<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <channel>
    <title>DEV Community: Frankline Kibet</title>
    <description>The latest articles on DEV Community by Frankline Kibet (@super_b8c82b4153dee9fab1c).</description>
    <link>https://dev.to/super_b8c82b4153dee9fab1c</link>
    <image>
      <url>https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3972124%2Fdf7a6bf9-4cbd-4b70-98dd-0393b45595dc.png</url>
      <title>DEV Community: Frankline Kibet</title>
      <link>https://dev.to/super_b8c82b4153dee9fab1c</link>
    </image>
    <atom:link rel="self" type="application/rss+xml" href="https://dev.to/feed/super_b8c82b4153dee9fab1c"/>
    <language>en</language>
    <item>
      <title>SQL JOINS WITH EXAMPLES OF EACH</title>
      <dc:creator>Frankline Kibet</dc:creator>
      <pubDate>Wed, 16 Sep 2026 03:07:38 +0000</pubDate>
      <link>https://dev.to/super_b8c82b4153dee9fab1c/sql-joins-with-examples-of-each-21ol</link>
      <guid>https://dev.to/super_b8c82b4153dee9fab1c/sql-joins-with-examples-of-each-21ol</guid>
      <description>&lt;p&gt;A join combines rows from two or more tables based on a related column between them. Instead of running separate queries and manually matching up results yourself, a join does that matching for you, in a single query&lt;br&gt;
When working with databases, your data is often stored in more than one table. This is way join are important &lt;/p&gt;

&lt;p&gt;*&lt;em&gt;TYPES OF JOINS *&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;1.&lt;em&gt;** Inner join**&lt;/em&gt;&lt;br&gt;
This is a type of join that returns only the rows that have a match in both tables.&lt;br&gt;
example&lt;br&gt;
&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fq5xald1yypgj79jgnyba.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fq5xald1yypgj79jgnyba.PNG" alt=" " width="730" height="144"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;em&gt;&lt;strong&gt;Left jOIN&lt;/strong&gt;&lt;/em&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;Returns all rows from the left table plus matching rows from the right table.&lt;/p&gt;

&lt;p&gt;Where there's no match the right table's columns come back as null&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fbrp1dktx5f6zak6safe9.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fbrp1dktx5f6zak6safe9.PNG" alt=" " width="800" height="514"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;3*&lt;em&gt;_.Right join _&lt;/em&gt;*&lt;br&gt;
Returns all rows from the right table plus matching rows from the left.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F4ebjo8yib2vrjmhiq9ql.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F4ebjo8yib2vrjmhiq9ql.PNG" alt=" " width="738" height="424"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;4.&lt;strong&gt;_ Full outer join _&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Return all row from all table.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F3ktrmvzn3gq10nent4qj.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F3ktrmvzn3gq10nent4qj.PNG" alt=" " width="725" height="143"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I used it when when you need a complete view of the table example like in this scenario above I need to check the results table and the subject table to identify areas of improvement.&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;
&lt;strong&gt;_self join _&lt;/strong&gt; &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;This is a practical&lt;br&gt;
use of self join that really help be in my project. it let me pair each book with it author with ease. &lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Flkf0pnco1qty4yiw9m60.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Flkf0pnco1qty4yiw9m60.PNG" alt=" " width="745" height="520"&gt;&lt;/a&gt; &lt;/p&gt;

</description>
      <category>beginners</category>
      <category>database</category>
      <category>sql</category>
      <category>tutorial</category>
    </item>
    <item>
      <title>DDL and DML WHAT ARE THEY?</title>
      <dc:creator>Frankline Kibet</dc:creator>
      <pubDate>Mon, 14 Sep 2026 12:33:56 +0000</pubDate>
      <link>https://dev.to/super_b8c82b4153dee9fab1c/ddl-and-dml-what-are-they-2g98</link>
      <guid>https://dev.to/super_b8c82b4153dee9fab1c/ddl-and-dml-what-are-they-2g98</guid>
      <description>&lt;p&gt;DDL &lt;br&gt;
DDL  is an abbreviation for data definition language  used to define, modify, and manage the structure of a database.&lt;br&gt;
Common DDL commands &lt;br&gt;
Create - Creates a new table, database, index, or view &lt;br&gt;
Example &lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fjwgsk70rh0z0pomg7vac.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fjwgsk70rh0z0pomg7vac.PNG" alt=" " width="331" height="120"&gt;&lt;/a&gt;&lt;br&gt;
&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fprzc5dv0lbzcuss1iya4.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fprzc5dv0lbzcuss1iya4.PNG" alt=" " width="497" height="141"&gt;&lt;/a&gt;&lt;br&gt;
Alter&lt;br&gt;
Used to modify the exiting table &lt;br&gt;
Alter can be  used to:&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;&lt;p&gt;Remove column&lt;/p&gt;&lt;/li&gt;
&lt;li&gt;&lt;p&gt;Add column &lt;/p&gt;&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fcdhbjvycw5mtbsqvuoe3.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fcdhbjvycw5mtbsqvuoe3.PNG" alt=" " width="498" height="104"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Truncate – it empties all rows from a table but keeps its structure&lt;/p&gt;

&lt;p&gt;truncate table safari.student;&lt;/p&gt;

&lt;p&gt;DML&lt;br&gt;
DML  is an abbreviation  for data manipulation language,&lt;br&gt;
These are the commands that work with the actual data stored inside tables exmple adding rows, reading rows, updating values, and deleting rows. &lt;br&gt;
Example of commands &lt;br&gt;
Select  reads data from one or more tables&lt;br&gt;
Example:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Ft106g7iz8jgcjxt2id46.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Ft106g7iz8jgcjxt2id46.PNG" alt=" " width="356" height="104"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;2.Insert&lt;br&gt;
Adds new rows to a table&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F0btl4hu2ocbdt1shbh85.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F0btl4hu2ocbdt1shbh85.PNG" alt=" " width="686" height="85"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Update 
Is used to modifies existing rows.
example&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F28ukzmltoh2z97xzqgt1.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F28ukzmltoh2z97xzqgt1.PNG" alt=" " width="526" height="61"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Delete
is used to removes rows from a table&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fgz4nyk652tc03k9b84u5.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fgz4nyk652tc03k9b84u5.PNG" alt=" " width="681" height="70"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Diffence&lt;br&gt;
The main difference: DDL defines and changes the structure of a table  using  commands like CREATE, ALTER, DROP while  DML works with the actual data inside that structure  commands like SELECT, INSERT, UPDATE, DELETE.&lt;/p&gt;

</description>
      <category>beginners</category>
      <category>database</category>
      <category>sql</category>
      <category>tutorial</category>
    </item>
    <item>
      <title>ANALYZING HOTEL BOOKING DATA: A TEMBO HOTEL SQL + POWER BI CASE STUDY</title>
      <dc:creator>Frankline Kibet</dc:creator>
      <pubDate>Mon, 14 Sep 2026 04:29:45 +0000</pubDate>
      <link>https://dev.to/super_b8c82b4153dee9fab1c/analyzing-hotel-booking-data-a-tembo-hotel-sql-power-bi-case-study-239l</link>
      <guid>https://dev.to/super_b8c82b4153dee9fab1c/analyzing-hotel-booking-data-a-tembo-hotel-sql-power-bi-case-study-239l</guid>
      <description>&lt;p&gt;Tembo Hotel had been tracking all of their bookings in Excel, but the data had grown messy over time  inconsistent formatting, duplicate entries, and mixed date formats made it impossible to get reliable answers out of it. I was tasked with cleaning the dataset and turning it into something the CEO could actually use to understand the business.&lt;/p&gt;

&lt;p&gt;Here's how I approached it, from raw CSV to a working Power BI dashboard.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Foswwpbjzqdm9etjbigc1.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Foswwpbjzqdm9etjbigc1.PNG" alt=" " width="499" height="360"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The cleaning process started by deleting duplicates .&lt;br&gt;
&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fmidwzs79hx00k8q06fmx.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fmidwzs79hx00k8q06fmx.PNG" alt=" " width="800" height="333"&gt;&lt;/a&gt; &lt;/p&gt;

&lt;p&gt;Then standardization values and trimming to remove extra space.&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fst8i6kazqm7j58wfli4u.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fst8i6kazqm7j58wfli4u.PNG" alt=" " width="800" height="373"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;the changing the data format and data type from text to data &lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F6e1kiq6pkcnuad41nlcp.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F6e1kiq6pkcnuad41nlcp.PNG" alt=" " width="698" height="504"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;after the cleaning process is over I loaded the clean data on table which I will use for analysis.&lt;br&gt;
&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Flrquzjk7umovy3awrdmd.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Flrquzjk7umovy3awrdmd.PNG" alt=" " width="607" height="150"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;The CEO wanted to know about his business . Analysis started by answering   major business questions&lt;br&gt;
&lt;strong&gt;ANALYSIS&lt;/strong&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Revenue analysis &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fu5s28ab2x02ag5zv74xu.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fu5s28ab2x02ag5zv74xu.PNG" alt=" " width="799" height="373"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Guest insighs &lt;/li&gt;
&lt;li&gt;staff performance&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fx4cdux4jbqr1meoym8eu.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fx4cdux4jbqr1meoym8eu.PNG" alt=" " width="800" height="380"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Cancellation and revenue lost &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;One of the more important findings: a meaningful share of potential revenue was lost specifically to cancellations, concentrated in certain room types more than others.&lt;br&gt;
&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Ff6cldnakwpa8frgng2ob.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Ff6cldnakwpa8frgng2ob.PNG" alt=" " width="703" height="181"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;I created it as view so that it so that it could be easy to import then on power bi for visulation &lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Presenting the outcome&lt;/strong&gt; &lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;Dashboard
&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fev7uc9snsdep78zv4r68.PNG" alt=" " width="800" height="371"&gt;
2.The revenue analysis 
&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fls8kii2k5ypuwd1n7e5p.PNG" alt=" " width="799" height="363"&gt;
&lt;/li&gt;
&lt;li&gt;Guest insight &lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F03hwif5hsyfjx8fowg78.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F03hwif5hsyfjx8fowg78.PNG" alt=" " width="800" height="372"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;ol&gt;
&lt;li&gt;cancelation and revenue lost on it .&lt;/li&gt;
&lt;/ol&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fyf4xistdfygpuq244iu8.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fyf4xistdfygpuq244iu8.PNG" alt=" " width="800" height="370"&gt;&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;REPORT&lt;/p&gt;

&lt;p&gt;&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Faxbhrepe04zt3o2ebp3b.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Faxbhrepe04zt3o2ebp3b.PNG" alt=" " width="752" height="429"&gt;&lt;/a&gt;&lt;br&gt;
&lt;a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fvhthsuwa05ivfkqc4xqk.PNG" class="article-body-image-wrapper"&gt;&lt;img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fvhthsuwa05ivfkqc4xqk.PNG" alt=" " width="762" height="421"&gt;&lt;/a&gt;&lt;/p&gt;

</description>
      <category>analytics</category>
      <category>data</category>
      <category>database</category>
      <category>sql</category>
    </item>
    <item>
      <title>Data</title>
      <dc:creator>Frankline Kibet</dc:creator>
      <pubDate>Mon, 07 Sep 2026 11:58:42 +0000</pubDate>
      <link>https://dev.to/super_b8c82b4153dee9fab1c/data-1f4f</link>
      <guid>https://dev.to/super_b8c82b4153dee9fab1c/data-1f4f</guid>
      <description></description>
    </item>
  </channel>
</rss>
