DEV Community: Kahindi Kevin

Learning how to use Windows SQL Functions and Joins in Relational Databases.

Kahindi Kevin — Wed, 04 Mar 2026 02:42:57 +0000

This article explains how Windows Functions and SQL Joins are used and their importance for querying data from databases.

Rows from two or more tables can be combined using a JOIN clause, depending on a linked column between them.

Several types of joins help in data query and manipulation in SQL. These varieties are discussed as follows:

INNER JOIN
Entries with matching values in both tables are chosen using the INNER JOIN keyword.
In reference to a table, this can be illustrated as follows:-

A good example illustrating an INNER JOIN is as follows:

select o.order_id, c.first_name,c.last_name
from customers c
inner join orders o
on o.customer_id =c.customers_id;

NOTE:-
As long as the columns match, the INNER JOIN keyword selects every row from both tables.
Orders will not be displayed if there are records in the "Orders" table that do not match those in the "Customers" table.

RIGHT JOIN
This is an important query command in SQL.
All of the records from the right table (table2) and any matching records from the left table (table1) are returned using the RIGHT JOIN keyword.

The tables being highlighted are shown below:

An illustration of this is as follows:

SELECT 
    c.customer_id,
    c.name,
    c.city,
    COALESCE(SUM(o.amount), 0) AS total_spent,
    COUNT(o.order_id) AS order_count
FROM customers c
RIGHT JOIN orders o 
    ON c.customer_id = o.customer_id
GROUP BY c.customer_id, c.name, c.city;

LEFT JOIN

All of the records from the left table (table1) and any matching records from the right table (table2) are returned using the LEFT JOIN keyword.

CROSS JOIN
This type of join is as illustrated below:-

The Cartesian product of two or more tables is returned by the CROSS JOIN keyword, which combines each record from the first table with each row from the second table.

NOTE:-
Therefore, if the first table has 50 rows, and the second table has 2500 rows, the result set will be 50x2500 rows.

WINDOWS FUNCTIONS IN SQL
Window functions allow you to perform calculations across a set of table rows that are somehow related to the current row.
Unlike an aggregate function like SUM(), which returns one result for a group of rows, window functions return a value for each row while still providing information from the related rows.

Types of Windows Functions

A. ROW_NUMBER () function
This function assigns a unique number to each row, starting from 1, based on the order specified by the ORDER BY clause.

B. LAG () function
This function will help query values from the past data from the previous row, e.g., if you want to compare the current lead time in days and the previous lead time in days.

C. RANK () function
You can use this function to rank your data in either descending or ascending order. In a tie, it will bypass the subsequent rank value. For example, the following record will be placed fourth rather than third if two rows are tied at rank 2.

D. NTILE() function
You can use this function to split a set of rows into specified, roughly equal portions.
For example, NTILE(3) will split the chosen rows into three roughly equal parts; by roughly, we mean that the number of rows in each portion may vary.

E. DENSE_RANK() function
This function helps to rank - in that, it does not leave gaps, so if there are ties, the next rank will be consecutive.

Example
If two customers are tied for rank 1, the next customer will be ranked 2nd.

I have found it great to keep on learning!
Here are my thoughts on this subject.
I cannot wait to interact with you on this topic!
Keep it here for more!

Dashboards: A Study into the nexus between KPI's, Metrics and Data points in Microsoft Excel

Kahindi Kevin — Tue, 17 Feb 2026 10:19:54 +0000

Learning is a never ending adventure. You end up realizing a whole lot of things once you are curious about an item of interest. The area of interest that stirred my curiosity this time was; dashboards.
What are they? What entails a dashboard that helps us understand the data easily?
A dashboard is a centralized, visual interface that displays metrics, data points, and key performance indicators (KPIs) in real-time; thus helping in decision-making.
The dashboard entails KPI's, metrics and data points that helps in this role.
The idea that propelled me to understand this topic is to help all aspiring analysts and experienced engineers to develop good dashboards.
Dashboards should not just be filled with many graphics that do not answer to the key questions intended to better the enterprises performance.

Importance of Dashboards

Enables continued monitoring and actual analysis of insights.
Helps to support informed decision making by business executives
Through identifying patterns and trends in data, strategic planning and risk management is achieved by the company's executives.

What is a Data point?
Data points are individual texts, dates or items in a worksheet that form important basis for visualization and analysis.
They could be an intersection of two points in a graph or distinct parts in the cells of various worksheets.

They are represented by slices, dots and bars in a data series.
They can also be represent key items that need farther investigation by the important management figures normally represented as outliers.

What is a Key Performance Indicator (KPI)?

Anyone new to the analytics world could succumb to getting mixed up. Ideally, elements of a data set in different columns are not KPI.
That is to say:

CustomerID is not a KPI
Ordernumber is not a KPI
CostofProduction is not KPI

From the above submissions, it is proper to digest and lay bare the facts from my inquest that a KPI is:-

I. It is not a text - it is a number backed by information.

Lead time (in days)
Average revenue
Discount (%)
Net Sales (volumes)

II. Too many KPI's = confusion

From my understanding about KPI's, I was obsessed with need to fill my dashboard with many KPI's thinking I was improving how my users interact with the shared data.
Unfortunately, I was wrong, this does not in any way help my users.

III. KPI's answers "how" and metrics helps to answer 'why"
A good example for this:

Net Profits increased

What is a Metric?
Our metrics helps to answer this, why?:-
A metric is a quantitative data form that measures performance against a specific goal.

Why?

The number of goods sold rose
The variable costs decreased by a considerable margin
The price of our products were favorable in comparison to our competitors

IV. KPI's must change over time
The basis of a KPI is to track changes over a period of time.
This helps to measure the progress with ease.
A good KPI needs to change over time.

Slicers help to amplify the KPI. When you input a slicer to your data items; and it changes, it helps certify that it is a good KPI.

Dashboards are important when it comes to storytelling.
An easy to interact dashboard helps in quicker analysis and faster implementation of decisions made.
From my interest in trying to understand Microsoft Excel, it helped me to change my perspective towards it.
It has made me change from just seeing columns, rows and cells to viewing it as a performance measurement tool.
What are your metrics and data points?
What are your KPI's?
Are they helping you conduct a proper analysis or not helping at all?
The study quest continues 🚀

Technical Articles: An insatiable field that needs exploration!

Kahindi Kevin — Mon, 09 Feb 2026 16:24:56 +0000

Writing is a skill that needs constant exploration to better your learning. This is a field that, when appropriately exploited, leads to transformative changes in the technical space.

The need for Technical Articles

Your learning curve progression is pegged on great articles

Well-written technical articles are an indication of how well you are learning. The proper expression in the articles reflects how well you have been able to grasp key concepts in your learning.

Enhanced Learning by Teaching Others

With a great command in self-expression by way of writing understandable articles, one is able to improve his self learning journey. This ensures that other people learn best from their articles.

Writing helps promote what you are doing

Writing is an art normally explored as a way to reach a greater audience. This audience consists of your "untapped market" who requires your services.
How you write enables you to have a command of what you are doing and, therefore, be in a position to market that skill widely to even earn a livelihood from it.

Explore emerging trends in the technological space

Technology is an ever-changing space. Every day, technological alterations occur through inventions and innovations. This needs to reach a wider coverage across the globe. That is where technical writing becomes necessary. Through writing technical jargon in easy-to-understand language, many people are able to appreciate the technological leaps.

What to anticipate from my Learning!
I am new to data science and analytics - I look forward to working with key tools like Microsoft Excel, Power BI, Python and PostgreSQL.
I will keep you posted on my learning journey
All the best in your personal learning journey!
Let's engage as we learn and seek to expand our frontiers in the technological space.
I look forward to hearing from all of you!🚀

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

Kahindi Kevin — Sat, 07 Feb 2026 17:42:45 +0000

Data runs the world.
From a set of raw data, an analyst is supposed to make sense of it.
One of the key tools to derive insights from large data sets is Power BI.
Power BI is preferred since it can easily handle different data formats.
The analyst has to clean the data through actionable steps on Power BI, like -

Removing duplicates from various rows/ columns
Removing pseudo blanks; i.e., unknown values, errors and nulls
Find and replace values
Text cleaning that involves inputting capitalizations, lowercases, proper names and uppercases

These actions are conducted on Power Query Editor. This is done through the following steps:

REMOVING BLANK COLUMN/ ROWS
Select the column or row that needs to be worked on:
- Right-click on the column/ row
- On the pop-up options displayed, select the remove blank option
- This can also be used to remove duplicates and pseudo-blanks

Power BI uses data analysis expressions (DAX) functions to create tables, measures and columns.
These tools are also important for solving simple and complex calculations, including mean, sum, maximum values per item (MAX), minimum values per item (MIN) and geometric mean.
Power BI displays its calculated values through either a new column or a new measure.
A new column creates a column which displays a set of values from the data being used, then calculates the value and displays it on the right tab under the Power BI title chart.
A new measure requires the help of the card tool on Power BI to visualize the expected outcome of a certain data type item.

Depending on the item that you seek to find on Power BI, you will be able to find it.
The following example best expresses how this occurs on Power BI:

AVERAGE Function

Average Planted Area = AVERAGE('Kenya_Crops_Power BI DATASET'[Planted Area (Acres)]) 
when calculating the average planted area

MAX Function

To get the Highest Revenue of a given dataset, this is the expression that is input into the Power BI.

Highest Revenue = MAX('Kenya_Crops_Power BI DATASET'[Revenue (KES)])

The calculated measure values are then displayed through the new measure option as follows:

The above illustrations can be replicated when finding the MIN values, geometric mean, sum, and averages.

Power BI is also great at finding logical relationships with the help of logical functions.
Most of these logical functions are developed under the new column in Power BI.
To best expound on these functions; we are going to adopt two examples:

Yield Category

When given a Kenya crops dataset example and told to categorize the yield category into high, medium or low.
This is how to go about it on Power BI:

Yield Category Classification = IF('Kenya_Crops_Power BI DATASET'[Yield (Kg)]>3000, "High",IF('Kenya_Crops_Power BI DATASET'[Yield (Kg)]>=1000,"Medium","Low"))

This is how Power BI will work on the data to display the following information:

Planting Date in Quarters

When given a Kenya crops dataset example and told to categorize the planting date into quarters - quarter 1, 2, 3 and 4.
This is how to go about it on Power BI:

Planting Quarter = IF(MONTH('Kenya_Crops_Power BI DATASET'[Planting Date])<=3, "Quarter 1", IF(MONTH('Kenya_Crops_Power BI DATASET'[Planting Date])<=6,"Quarter 2", IF(MONTH('Kenya_Crops_Power BI DATASET'[Planting Date]<=9), "Quarter 3", "Quarter 4")))

The display on Power BI as the result of the logical expression above will be as follows:

Graphs used to Document the Data Items Results

The analysis of data is not complete through illustrations of the data displayed in the form of different graphs.
This makes it easier to develop insights that will be relevant to assist the decision makers in arriving at impactful decisions on the enterprise.
When you are asked to develop a chart that will display the top 10 farmers in our illustration data - Kenya_ Crops_ Dataset.
This is how to go about it.

Select the items to be placed on the x-axis and y-axis.

In this case, the x-axis represents the sum of revenue, and the y-axis represents the farmer's name

On the visualization tab on the right-hand side of Power BI, select the appropriate chart type.

In this case, the chart type was a clustered bar chart.

The other key item that needs you to be keen on is the application of filters to narrow down the data from all the farmers to the top 10 farmers to be displayed on the chart.

The filter chart is applied after selecting the chart as follows:

Click on the chart
On the right side of your Power BI, select the filter tab.
Keep your eyes on the filter type - select the Top N
Thereafter, under the show items, select Top and input the number you want, in this case 10.

The graph displayed is as shown below:

Several other charts can be developed using the above steps.
I will look into the donut chart, Q & A chart, filled map and combo chart that I will use as my final display on the dashboard.

Donut Chart

This is used to display data analysis in duo combinations of interrelated data items, for example, revenue and crop variety.
It is used when the display is not "overcrowded"
This is important to help in easier analysis.

A display of this chart is as follows:

Q & A Chart

This is a visualization that helps the analyst to place a platform where anyone can ask questions to the dashboard and get replies on the data under study.
The chart is displayed as follows:

Filled Map

Uses the global positioning system (GPS) to pinpoint where your case study data is from around the globe.
The only anomaly with this display item is that it uses well - known items names when displaying on the map.
An illustration is when analysing county information, our map pointed out Meru in Tanzania instead of Meru, a county in Kenya
This can be corrected by using these steps:
Select the county item on the data tab item under Power BI in the Kenya _ Crops _ Dataset
Go to the column tools on the Power BI ribbon
Under data category, select State/Province
It rectifies the issue with GPS picking the well-known landmarks around the globe.
This displays the steps above:

The filled map is displayed as follows:

Combo Chart

It displays two or more variables that are under analysis.
This could be a revenue and profit analysis by county.

The chart is displayed as follows:

Dashboards on Power BI

A high-level view of specific KPIs from one or more reports is offered by dashboards.
Reports are added to an existing or new dashboard after being pinned to a live site.

Importances of Dashboards in Power BI

Enhanced Data Storytelling
Dashboards facilitate the communication of insights by assisting in the explanation of the "what," "when," "why," and "how" underlying data using interactive tiles and, in certain situations, data storytelling.
**Customization and Personalization*
Dashboards can be tailored to provide metrics that are significant to particular departments or jobs, guaranteeing that users get the most important data.
Useful Information from Q&A
Data discovery is facilitated by the integrated Q&A function, which enables users to pose queries in natural language and get prompt visual responses.

An example of a dashboard is as follows, developed from the set of charts shared above.

It has been an amazing learning experience on Power BI.
This is an important tool for anyone seeking to understand data analysis. This is my input in the learning curve towards mastering data analysis.
Your input in the form of comments, clarifications and guidance will be important towards bettering my journey towards being great in this field.
Happy learning, all!📌

Power BI - A Beginner's Guide to Data Modeling and Schemas

Kahindi Kevin — Sat, 31 Jan 2026 17:10:22 +0000

Enterprises globally rely on insights from analysed raw data in their lines of operation to survive.
This survival necessitates credible business intelligence on future predictions, which must be developed through visualisation-friendly tools.
Proper business intelligence determines the direction the organisation will take and how it will survive in its industry.

From my desire as a data analyst to understand what tools are important in the field of business intelligence and visualisation, I have conducted an analysis and found out that - Power BI is a great tool!

Data visualisation is a cornerstone of effective data storytelling.
With the use of Power BI, all who are not data analytics-savvy are open to consuming information through trends and representations of pie charts, line graphs and columnar charts.
Power BI has incorporated key concepts to ensure the ease of all users in learning through data models and visualisations - these concepts include: the star and snowflake schema, fact and dimension tables and how important it is to create relationships between data types for easier use. All of these concepts will be explained further below:

Data Modelling and Visualisation Importance in Business Modelling

The BI in Power BI stands for business intelligence.
Business intelligence (BI) is a wide range of tools, procedures, and methods that concentrate on analysing corporate data to produce useful insights that aid in operational and strategic decision-making.
Decisions determine the direction of an organisation. These decisions are only helpful when they are arrived at in a timely and cost-effective manner.
Power BI helps board members in enterprises to arrive at meaningful conclusions on the right direction to be taken by the organisation through descriptive analytics, reports and dashboards.
To arrive at data models, Power BI embraces the star and snowflake schema concepts.

What is a Schema?

A schema in Power BI is the arrangement and structure of data inside a data model.
Schemas impact the effectiveness and performance of data searches and reporting by defining the connections and relationships between data within the model.
Designing the best data models that facilitate thorough analysis requires an understanding of schemas.

Understanding the Star Schema Concept in Power BI

This is a fundamental principle in Power BI.
A star schema is a type of database schema that is used in data warehousing and business intelligence.
Its name is coined from the star-shaped output displayed after the organisation of data into dimension tables and fact tables.
It eases data organisation and helps improve the analytical capabilities of the data.

Features of a Star Schema

Simplicity
Since the star schema is simple and easy to comprehend, even individuals with less technical expertise can utilise it.
Flexibility
It makes it simple to add new facts or dimensions without drastically changing the current schema.
Performance
Since fewer joins are required to get data, the decentralised structure speeds up queries.

Dimension Tables Vs. Fact Tables
Both of these concepts are crucial to understanding the star schema approach in data warehousing and business intelligence.

Dimension Tables
Dimension tables offer characteristics that are descriptive of the information in the fact table.
During analysis, they aid in the classification and filtering of data.

Features of Dimension Tables

Primary Keys
Every record in a dimension table is uniquely identified by its main key.
Attributes
These are descriptive fields that provide context (e.g., customer demographics and product name).
Using a retail shop example once more, a product dimension table could resemble this:

Item ID	Item Name	Item Category	Price (Ksh)
203	Bread	Consumables	130
204	Bulb	Electricals	250
205	Cussons	Toiletries	390
206	Hammer	Tools	450

In this case, the information about each product enhances the fact table's analysis of sales transactions.

Fact Tables
A key component of the star schema is fact tables, which include quantitative information that may be examined.

Features of Fact Tables

Foreign Keys
The keys connect to dimension tables and offer context for the measures.
Measures
The provided figures are numerical data that quantify business performance indicators, such as sales income and units sold.

Order ID	Product ID	Customer ID	Quantity sold	All SalesKES
101	1001	10001	220	220000
102	1002	10002	600	90000
103	1003	10003	960	480000
104	1004	10004	2002	260260

Every record in a dimension table is uniquely identified by its main key.
Each row in this table denotes a transaction (a fact), and the columns offer metrics related to that transaction.

Outstanding Differences between Dimension Table and Fact Table

Structure
Dimension tables are narrower with more attributes, whereas fact tables are often bigger with fewer attributes.
Purpose
Dimension tables give events or transactions context, whereas fact tables document them.

Understanding the Snowflakes Schema Concept in Power BI

This is a revised version of the star schema.
A snowflake schema is a multi-dimensional data model in which dimension tables are divided into subdimensions.
Dimension tables under snowflakes schema are organised into sub-dimensions.
Data engineers can divide each dimension table into logical subdimensions in a snowflake structure. Although this increases the complexity of the data model, data analysts may find it simpler to deal with, particularly for some types of data.

Strengths of Snowflakes Schemas in Power BI

Fast data retrieval
Dimensions link to sub-dimesnsions making it easier to retrieve.
Enforces Data Quality
Since data is interlinked, its integrity is reliable.

Weaknesses of Snowflakes Schema in Power BI

Cost Implications on Initial Set up
This is high since the architecture "expands" from the centre.
Rigid Data Model
The data model remains repetitive, hence this cannot be easily remodelled to fit emerging needs.

From the discussed schemas, relationships are created amongst the data dimensions and sub-dimensions, making a database of interconnected data of an organisation.

Good Models are necessary for Accurate reporting and Performance

For easier data analysis, it is important to develop well-thought-out dashboards and trend reports.
These dashboards and trend reports can only be developed using good models for easier visualisations by the necessary decision makers.
Without the development of good models, there will be no accurate reporting and therefore no ease of performance of plans to actualise the KPI's in any organisation.

These are my thoughts from analysing Power BI as a guide to developing models through schemas.
I will be happy when, upon going through this writing, you will be kind enough to share your comments or improvements to my write-up.
Happy learning!
I look forward to hearing from you!
Just let me know 🚀

Microsoft Excel Guide to Beginners using a HR Dataset Example

Kahindi Kevin — Sat, 24 Jan 2026 20:53:08 +0000

Microsoft Excel (Ms Excel) happens to be a key toolset in any data analyst's skillset. It helps any data to be understood with ease. People are able to draw conclusions and make interpretations when it is presented in terms of sums, averages, making comparisons from period to period and through presentations in the form of charts and responsive dashboards.
Ms Excel is represented in rows (vertical), columns (horizontal) and cells where formulas of data manipulation are input.

Data on View Mode on Ms Excel

The above image is a representation of Ms Excel - the rows, columns, cells and sheets of a dataset to be analysed.

Freezing of First Column
This helps the analyst not to lose track of the data that they are analysing. This allows easy manipulation of a large dataset without losing track of the data titles.
The process used to achieve this is as follows:

On the ribbon/ toolbar, click on View
Choose the item called Freeze Panes
For ease of data access, with a set of data titles, select freeze the first column.

Spilling Over Columns
For data titles in the columns spilling over to other cells, rendering some data hidden in view could lead to the data not being well understood. It is good to auto-adjust the entire dataset for easier reading of titles for proper analysis and interpretation.
This can be done through:

Click on Home on the toolbar
Choose Format
Autofit Column Width; or
Click on the row and column intersection
Hover your cursor on the column until a cross with arrows on the left and right appears
Double-click on the area that this cross appears
The column splits are removed all across the dataset.

Removing Duplicates from the Raw Data
This is an important aspect that every person who intends to manipulate data for it to be easily understood needs to work on.
This will ensure that the raw data does not have any repetitions that could make the analysis poor.
The procedure used to achieve this is as follows:

Select the entire document; Ctrl + A
Select Data on the toolbar
Under data tools, select remove duplicates

Using Operator Functions in Ms Excel

These operators are used to help users analyze raw data for easier interpretation.
They include:

Addition (+)
Subtraction (-)
Division (/)
Multiplication (*)
Exponent (^)

The above operators are used to get averages/ mean, MAX and MIN values, counts/ sum and predictions from raw data with ease.

ADDITION OPERATOR

This helps to find the sum of a set of data.
This can be done using COUNTS, COUNTIF, SUM and SUMIFS.

=SUMIF(E2:E877,">100000") - For salaries equal to or above  $100000

=COUNTIF(G2:G877,"50") - For ages equal to or above 50 years

MAX AND MIN VALUES

This is one of the ways to analyse data.
We end up arriving at the maximum/ highest or minimum/ lowest values of raw data.

=MAX(D2:D22) - the highest value - e.g., age, salary, fares

=MIN(F2:F22) - the lowest value - e.g., age, salary, fares

Using Lookup Functions in Ms Excel
These are divided into three:

VLOOKUP Function

This searches for data of relevance on the vertical items.

HLOOKUP Function

This searches for data of relevance on the horizontal items.

XLOOKUP or Index and Match Function

This indexes the column with the information that you want to know.

VLOOKUP in Use

Illustration One
How much is employee ID 10520s bonus?

=VLOOKUP(10520,A1:P877,15,FALSE)

 The answer is $630.98

HLOOKUP in Use

Illustration Two
What is the marital status of ID 10102?

=HLOOKUP(10102,A1:AGS26,16,FALSE)

The answer to this is MARRIED.

XLOOKUP in Use

Illustration Three
What is the name/ID of employee earning $77498?

=XLOOKUP(77498,E2:E877,B2:B877,"NOT FOUND")

The answer to this is John.

PIVOTTABLES
Used to develop tables that interpret raw data to set conditions.

To create a suitable pivot table, this is the procedure:

Click Insert on the toolbar and select Pivot Table

Click OK to proceed.

On the right side under PivotTable Fields, drag and drop data title items into rows, column or filter sections to meet your analysis needs.

Developing Charts on Ms Excel for ease of Interpretation of Raw Data

This is used to interpret the analysed data.
Charts come in various forms and shapes - bars, pie chart and line charts.
The procedure used to get charts is as follows:
Click on the developed PivotTable
Pivot chart analyze
Pivot chart
Select the appropriate chart type from your available options
The developed PivotTable for analysis

Click on PivotChart on the Toolbar

Select the Chart Type to be displayed

For simple data analytics, Microsoft Excel is a helpful tool. It facilitates data organization, basic analysis, and straightforward result presentation. Ms Excel is a good starting place for novices learning advanced data analysis.

📌To all learners, keep on practising!

Git for Beginners: Push, Pull, and Track Your Code Without Fear

Kahindi Kevin — Sat, 17 Jan 2026 20:53:46 +0000

Git may initially seem difficult to someone who is unfamiliar with software development.
Although they sound scary, these terms are used in regular developer operations including:

Pull code
Push code
Track changes

I'll explain Git in an easy-to-understand manner in this post so you can manage your code.

What Version Control Is and Its Importance

Version Control makes it easier to monitor changes made to files over time. Git maintains a complete history of your project.

Importances of Git and Version Control In Development

Collaborate on assignments with peers without overwriting
Look into projects to find out who improved them
Revert to previous forms of work

Installing Git and Set up Procedures
For Windows users:

Download Git
First, make sure Git is installed:

git --version

Git is installed, and a version number is displayed.
Set up your identity through:

git config --global user.name "Your Name"
git config --global user.email "youremail@example.com"

Connecting Git to your Github Account
After you have set up your identity, proceed to generate an SSH key

# Create a new SSH key
ssh-keygen t ed25519 -C "your email"
# Press ENTER to define default location
# Copy the key to be used later on
# For Windows users 
C:\Users\YOUR_USERNAME\.ssh\id_ed25519.pub

Connect SSH key to Github

Go to Github
Click on your profile on the top right, choose settings
On the left side of the page, select SSH and GPG keys
Click on "New SSH key"
The key that you had earlier copied, paste it on the pop up and give it a name
Click add SSH key Test your Generated key

ssh -T git@github.com

An authentication key is generated that confirms the connection.

Tracking Changes: Add and Commit
These are the ways in which Git tracks changes through committing and staging.

A. Add Files to the Staging Area

Git detects when you create or edit files, but it does not immediately save them.
In order to stage a file:

git add filename.txt

To stage every work that is in progress:

git add .

B. Commit changes

A commit is a snapshot of your project at a specific point in time.

git commit -m "Add SQL project structure"

Push and Pull Code
For you to better understand the push and pull concept, it is best to introduce the aspect of repositories which are divided into either local or remote repository.

Local REPOSITORY

This lives on your PC.

Remote REPOSITORY

This lives on a different platform like Github.
Code is written locally, committed, and then pushed to the remote repository for public viewing.

Connecting to a Remote Repository
After creating a repository on Github:

git remote add origin https://github.com/username/repository-name.git

Check that the connection works:

git remote -v

Pushing Code to Github

Once you upload any project your local repository commits to the remote repository:

git push origin main

origin is the remote name
main is the branch name Subsequently, your code will appear on Github.

Pulling Code from a Remote Repository
In the event that changes were made on GitHub (by your collaborator on a project), pull them to your local machine:

git pull origin main

This keeps your local project up to date.

Git and Github are key basics in your coding journey.
Embrace their capabilities and diversities and your journey will be unstoppable 🚀