DEV Community: Paulet Wairagu

Cloud : My AWS CCP Study plan

Paulet Wairagu — Thu, 09 Apr 2026 17:19:46 +0000

I come from a technical background; data, SQL, automation and dashboards but cloud is one area I’ve only _tinkered _with so far. Nothing fully structured, nothing end-to-end.

That changes now.

It’s April 9th, and my goal is simple:
👉 Sit for the AWS Certified Cloud Practitioner (CCP) exam in the last week of April.

This isn’t just about passing an exam. It’s about finally bringing all my scattered cloud knowledge into one structured foundation I can actually build on.

Even better — I’m doing this 100% with free resources, and if you’re on the same path, feel free to follow along or use this as your guide.

📚 My Study Plan
1. Core Learning (Main Course)

Andrew Brown’s AWS CCP Course (FreeCodeCamp)
This is the backbone.

Anyone who knows Andrew knows he doesn’t miss — his cloud content is detailed, practical, and built for real understanding.
The 14-hour course is honestly a gem, and I’ll be using it to cover all core concepts end-to-end.

Reinforcement (Interactive Learning)

AWS Skill Builder
This is where I’ll reinforce concepts in a lighter, more interactive way.

I like that it breaks things down simply and even gamifies parts of the learning.
I’ll also be using their official exam prep plan to stay aligned with what AWS actually expects.

Practice & Testing YouTube practice questions (I’ll keep adding links as I go) Repetition of weak areas Simulating exam-style thinking

This is where things move from “I understand” to “I can pass.”

Notes & Organization Notion will be my command center

I’ll use it to:

Track progress
Write summarized notes
Store key concepts and cheat sheets
Collect useful links/resources
🎯 Why This Approach?

I’ve realized something important:
I don’t lack ability — I’ve just lacked structure.

I’ve explored AWS before, but in pieces. This plan is about pulling everything together into one clear, focused sprint.

⏳ Timeline
Start: April 9
Exam Target: Last week of April
Approach: Focused, daily consistency over perfection
🤝 If You’re Also Studying…

If you’re preparing for AWS CCP too:

Feel free to use this plan
Share resources
Drop practice questions
Let’s make this easier together

I’ll keep updating this with useful links and insights as I go.

Cloud : My AWS CCP Study plan

Paulet Wairagu — Thu, 09 Apr 2026 17:14:11 +0000

I come from a technical background; data, SQL, automation and dashboards but cloud is one area I’ve only _tinkered _with so far. Nothing fully structured, nothing end-to-end.

That changes now.

It’s April 9th, and my goal is simple:
👉 Sit for the AWS Certified Cloud Practitioner (CCP) exam in the last week of April.

This isn’t just about passing an exam. It’s about finally bringing all my scattered cloud knowledge into one structured foundation I can actually build on.

Even better — I’m doing this 100% with free resources, and if you’re on the same path, feel free to follow along or use this as your guide.

📚 My Study Plan
1. Core Learning (Main Course)

Andrew Brown’s AWS CCP Course (FreeCodeCamp)
This is the backbone.

2. Reinforcement (Interactive Learning)

AWS Skill Builder
This is where I’ll reinforce concepts in a lighter, more interactive way.

I’ll also be using their official exam prep plan to stay aligned with what AWS actually expects.

skillbuilder.aws

I like that it breaks things down simply and even gamifies parts of the learning.

3. Practice & Testing
YouTube practice questions (I’ll keep adding links as I go)
Repetition of weak areas
Simulating exam-style thinking

This is where things move from “I understand” to “I can pass.”

4. Notes & Organization
Notion will be my command center

I’ll use it to:

Track progress
Write summarized notes
Store key concepts and cheat sheets
Collect useful links/resources

🎯 Why This Approach?

I’ve realized something important:
I don’t lack ability, I’ve just lacked structure.

I’ve explored AWS before, but in pieces. This plan is about pulling everything together into one clear, focused sprint.

⏳ Timeline
Start: April 9
Exam Target: Last week of April
Approach: Focused, daily consistency over perfection
🤝 If You’re Also Studying…

If you’re preparing for AWS CCP too:

Feel free to use this plan
Share resources
Drop practice questions
Let’s make this easier together

I’ll keep updating this with useful links and insights as I go.

SQL - Basics

Paulet Wairagu — Tue, 13 Jan 2026 07:34:23 +0000

Data is everywhere and is often stored in different files and formats. As data grows, managing it using files alone becomes inefficient. This creates the need for a database.

A database is an organized collection of data that allows information to be stored, retrieved, and managed efficiently. Databases are beneficial because they provide:

Large and scalable storage
Secure and controlled access to data
Data consistency and integrity
Faster querying and data management

A Database Management System (DBMS) is a software system that allows users to create, manage, secure, and manipulate databases. Examples include MySQL, PostgreSQL, Oracle, SQL Server, and SQLite

Structured Query Language (SQL) is the standard language used to communicate with relational databases. SQL allows users to:

Create databases and tables
Insert, update, and delete data
Retrieve data using queries
Control access and permissions

Several types of databases exist, each designed for specific use cases:

Relational Database Stores data in predefined tables made up of rows and columns. Example: MySQL, PostgreSQL Best for structured data and complex queries.
Key-Value Database Stores data as key–value pairs. Example: Redis, DynamoDB Best for fast lookups and caching.
Column-Based (Column-Oriented) Database Stores data grouped by columns rather than rows. Example: Apache Cassandra, Google BigQuery Best for analytics and large-scale data processing.
Graph Database Stores data as nodes and relationships. Example: Neo4j Best for relationship-heavy data such as social networks.
Document Database Stores data in document formats such as JSON or BSON. Example:_ MongoDB_ Best for semi-structured or flexible data.

Databases exist within a structure.
A database server hosts and manages databases.
Within a database, data is organized using a schema.
A schema defines the structure of the database and contains:

Tables
Views
Indexes
Stored procedures and other objects

Tables consist of:

Rows (records): individual entries
Columns (fields): attributes of the data

Types of SQL Commands (With Examples)

DDL – Data Definition Language Used to define and modify database structures. CREATE TABLE students ( student_id INT PRIMARY KEY, name VARCHAR(50), age INT );

ALTER TABLE students ADD email VARCHAR(100);

DML – Data Manipulation Language Used to insert, update, and delete data.

INSERT INTO students VALUES (1, 'Alice', 20);

UPDATE students SET age = 21 WHERE student_id = 1;

DELETE FROM students WHERE student_id = 1;

DQL – Data Query Language Used to retrieve data from the database.

SELECT * FROM students;

SELECT name, age FROM students WHERE age > 18;

DCL – Data Control Language Used to control access and permissions.

GRANT SELECT ON students TO user1;

REVOKE SELECT ON students FROM user1;

It is also worth noting the Coding Order in SQL (Logical Query Processing Order).
Although SQL is written in one order, it is executed logically in the following sequence:

FROM
WHERE
GROUP BY
HAVING
SELECT
ORDER BY
LIMIT

Example:
SELECT department, COUNT(*) FROM employees WHERE salary > 50000 GROUP BY department HAVING COUNT(*) > 3 ORDER BY department;

THINKING GAME DOCUMENTARY: MY REVIEW

Paulet Wairagu — Wed, 26 Nov 2025 18:47:57 +0000

So I finally watched AlphaGo, that documentary about the Google DeepMind AI that took on the world champion in Go, and honestly… I didn’t expect to enjoy it this much. I thought it would be one of those “tech bros hype themselves” films, but wueh, it’s actually deep.

First things first,the game itself. Go is like chess on steroids. Watching those pros talk about it felt like watching athletes explaining how they breathe. The amount of strategy, intuition, and reading the board… it made me respect the game a lot more. And the way the documentary broke it down for normal people? Lovely. Even those of us who have never touched a Go board can follow the tension.

Then there's Lee Sedol. Man, that guy carried the emotional weight of the whole thing. You feel the pressure on him — not just to win, but to defend human creativity. The guy literally said he felt like he was playing on behalf of everyone. That scene where he loses a game and walks out looking completely defeated? Your heart just sinks. Been there, that feeling of “I did everything and still lost.”

And then AlphaGo. The AI itself almost feels like a character. Quiet, calculating, no hype, just vibes and probabilities. The wild part is when it makes those “impossible” moves that even the Go masters can’t understand. Move 37 especially — the commentators looked like they’d seen witchcraft. Even Sedol was like, “No human plays like that.” That’s the moment I realized AI isn’t just copying; sometimes it’s genuinely creating.

But my favourite part is how the doc doesn’t frame it as “humans vs robots.” It shows how the match changed how humans think. After the loss, the pros started studying AlphaGo games and discovering new strategies. Like the AI unlocked creativity instead of killing it. That hit me because we’re in that same AI era now — people thinking AI is coming to take all jobs, yet here we are, learning new ways of thinking from it.

Cinematography was also clean — the slow, quiet shots, the close-ups, the music. It’s not rushed or over-dramatic. Just calm, like meditation.

If you like strategy, tech, psychology, or you just want to see a human fight for dignity against a machine, this is a solid watch. It’s not a hype documentary; it’s a thoughtful one. Emotion, tension, and a bit of “eish, surely, how is an algorithm beating a whole world champion?”

Highly recommend.

The 5 Most Common Data Quality Issues (and How Analysts Can Fix Them)

Paulet Wairagu — Mon, 24 Nov 2025 10:50:56 +0000

Data analysts spend more time cleaning data than analyzing it. In fact, in most real-world projects, 60–80% of your time goes into preparing data for meaningful insights.
Poor data quality leads to incorrect conclusions, broken dashboards, and bad decisions which is why understanding common issues and knowing how to fix them is a core skill for every analyst.

Here are the five most common data quality problems and practical steps to solve each one.

1. Missing or Null Values

Missing data can distort metrics, create gaps in reports, or lead to inaccurate ML models.

Causes:
• Manual data entry errors
• Incomplete integrations
• System migration issues

How to fix it:

Identify missingness patterns using COUNT(*) in SQL or df.isna().sum() in Python.
Drop rows only when missing data is irrelevant.
Impute using averages, medians, or domain logic.
Use Power Query’s “Replace Errors” or “Fill Down” functions for structured fixes.

2. Inconsistent Formatting

You’ve probably seen this: “Kenya”, “kenya”, “K E N Y A”, or mismatched date formats in the same column.

Why it happens:
• Different data sources
• Manual inputs
• Lack of data validation rules

How to fix it:

Apply standard casing (upper/lower/title).
Convert all dates to a unified ISO format (YYYY-MM-DD).
Use Excel Power Query’s “Transform → Format” options.
In SQL, standardize with functions like UPPER(), TRIM(), or TO_DATE().

3. Duplicate Records
Duplicates inflate counts, break KPIs, and cause incorrect aggregations.

Why it happens:
• Multiple data entry points
• Poor primary key definition
• System sync issues

How to fix it:

Identify duplicates using ROW_NUMBER() windows in SQL.
Use Power Query’s “Remove Duplicates”.
Implement unique IDs early in the pipeline.
In Python, use df.drop_duplicates().

4. Outliers and Incorrect Values

Some values are valid extreme cases; others are simply errors (like a customer aged 600).

Why it happens:
• Typographical errors
• Faulty sensors or scraping issues
• Incorrect units (meters vs. feet)

How to fix it:

Visualize distributions using box plots or histograms.
Apply domain thresholds or rule-based logic.
Use interquartile ranges or z-scores for statistical outlier detection.
Create automated validations in Power BI or SQL.

5. Mixed Granularity

Data at different levels combined into one column or table — e.g., weekly and monthly data in the same dataset.

Why it happens:
• Data integration from multiple systems
• Poorly designed source tables

How to fix it:

Split datasets by granularity before analysis.
Create dimensional tables for dates, products, etc.
Aggregate or disaggregate consistently before joining.
Use a proper star schema when possible.