About
👋 I’m Fari Lindo — founder and creator of Data Inside Data™.
I’m a systems-focused technologist working across IT infrastructure, cybersecurity, cloud engineering, data science and engineering, analytics, and automation. I believe learning is a lifelong practice — and that the most effective way to grow is by building real systems, solving real problems, and reflecting on the process openly.
My work centers on one core principle:
Build real systems. Document the thinking. Share the process.
Data Inside Data™ is where I design, build, and ship technical systems — from early experiments to production-ready workflows. It’s a living workspace where ideas move from concept to execution, and where complexity is broken down into structured, practical steps.
Whether you’re just starting out or already deep in the tech stack, my goal is simple:
make advanced systems understandable, repeatable, and usable in real-world environments.
What I Share Here
- Real projects that reflect my experience and ongoing pursuits
- Deep‑dive breakdowns of each project’s development process
- Beginner‑friendly tutorials for those entering tech
- Advanced content for experienced builders
- Tools, diagrams, and workflows that mirror real‑world engineering environments
This site serves as both a teaching space and a living archive — documenting what I build, what I learn, and how systems evolve from concept to production.
This site is intentionally hands-on, architecture-aware, and grounded in real-world problem solving, not theory alone.
My Work Operates at the Intersection of
- Data systems (data science, analytics engineering, databases)
- Application and systems development (web, tooling, automation)
- Cloud and DevOps/DevSecOps practices)
- IT and infrastructure (hardware, operating systems, networking)
- Documentation-driven engineering
- Impact-oriented systems (education, nonprofit, and community work)
It highlights the environments, training, and projects that shaped my approach to building, analyzing, and documenting technical systems.
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%% ------------------------------------
%% Track 1 — Infrastructure Foundations
%% ------------------------------------
subgraph T1["Infrastructure Foundations"]
A["2020<br/>Windows Server<br/>2012 R2"]
B["2023<br/>BMCC A+<br/>Training"]
P["2024<br/>AWS CCP<br/>Training<br/>—Cert"]
C["2025<br/>Per Scholas<br/>CompTIA A+"]
A --> B --> P --> C
end
%% ---------------------------------------
%% Track 2 — Application Dev Layer
%% ---------------------------------------
subgraph T2["Application Dev Layer"]
D["2024<br/>Revature<br/>Java + SQL"]
D2["2024<br/>Udacity<br/>AI w/ Python"]
D --> D2
end
%% ---------------------------------
%% Track 3 — AI & Data Systems Layer
%% ---------------------------------
subgraph T3["AI & Data Systems Layer"]
X["2023<br/>Fullstack Academy<br/>Data Analytics"]
E["2024<br/>Udacity<br/>AI/ML Nanodegree"]
F["2024<br/>AWS Racing League"]
G["2025<br/>The Knowledge House<br/>Data Science Fellowship"]
X --> E --> F --> G
end
%% ------------------------------
%% Track 4 — Platform Integration
%% ------------------------------
subgraph T4["DataInsideData™ Platform"]
I["2026<br/>Platform Launch"]
end
%% -------------------------
%% Cross-track progression
%% -------------------------
C --> D
D2 --> X
G --> I
📌 This diagram represents a systems view of my technical development — not a linear timeline, but a layered progression of capabilities that build toward real-world platform design and implementation.
🏅 Certifications & Credentials
These credentials reflect both formal training and hands-on application across data, cloud, and infrastructure systems.
☁️ AWS Certified Cloud Practitioner
Issued: March 2025
- Core AWS services
- Cloud security & shared responsibility
- Cost-aware infrastructure design
🛠 CompTIA A+ Certification
Issued: March 2025
- Hardware & operating systems
- Networking fundamentals
- Troubleshooting & system reliability
🎓 The Knowledge House Fellowship — Data Science Track
| New York City | 2025 Cohort |
An intensive fellowship focused on:
- Applied Python & SQL
- Machine learning foundations to expert
- Real client-facing analytics projects
- Communicating insights to non-technical stakeholders
📊 Udacity — AI Programming with Python Nanodegree
A structured program focused on building practical AI and machine learning capabilities using Python, with an emphasis on real-world application.
Focus Areas:
- Python for data and AI workflows
- NumPy and Pandas for structured data processing
- Matplotlib for visualization and analysis
- Classification and machine learning fundamentals
- Computer vision concepts
- LLMs and generative AI
- GPU acceleration with CUDA
- Applying models to real-world, business-driven problems
🧭 How to explore this site
- Projects → Architecture, systems, and case studies
- Posts → Tutorials, breakdowns, and lessons learned
- Start Here → Guided entry points based on your goals
- How Tos → Practical step-by-step walkthroughs
- Fixes → Concise troubleshooting notes based on real development
🤝 Let’s connect
If you’re interested in collaboration, consulting, or just exchanging ideas:
📩 visit the Contact page.
Data Inside Data™ is not just a blog — it’s a workspace.
Thanks for being here.
Data Inside Data™
Tech Hands, a Science Mind, and a Heart for Community™.