help refactor messy code, explain Git workflows, and generate dbt models

3. 𝗗𝗮𝘁𝗮 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 
 • Tools: Airflow, Prefect, dbt, Databricks
 • Focus: Build ETL pipelines, schedule jobs, test transformations
 • AI Boost: Use AI to design pipeline architecture, write DAGs, and troubleshoot errors

4. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗖𝗼𝗿𝗲
 • Tools: AWS/GCP/Azure, Spark, Kafka
 • Focus: Scale workflows, handle big data, deploy in cloud 
 • AI Boost: Use AI to generate infrastructure-as-code, optimize Spark jobs, and simulate Kafka streams

5. 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 & 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀
 • Tools: GitHub, Streamlit, FastAPI, LinkedIn
 • Focus: Build end-to-end projects, document, share, and network
 • AI Boost: Generate project ideas, write documentation, and create interactive dashboards

But Will AI Replace Data Engineers❓
It’s a common fear. But here’s the truth:
AI won't take your job; 
it will just automate the boring parts, raising the floor for everyone else.

Explore these data engineering projects to upskill and level up-

Beginner:
ETL pipeline using Python and SQL by Ankit Bansal:
- https://lnkd.in/gTdCV9aJ
- https://lnkd.in/gxEYM3Bb

Intermediate:
- Data warehouse solution using Snowflake, dbt by Shashank Mishra 🇮🇳 : https://lnkd.in/gf-c5TR7
- Mr. K Talks Tech : https://lnkd.in/gt9JAkRt
- Snowflake project by Data Engineering Simplified : https://lnkd.in/gXRWyHpc
- Apache Spark project by Ankur Ranjan: https://lnkd.in/gt9JAkRt

Advanced: Implement a real-time streaming data processing
- Darshil Parmar : https://lnkd.in/ghiEsa7P
- Yusuf Ganiyu : https://lnkd.in/giYwJaCS

Cloud Projects:
- Microsoft Azure by Data Engineering Simplified - https://lnkd.in/gx3aqzKU
- Microsoft Azure by Sumit Mittal - https://lnkd.in/dX2yma5b
- Amazon Web Services (AWS) by Darshil Parmar - https://lnkd.in/gJg7KV-7
- Google Cloud by Anjan - https://lnkd.in/gjHbmCaM

System Design concepts with:
- Alex Xu - https://lnkd.in/gGdgJRDd
- Design Gurus - https://lnkd.in/gaphzp89

DataExpert.io handbook compiled by Zach Wilson -https://lnkd.in/gb4xBQJy
🔥 𝗔𝗜 𝘄𝗼𝗻'𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀—𝗶𝘁 𝘄𝗶𝗹𝗹 𝗺𝗮𝗸𝗲 𝘁𝗵𝗲 𝗴𝗼𝗼𝗱 𝗼𝗻𝗲𝘀 𝘂𝗻𝘀𝘁𝗼𝗽𝗽𝗮𝗯𝗹𝗲 𝗮𝗻𝗱 𝗹𝗲𝗮𝘃𝗲 𝘁𝗵𝗲 𝗿𝗲𝘀𝘁 𝗯𝗲𝗵𝗶𝗻𝗱.

From 'What does that even mean?' to 'I got this' in 30 terms!

Data Engineering isn't about tools; it's about understanding the language. 

Master these concepts, and you’ll see the entire system relatable, not just the pipeline parts.

𝗗𝗔𝗧𝗔 𝗠𝗢𝗩𝗘𝗠𝗘𝗡𝗧
→ ETL vs ELT: Cook before serving vs serve then cook. ETL transforms first, ELT loads raw and transforms later.
→ Stream vs Batch: Live TV (process as it arrives) vs binge-watching (scheduled chunks).
→ Data Pipeline: Automated highway moving data from source to destination.

𝗗𝗔𝗧𝗔 𝗦𝗧𝗢𝗥𝗔𝗚𝗘
→ Data Warehouse: Organized filing cabinet—structured, optimized for analytics (Snowflake, BigQuery).
→ Data Lake: Your attic—stores raw everything: CSVs, logs, videos. Cheap but messy.
→ Data Lakehouse: Lake affordability + warehouse reliability (Delta Lake, Iceberg).

𝗗𝗔𝗧𝗔 𝗦𝗧𝗥𝗨𝗖𝗧𝗨𝗥𝗘
→ Star Schema: Central fact table + dimension tables. Simple, fast.
→ Snowflake Schema: Normalized star schema—saves storage, complicates joins.
→ Data Modeling: Designing table relationships so queries don't become nightmares.

𝗗𝗔𝗧𝗔 𝗣𝗥𝗢𝗖𝗘𝗦𝗦𝗜𝗡𝗚
→ OLTP: Real-time transactions (your bank app).
→ OLAP: Analytics on millions of rows (your dashboard).
→ Partitioning: Slice tables by date/region so queries don't scan everything.
→ Sharding: Split data across servers—multiple checkout lanes.

𝗗𝗔𝗧𝗔 𝗗𝗜𝗦𝗖𝗢𝗩𝗘𝗥𝗬
→ Data Catalog: Library index—find datasets, understand metadata.
→ Data Lineage: Family tree—track where data came from and where it's going.
→ Indexing: Shortcuts so databases don't scan every row.

𝗤𝗨𝗔𝗟𝗜𝗧𝗬 & 𝗚𝗢𝗩𝗘𝗥𝗡𝗔𝗡𝗖𝗘
→ Data Quality: Accurate, complete, consistent—garbage in = garbage out.
→ Data Governance: Access rules, security, compliance.
→ Caching: Store frequent data in fast memory.

𝗦𝗬𝗦𝗧𝗘𝗠 𝗗𝗘𝗦𝗜𝗚𝗡
→ Distributed Systems: Multiple machines working as one.
→ Message Queue: Buffer for async communication (Kafka, RabbitMQ).
→ Orchestration: Workflow conductor (Airflow, Prefect).
→ Fault Tolerance: Keep running when parts fail.
→ Scalability: Grow without rewriting everything.

💡 Reality Check: Tools change yearly. These concepts? Forever.

Whether you're a DE, analyst, or ML engineer—mastering these puts you ahead of 90% of the field.

Image Credits: Shalini Goyal

Mastering these fundamentals is your true skill. 
Tools change, but the need for reliable, scalable, and governed data pipelines stays forever.
#data #engineering

🏗️ Confused by Data Lake vs Warehouse vs Lakehouse vs Mesh?

Selecting the right architecture is game-changing for scalable, trustworthy analytics.

1️⃣ Data Lake
Think of a natural lake — it holds everything: clean water, mud, fish.
Similarly, a Data Lake stores raw data in any format. Flexible, but you’ll need to clean it before use.

2️⃣ Data Warehouse
Imagine a warehouse — everything is neatly packed and labeled.
A Data Warehouse stores structured, cleaned data ready for dashboards and reports. Reliable, but less flexible.

3️⃣ Data Lakehouse
Picture a house by the lake — you get the beauty of the lake and the comfort of a home.
A Data Lakehouse combines raw + structured data. Best of both worlds!

4️⃣ Data Mesh
Think of a network of homes — each family manages its own space.
Data Mesh decentralizes data ownership. Teams treat data as a product and manage it themselves.

🎯 THE SIMPLE TRUTH:
→ Starting out? Use a Data Warehouse (BigQuery, Snowflake, Redshift)
→ Need cheap storage for logs/raw data? Add a Data Lake
→ Want both? Modern Lakehouses (Databricks, Iceberg) are solid
→ Data Mesh? Don't even think about it until you're at massive scale

🎓 Major Takeaways from my experience:
→ Architectures must be aligned with specific project needs for optimal results.
→ Hybrid models like Lakehouse unlock flexibility and performance.
→ Evolving architectures foster innovation and reduce bottlenecks in data pipelines.

Embedding flexible, forward-thinking architecture is vital for data-driven success. 

Companies leading these transformations are able to pace up by 3-5x reducing infrastructure costs by 40-60% through right-sized, purpose-built data systems delivering insights.

Here are some resources to understand more about each of them -
→Designing Data-Intensive Applications by Martin Kleppmann - https://lnkd.in/drm6wQvH
→The Data Warehouse Toolkit by Ralph Kimball - https://lnkd.in/daWQwZ7Q
→Databricks Lakehouse Fundamentals (Free Course) - https://lnkd.in/gQEA-guy
→Google’s Data Mesh Playbook - https://lnkd.in/gumDAdfk

𝘈𝘳𝘤𝘩𝘪𝘵𝘦𝘤𝘵𝘶𝘳𝘦 𝘪𝘴 𝘵𝘩𝘦 𝘣𝘢𝘤𝘬𝘣𝘰𝘯𝘦 𝘰𝘧 𝘪𝘯𝘯𝘰𝘷𝘢𝘵𝘪𝘰𝘯 𝘪𝘯 𝘥𝘢𝘵𝘢 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨.

73% of data pipeline failures happen because of dependency issues, not bad code.
Cron jobs don't care if yesterday's data never arrived. They'll happily run on empty tables and produce garbage.

Airflow? It checks, retries, alerts, and refuses to cascade your failure downstream.

→ Beyond scheduling Airflow gives an important feature of 𝗗𝗔𝗚𝘀(𝗗𝗶𝗿𝗲𝗰𝘁 𝗔𝗰𝘆𝗰𝗹𝗶𝗰 𝗚𝗿𝗮𝗽𝗵𝘀), that helps to monitor, retry and gives flexibility to organize pipelines with minimum/less code.

🤔 Data Engineers often misinterpret Airflow just as an orchestration tool.

Apache Airflow is an open-source platform designed to programmatically author, schedule, retry and monitor workflows. 

🎼Think of it as the "conductor of an orchestra", where each musician (your data tasks, like data extraction, transformation, loading, or model training) knows when to play, what to play, and what happens if they make a mistake.

Most data engineers automate the wrong things at the wrong time.

Here's the framework I use after 8 years of building production systems:
✅ AUTOMATE WHEN: 
→ Task runs daily/weekly 
→ Human errors cause outages 
→ Work blocks other priorities
→ Team growth = more manual work
Examples: Reports, schema checks, alerts

❌ DON'T AUTOMATE WHEN: 
→ Task happens quarterly 
→ Requirements change weekly 
→ Process isn't understood yet 
→ Manual steps reveal insights

My rule: If it’s done 3+ times, script it; 10+ times, automate it; fails 5+ times, redesign it.
Automate what matters, when it matters—not everything!

Airflow isn’t just about running tasks.

🕑 It’s about building trustworthy, scalable, and observable pipelines that run reliably at 2am — even when you’re not watching.

Airflow gives structure to your chaos.
DAGs give clarity to your pipelines. 
That’s why DEs love them ❤️

Real Example: 
Instead of: 
❌ Cron jobs that fail silently 
❌ Manual dependency management
❌ No visibility into failures
You get: 
✅ Visual workflow monitoring 
✅ Automatic failure notifications
✅ Smart task scheduling 
✅ Easy debugging & restarting

Here's an amazing Airflow cheatsheet with some fundamentals concepts and interview questions to prepare for data engineers. 👇

Carousel Credits -  Futurense.com

Willing to learn more, here's the link to Astronomer free learning path - https://lnkd.in/g8-EWBda

Also, level up with Marc Lamberti's Udemy course on Airflow 3 - https://lnkd.in/gwArwaME

Airflow

Success and failure are separated by one decision: 
The choice to keep moving when everything says stop.

Agree?

Data governance is not the security guard saying “no”.

It is more like traffic lights in a busy city – a few rules that let everyone move faster and safely at scale.

Behind every confident decision is invisible governance doing the heavy lifting.
You know that feeling when you pull a report and immediately trust the numbers? 

When you can find the exact data you need in seconds? 
When your dashboard doesn't contradict itself?
That's not luck. That's governance.

🚫 The Misconception vs. ✅ The Reality

What People Think Data Governance Is:
 ❌ The "Department of No" that blocks data access
 ❌ Bureaucratic red tape that kills innovation
 ❌ A 500-page policy document gathering digital dust
 ❌ IT's way of controlling everything

What Data Governance Actually Is:
 ✅ The GPS system for your organization's data
 ✅ The quality control that makes "yes" possible
 ✅ The shared language that stops data arguments
 ✅ The trust layer that turns data into decisions

As a data engineer, you're not building barriers. You're building trust. 
You're not slowing things down. You're preventing the slowdowns that come from:
→ Wrong data in production
→ Compliance violations
→ Metric arguments
→ Security incidents
→ Data disasters

𝘎𝘰𝘰𝘥 𝘨𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘪𝘴 𝘪𝘯𝘷𝘪𝘴𝘪𝘣𝘭𝘦. 𝘉𝘢𝘥 𝘨𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘪𝘴 𝘪𝘮𝘱𝘰𝘴𝘴𝘪𝘣𝘭𝘦 𝘵𝘰 𝘪𝘨𝘯𝘰𝘳𝘦.

👉 Here's a quick Reality Check -
💵 Bad governance costs you:
→ 3 AM panic calls
→ Executive trust
→ Real money (hello, compliance fines!)
→ Your sanity

✅ Good governance gets you:
→ Predictable, reliable data
→ Business stakeholder love
→ Career advancement
→ Actual weekends

As a data engineer building governance always ensure that you track:
 • Time to Data - How long until new users can access data they need?
 • Data Trust Score - % of users who trust the data they use
 • Incident Response Time - How fast can you fix data quality issues?
 • Self-Service Adoption - Are users finding data themselves or asking you?
 • Compliance Coverage - % of sensitive data properly classified and protected
 • Metadata Completeness - How well documented is your data?

Here are some incredible folks to follow in the field of Data Governance -
Chad Sanderson
George Firican
🎯 Mark Freeman II
Piotr Czarnas
Dylan Anderson

𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀𝗻'𝘁 𝘁𝗵𝗲 𝗼𝗯𝘀𝘁𝗮𝗰𝗹𝗲 𝘁𝗼 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻—𝗶𝘁'𝘀 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝘁𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝘀𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲.

"I understand Git—I know git commit and git push."
That's like saying "I understand flying—I know takeoff and landing."

Everything in between? That's where pipelines break, data gets lost, and 3 AM incidents happen.

That the biggest myth in Data Engineering!
Even after 8+ years in tech, I've seen teams struggle not with writing code, but with managing it.

Git for Data Engineers: Understand Why It's Different
The Data Engineering Reality:

You're not just versioning code. You're versioning:
→ Pipelines that run on schedules
→ SQL transformations that power dashboards
→ DAGs that orchestrate entire workflows
→ dbt models that define business logic
→ Config files that control production behavior

𝘖𝘯𝘦 𝘸𝘳𝘰𝘯𝘨 𝘮𝘦𝘳𝘨𝘦 = 𝘗𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯 𝘥𝘰𝘸𝘯. 𝘋𝘢𝘴𝘩𝘣𝘰𝘢𝘳𝘥𝘴 𝘣𝘳𝘰𝘬𝘦𝘯. 𝘊𝘌𝘖 𝘢𝘴𝘬𝘪𝘯𝘨 𝘲𝘶𝘦𝘴𝘵𝘪𝘰𝘯𝘴.
Git isn't optional. It's your safety net.

🎓 Git Skills Every Data Engineer Needs

Level 1: Survival 
→ Clone, commit, push, pull
→ Create branches
→ Basic merges

Level 2: Proficiency  
→ Pull requests & code review
→ Resolve merge conflicts
→ Rebase vs merge (know the difference)
→ Cherry-pick commits

Level 3: Mastery 
→ Interactive rebase for clean history
→ Git hooks for automation
→ Submodules for shared code
→ Advanced conflict resolution

Here's a some resources to upskill with Git:
• Learn Git Branching - https://lnkd.in/dvPvYEeC
• Pro Git Book - https://lnkd.in/dasXAAYv

Without Git?
𝗬𝗼𝘂'𝗿𝗲 𝗳𝗹𝘆𝗶𝗻𝗴 𝗯𝗹𝗶𝗻𝗱, 𝗼𝗻𝗲 𝗺𝗶𝘀𝘁𝗮𝗸𝗲 𝗮𝘄𝗮𝘆 𝗳𝗿𝗼𝗺 𝗱𝗶𝘀𝗮𝘀𝘁𝗲𝗿.
With Git?
𝗬𝗼𝘂'𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝗹𝗶𝗸𝗲 𝗮 𝗽𝗿𝗼, 𝘄𝗶𝘁𝗵 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗿𝗼𝗹.

Image Credits: Priyanka Vergadia

What's your biggest Git fear or confusion as a data engineer? Let's solve it together in the comments.

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