DEV Community: Kuria

ETL vs ELT: Which One Should You Use and Why

Kuria — Wed, 22 Apr 2026 18:29:50 +0000

As a data engineer there is a myriad of tools to choose from in the quest to avail clear data for analysis. Clean data leads valuable insights and business decision. On the other hand unclean data results in bad business decisions and insight.

Among the tool utilized by data engineers is ETL and ELT. ETL is a vital data processing tool that is used to Extract, Transform and Load data from various sources and into a designated system.

The ETL process commence by extraction of raw data from various sources such as the Database, CSV files, APIs Applications among other sources. The raw data undergoes transformation which entails cleaning, data type validation and converting the data into a proper format for analysis. Data is then loaded to a Database or Data warehouse ready for analysis.
ETL solutions enhance data quality by cleaning and preparing the data before it is loaded into a target repository.

On the other hand, ELT process data is extracted, loaded and later transformed. The key distinction between ETL and ELT (extract, load, transform) therefore lies in the order of steps. In ELT, data is extracted from source systems and loaded directly into the target repository in its raw form, rather than being first placed in a staging area for transformation. The transformation is then performed within the target system as required.

While both methods utilize data lakes and warehouses, they offer different trade-offs in terms of flexibility and preparation.

ELT (The High-Speed Approach)

This method is built for scale and speed. Because it loads data directly from the source without pre-processing, it is the preferred choice for massive, unstructured "Big Data" sets. You don't need a perfectly defined plan for storage or extraction before you start moving data, which makes it highly agile.

ETL (The Methodical Approach)

ETL requires significant upfront strategy. Before moving anything, you must identify specific data points, establish integration keys, and map out metadata. Furthermore, you have to build complex transformation rules based on exactly how the data will be analyzed later. This means the data is already summarized and "cleaned" by the time it reaches its destination.

summary

ELT is better suited for handling large volumes of big data, while ETL works best with smaller, structured datasets. ELT requires minimal upfront planning and performs transformations after the data is loaded into the warehouse, making it more flexible and adaptable for future use. In contrast, ETL involves more predefined rules and transforms data before loading, resulting in a more rigid process tailored to specific use cases.

How Analysts Turn Messy Data, DAX, and Dashboards into Action with Power BI

Kuria — Sun, 08 Feb 2026 18:58:55 +0000

Data is everywhere. Insight is not.
Most organizations don’t struggle with having data—they struggle with turning scattered, messy, and often contradictory data into decisions people actually trust and act on. This is where analysts earn their keep, and where Power BI quietly becomes one of the most powerful tools in the modern analytics stack.
This article walks through how analysts use Power BI to translate raw data, complex DAX, and dashboards into real business action.

Messy Data Is the Starting Point, Not the Problem
Let’s be honest: clean data is the exception.
Analysts usually inherit:
• Excel files with inconsistent columns
• Databases designed for transactions, not analytics
• Multiple systems that disagree on basic definitions
• Missing values, duplicates, and broken dates
Power BI doesn’t magically fix this—but it embraces it.
Power Query: Where the Real Work Begins
Power Query is often where analysts spend the most time. This is where chaos turns into structure.
Common steps include:
• Standardizing column names and data types
• Cleaning nulls, duplicates, and formatting issues
• Merging data from multiple sources
• Creating derived fields like fiscal periods or status flags
Every transformation is recorded, repeatable, and refreshable. That alone is a massive upgrade from one-off Excel cleanups.

The Data Model Is the Real Product
Dashboards get the attention, but the data model does the heavy lifting.
Great analysts think less about charts and more about how the business actually works:
• What is a customer?
• How should revenue be aggregated?
• Which dates matter: order date, ship date, or invoice date?
Modeling for Humans, Not Just Machines
Power BI models are typically built using:
• Fact tables for transactions
• Dimension tables for context (dates, products, customers)
• Clear relationships with predictable filtering behavior
A strong model reduces DAX complexity, improves performance, and—most importantly—ensures everyone is answering the same question with the same logic.
This model becomes the organization’s analytical language.

DAX: Where Questions Become Answers
DAX is often described as “hard,” but in reality, it’s just precise.
Executives don’t ask for sums and averages. They ask things like:
• “Are we performing better than last quarter?”
• “Which regions are underperforming right now?”
• “What happens if we exclude one-time events?”
Why DAX Matters
DAX allows analysts to encode business logic once and reuse it everywhere:
• Time intelligence (YTD, rolling 12 months, comparisons)
• Ratios and KPIs
• Conditional logic for thresholds and alerts
The key skill isn’t memorizing functions—it’s understanding evaluation context. Knowing how filters flow through a model is what makes measures accurate, fast, and reliable.

Dashboards Are Interfaces for Decisions
A dashboard is not a data dump. It’s a decision interface.
The best Power BI dashboards answer three questions:

What’s happening?
Why is it happening?
What should I do next? Designing for Action Effective dashboards: • Surface KPIs first, details second • Use trends and comparisons instead of static totals • Highlight exceptions, not just averages Interactivity matters. Drill-downs, slicers, and tooltips let users explore data without calling the analyst every time they have a follow-up question. That’s how analytics scales. ________________________________________ Turning Insight into Action Insight without action is just interesting trivia. Analysts intentionally design reports to support: • Operational decisions (daily, tactical) • Management reviews (monthly, performance-focused) • Strategic planning (longer-term trends and scenarios) Clear targets, variance indicators, and contextual benchmarks help users quickly see where attention is needed. Trust Is the Final Step Publishing through Power BI Service, applying row-level security, and certifying datasets builds trust. When users trust the numbers, they stop debating data and start debating decisions. That’s the real win. ________________________________________ Final Thoughts Power BI is not “just a visualization tool.” It’s where data preparation, modeling, analytics, and storytelling come together. Analysts sit at the intersection of all four: • They clean messy data • Model the business correctly • Translate questions into DAX • Design dashboards that lead to action When done well, Power BI doesn’t just report on the business—it changes how the business operates. ________________________________________

Introduction to Git and Github

Kuria — Sun, 18 Jan 2026 06:08:48 +0000

Have you ever asked yourself how developers used to keep track of changes they made to files before Git?
It must have been a tedious task, saving tons of files just to track changes. For instance, a developer might save files as file001, file002, file003, and so forth.

Thanks to Git and GitHub, with just a few commands a developer can save work and keep an eye on every change made to a file.

Git is a version control system that allows users to track changes in files.

On the other hand, GitHub is an online platform that helps developers store, share, and work together on code. Git works on your computer, while GitHub stores your Git projects on the internet.

why version control is important:

Every change is recorded
You can go back to older versions of your code
You can see who made a change and when
Multiple developers can work on the same project safely

How to Track Changes Using Git

To start tracking changes, you first need to create a Git repository.

Step 1: Initialize Git

Inside your project folder, run:

git init

This tells Git to start tracking the project.

Step 2: Check File Status

To see which files have changed:

git status

Step 3: Add Files to Staging

To prepare files for saving:

git add .

(The . means “add all changed files.”)

Step 4: Commit Changes

To save a snapshot of your work:

git commit -m "Describe what you changed"

A commit is like saving a checkpoint in a game—you can always return to it later.

How to Push Code to GitHub

Pushing means uploading your local code to GitHub.

Step 1: Create a Repository on GitHub

Go to GitHub
Create a new repository
Copy the repository URL Step 2: Connect Your Project to GitHub

In your project folder, run:

git remote add origin

Step 3: Push Your Code
git push -u origin main

Now your code is stored on GitHub and accessible online.

How to Pull Code from GitHub

Pulling means downloading the latest code from GitHub to your computer.

To get updates from GitHub:

git pull origin main

This is useful when:

You are working on multiple computers
Other developers have made changes to the project

Summary

Git tracks changes in your files
Version control helps you manage and recover your code
GitHub stores your projects online
Push uploads your code to GitHub
Pull downloads updates from GitHub
Commits save your progress step by step