SQL JOINS

arno kiru — Sat, 24 May 2025 23:34:56 +0000

What is a join?! This is a clause that is used to combine rows from 2/more tables based on a related column between them. There are several types of joins. They include;
-Inner join

Right join
Left join
Full join/ full outer join
self-join. All the fore-mentioned are types of joins that have unique characteristics to one another that set them apart and provide thereby a different result.

Inner join

This a type of join that when called returns records that have matching values in both tables, each table that is, “table1” and “table2”. The inner join is the default type of join. Below, I give the format that it should take and an example in case.

SELECT column_name(s)
FROM table1
INNER JOIN table1 on table2.column_name=table2.column_name;

The above syntax returns matching values that are in both table1 and table2. To note is that either inner join or join that is passed on the phrase a record will be returned as a result of inner join being the default join.

Left Join

The left join returns all records from the left table(table1) and matching record from right table(table2). It follows the following syntax;

SELECT column_name(s)
FROM table1
Left join table2 on table1.column_name=table2.column_name;

This now thereby returns all the records from table1 and matching values from table2. In the instance that there is a lack of matching records in table2, then, only records from table1 are returned.

Right join

The right join returns all records from the right table(table2) and matching records in the left table(table1). Of essence to note is that in the instance where there are no matching records from the left table(table1), then there are no columns or rather records that’ll be returned from the left table. It follows the following syntax;

SELECT column_name(s)
FROM table1
Right join table2 on table1.column_name=table2.column_name;

This therefore returns all records from table2 and matching records from table1. In the case there are no matching values from table1 then there will be no records from table1 returned, only table2 records are thereby returned.

Outer join

The outer join also known as the full outer join returns all records when there is a match in either left(table1) or right(table2) records. This type of join returns all records as long as there is a matching column so to say in either of the tables. It follows the following syntax;

SELECT column_name(s)
FROM table1
OUTER JOIN table2 
ON table1.column_name = table2.column_name;

This phrase returns all rows from both the left and right table provided there’s a match between the tables and thus they are combined as a result. In the instance there happens to be a row in the left table that has no matching row in the right table, then the right table will have null values in the result.
The outer join is useful when you need to see all records from both tables regardless of whether they have a match or not.

To note is the difference between the full outer join and the full inner join. The inner join returns only records where the join condition is met in both and where a record in on table doesn’t have a corresponding match in the other table then that record is excluded from the result unlike in the case of an outer join where it would return “NULL”.

In conclusion, joins are a vital part in ensuring the success of data analysis. They bind relational data together and are thus important for the following uses so to say;

Connecting and transforming tables.
Handling data inconsistencies.
Extracting useful insights with precision.

Mastering SQL joins is of great essence. This ensures that one has meaningful results through their interactions with relational databases, but not only limited to but including joins mastery.

INTRODUCTION TO DATA ANALYTICS

arno kiru — Mon, 31 Mar 2025 07:19:44 +0000

WHAT IS DATA?
Data is a collection of factual information that describes quantity, quality, facts, statistics or basic units of meaning. It usually is used as a basis for reasoning, discussion or calculation. This then begs the question what is data analysis? Data analysis is the examination of data to identify patterns, trends and insights. These insights are fundamental in our day to day life scenarios dependant on the nature of data and field in which they are of relevance and to the respective stakeholders.

TYPES OF DATA
There are two types of data, qualitative and quantitave to be precise.

DATA ANALYTICS
The use of tools and processes to examine raw datasets by transforming and organizing them to identify patterns and derive insights to answer specific questions and hence make informed decisions based on the insights.This is done by a well trained and skilled data analyst. Roles of a data analyst includes the following:

Data collection: Gathering data from various sources including databases, spreadsheets and other platforms.
Data cleaning: Ensuring data accuracy, consistency, and relevance by identifying and addressing issues like missing values, duplicates, and inconsistencies.
Data analysis: Use of statistical methods, data mining techniques and analytical tools to identify patterns, trends and insights in the data.
Data interpretation: Drawing meaningful info from the analysis that explains the findings and thereby translate them into recommendations.
Data visualization: Creating charts, graphs and dashboards to communicate insight to the stakeholders.
Reporting and communicating: Preparing reports, presentations and other material to share findings and recommendations with both technical and non-technical audiences.

TOOLS
For a data analyst to perform these roles aptly, then he/she should be able to utilize the necessary tools specific to the needs of a project being undertaken. However, before diving deeper let's get the definition of a tool.
A tool is a software application, better still to a layman, a computer program that helps process, analyse, visualize and interpret data.These tools help the data analyst carry out his/her roles effectively.
The're various tools used in the data analytics niche, just to mention but a few, power BI, excel, SQL, Python, R programming and Spark among others.
In the next articles I will be discussing these tools and matters data analysis in greater depth.