Forem: Jedidah Ondiso

CONNECTING POWER BI TO SQL DATABASE

Jedidah Ondiso — Mon, 23 Mar 2026 19:53:29 +0000

POWER BI
Power BI is a data visualization and business intelligence tool developed by Microsoft. It helps convert raw data into clear and meaningful insights through interactive charts, reports and dashboards.

Key reasons for connecting Power BI to databases include:

Real-time Data Access: Enables monitoring of live business metrics, such as sales trends or customer behavior, directly from sources like SQL Server.
Handling Large Data Volumes: Unlike Excel, databases efficiently store and manage massive datasets, which Power BI can query directly.
Data Integrity and Security: Connecting directly to a SQL database ensures that reports are based on a trusted "single version of truth".
Automated Reporting: Streamlines the data analysis process by enabling automated updates, reducing manual effort, and improving efficiency.
Enhanced Visualization and AI: Allows complex data to be visualized through interactive dashboards, with embedded AI to identify trends and make predictions.
Data Democratization: Allows non-technical business users to query and analyze data, decreasing reliance on specialized IT staff.

Importance of SQL databases for storing and managing analytical data.

Powerful Querying Capabilities: SQL (Structured Query Language) is a powerful, standardized language for retrieving and manipulating data. Analysts can use it to perform simple data extraction as well as complex operations.

Handling Large Datasets Efficiently: SQL databases are optimized to manage and process millions or billions of rows quickly.

Integration with BI and Analytics Tools: SQL serves is the backbone for many popular BI and data science tools like Tableau and Power BI.

How Power BI connects to a local PostgreSQL Database
Open Power BI Desktop and navigate to the Home ribbon to get data then chose Postgresql and connect.

In the PostgreSQL database dialog box, enter the server details:
Server: Localhost. You can include the port number (default is 5432) in the format localhost:5432.
Database: Enter the exact name of your specific database.

Select the Database authentication method and enter your PostgreSQL Username and Password.

Once connected, it will load the tables in your database into Power BI, select the tables you would like to use, and click load your data.

How to connect Power BI to a cloud database such as Aiven PostgreSQL.

Select your service (e.g., PostgreSQL).

Under the Overview tab, find the Connection Information section.
In the same Connection Information section, Aiven provides downloadable SSL files:
ca.pem → Certificate Authority file
service.cert → Client certificate
service.key → Client private key
Download these files securely to your local machine or application environment.

🔗 Including the Certificate in Your Connection
Depending on the client library, you’ll reference the SSL files when connecting:
PostgreSQL

conn = psycopg2.connect(
host="HOST",
port="PORT",
dbname="DBNAME",
user="USERNAME",
password="PASSWORD",
sslmode="require",
sslrootcert="ca.pem",
sslcert="service.cert",
sslkey="service.key"

🔒 Why SSL Certificates Are Required

Encryption: SSL ensures all data exchanged between your application and the database is encrypted, preventing eavesdropping.
Authentication: Certificates verify that you’re connecting to the legitimate Aiven server, not an imposter.
Integrity: SSL prevents tampering with data in transit, ensuring queries and results aren’t altered. In short, SSL certificates safeguard sensitive information (like credentials and business data) against interception and unauthorized access.

JOINS AND WINDOWS FUNCTIONS IN SQL

Jedidah Ondiso — Mon, 02 Mar 2026 13:20:22 +0000

STRUCTURED QUERY LANGUAGE

SQL (Structured Query Language) is the standard programming language designed for managing, manipulating, and retrieving data stored in relational databases. Developed in the 1970s, it is used to interact with database systems to perform tasks such as updating records, deleting data, creating new tables, and managing user permissions.

JOINS FUNCTION
JOIN is a clause used to combine rows from two or more tables in a relational database, based on a related column (or join key) between them. This is a fundamental operation in SQL that allows data to be retrieved from multiple, logically related tables as a single, unified result set.

TYPES OF JOINS
1. Inner Join
Inner join statement joins two tables based on a common column and selects rows that have matching values in these columns.

-- join Customers and Orders tables with their matching fields customer_id

SELECT Customers.customer_id, Orders.item
FROM Customers
INNER JOIN Orders
ON Customers.customer_id = Orders.customer_id;

2. Right Join
Right join statement joins two tables based on a common column. It selects records that have matching values in these columns and the remaining rows from the right table.

SELECT Customers.customer_id, Customers.first_name, Orders.item
FROM Customers
RIGHT JOIN Orders
ON Customers.customer_id = Orders.customer_id;

3. Left Join
Left join statement combines two tables based on a common column. It then selects records having matching values in these columns and the remaining rows from the left table.

SELECT Customers.customer_id, Customers.first_name, Orders.item
FROM Customers
LEFT JOIN Orders
ON Customers.customer_id = Orders.customer_id;

4. Full Outer Join
Full outer join statement joins two tables based on a common column. It selects records that have matching values in these columns and the remaining rows from both of the tables.

SELECT Customers.customer_id, Customers.first_name, Orders.item
FROM Customers
FULL OUTER JOIN Orders
ON Customers.customer_id = Orders.customer_id;

5. Cross Join
Cross join statement return the Cartesian product of rows from the tables in the join. This is the result of combining each row from one table to each row of the second table.

SELECT * 
FROM Customers
CROSS JOIN Orders;

The query combines each row of the Customers table with each row of the Orders table.

Joins summary in a diagramn

WINDOWS FUNCTION

A window function in SQL is a type of function that performs a calculation across a specific set of rows (the 'window' in question), defined by an OVER() clause.

Window functions use values from one or multiple rows to return a value for each row, which makes them different from traditional aggregate functions, which return a single value for multiple rows.

SELECT column_1, column_2, column_3, function()
OVER (PARTITION BY partition_expression ORDER BY order_expression) as output_column_name
FROM table_name;

In this syntax:

The SELECT clause defines the columns you want to select from the table_name table.
Function() is the window function you want to use.
The OVER clause defines the partitioning and ordering of rows in the window.
The PARTITION BY clause divides rows into partitions based on the specified partition_expression; if the partition_expression is not specified, the result set will be treated as a single partition.
The ORDER BY clause uses the specified order_expression to define the order in which rows will be processed within each partition; if the order_expression is not specified, rows will be processed in an undefined order.

Finally, output_column_name is the name of your output column.
These are the key SQL window function components. One more thing worth mentioning is that window functions are applied after the processing of WHERE, GROUP BY, and HAVING clauses. This means you can use the output of your window functions in subsequent clauses of your queries.

The OVER() clause
Is essentially the core of window functions. It determines the partitioning and ordering of a rowset before the associated window function is applied. The OVER() clause can be applied with functions to compute aggregated values such as moving averages, running totals, cumulative aggregates, or top N per group results.

The PARTITION BY clause
Is used to partition the rows of a table into groups. This comes in handy when dealing with large datasets that need to be split into smaller parts, which are easier to manage. PARTITION BY is always used inside the OVER() clause; if it is omitted, the entire table is treated as a single partition.

The ORDER BY clause
It determines the order of rows within a partition; if it is omitted, the order is undefined. For instance, when it comes to ranking functions, ORDER BY specifies the order in which ranks are assigned to rows.

Frame specification
In the same OVER() clause, you can specify the upper and lower bounds of a window frame using one of the two subclauses, ROWS or RANGE. The basic syntax for both of these subclauses is essentially the same:

ROWS BETWEEN lower_bound AND upper_bound
RANGE BETWEEN lower_bound AND upper_bound

And in some cases, they might even return the same result. However, there's an important difference.

In the ROWS subclause, the frame is defined by beginning and ending row positions. Offsets are differences in row numbers from the current row number.

As opposed to that, in the RANGE subclause, the frame is defined by a value range. Offsets are differences in row values from the current row value.

Types of SQL window functions
Window functions in SQL Server are divided into three main types: aggregate, ranking, and value functions. Let's have a brief overview of each.

Aggregate window functions

AVG() returns the average of the values in a group, ignoring null values.
MAX() returns the maximum value in the expression.
MIN() returns the minimum value in the expression.
SUM() returns the sum of all the values, or only the DISTINCT values, in the expression.
COUNT() returns the number of items found in a group.
STDEV() returns the statistical standard deviation of all values in the specified expression. -** STDEVP() **returns the statistical standard deviation for the population for all values in the specified expression.
VAR() returns the statistical variance of all values in the specified expression; it may be followed by the OVER clause.
VARP() returns the statistical variance for the population for all values in the specified expression.

Ranking window functions

-ROW_NUMBER() assigns a unique sequential integer to rows within a partition of a result set.

RANK() assigns a unique rank to each row within a partition with gaps in the ranking sequence when there are ties.
DENSE_RANK() assigns a unique rank to each row within a partition without gaps in the ranking sequence when there are ties.
PERCENT_RANK() calculates the relative rank of a row within a group of rows.
NTILE() distributes rows in an ordered partition into a specified number of approximately equal groups.

Value window functions

LAG() retrieves values from rows that precede the current row in the result set.
LEAD() retrieves values from rows that follow the current row in the result set.
FIRST_VALUE() returns the first value in an ordered set of values within a partition.
**LAST_VALUE() **returns the last value in an ordered set of values within a partition.
NTH_VALUE() returns the value of the nth row in the ordered set of values.
CUME_DIST() returns the cumulative distribution of a value in a group of values.

HOW ANALYSTS TRANSLATE MESSY DATA,DAX, AND DASHBOARD INTO ACTION USING POWER BI

Jedidah Ondiso — Sun, 08 Feb 2026 20:53:46 +0000

Power BI is a Microsoft business analytics platform that transforms raw data from various sources into interactive, actionable insights and stunning visualizations. It serves as a self-service BI tool for data modeling, reporting, and sharing dashboards across organizations, allowing users to make data-driven decisions faster.

1. Cleaning and Modeling Messy Data

Data Integration: Combining information from multiple sources (databases, spreadsheets, APIs) that may have different formats.

Cleaning and modeling messy data in Power BI is primarily achieved using Power Query Editor to transform raw data into a structured format before loading it. Essential techniques include removing nulls/duplicates, changing data types, splitting columns, pivoting/unpivoting, and establishing a star schema (fact/dimension tables) for optimized reporting, ensuring data accuracy and performance.

Data Modeling: Structuring tables with relationships (star schema or snowflake schema) so that analysis is efficient and reliable.
Business Context: Analysts start by clarifying the business question to ensure the model supports decision-making

2. Using DAX (Data Analysis Expressions)****
Calculated Columns: Creating new columns (e.g., age groups, profit margins).

Measures: Defining dynamic calculations like totals, averages, year-to-date growth.

Advanced Logic: Applying filters, conditional logic, and time intelligence functions to uncover trends.
Flexibility: DAX allows analysts to go beyond raw numbers and create meaningful KPIs that align with business goals

3. Building Dashboards for Action
Interactive Visuals: Charts, maps, and matrices that let users drill down into details.

Storytelling: Dashboards are designed to highlight patterns, anomalies, and opportunities.

Decision Support: Leaders can quickly see which areas need attention, whether operational (daily performance), tactical (monthly trends), or strategic (long-term growth).

Avoiding Pitfalls: A polished dashboard is only useful if the underlying data is clean and modeled correctly; otherwise, it risks misleading decision-makers.

SCHEMAS AND DATA MODELLING IN POWER BI

Jedidah Ondiso — Sun, 01 Feb 2026 20:37:06 +0000

SCHEMA
A schema refers to the structure and organization of data within a data model. Schemas define how data is connected and related within the model, influencing the efficiency and performance of data queries and reports.

DATA MODELLING
Data modeling is process of structuring and relating your data tables to create a logical, efficient semantic model, enabling accurate analysis and powerful reports by defining relationships, creating DAX calculations (measures), and organizing data, often using star or snowflake schemas within Power Query and Model View in Power BI Desktop.

TYPES OF SCHEMAS IN POWER BI
Star Schema
The star schema is a simple and commonly used schema in data warehousing. It consists of a central fact table surrounded by dimension tables, forming a star-like pattern.

Structure
The central fact table contains quantitative data (e.g., sales), while the dimension tables hold descriptive attributes related to the facts (e.g. Employee, Date, Territory).

Use Cases

Star schemas are ideal for straightforward reporting and querying. They are efficient for read-heavy operations, making them suitable for dashboards and summary reports.

Benefits

Faster query performance, simplified relationships (one-to-many), and better usability for DAX calculations.

Snowflake Schema
This is a normalized version of the star schema. In this design, dimension tables are further divided into related tables, resulting in a more complex structure.

Use Cases
Snowflake schemas are used in scenarios requiring detailed data models and efficient storage. They are beneficial when dealing with large datasets where data redundancy needs to be minimized

Benefits
Reduced data redundancy and improved data integrity.

Drawbacks: Slower query performance and more complex relationships.

Why Good Modeling is Critical for Performance

Optimized Query Speed

A well-structured model (e.g., using star schemas with fact and dimension tables) organizes data for faster retrieval. It reduces unnecessary complexity, allowing BI tools to run queries faster.

Reduced Resource Usage
Good modeling minimizes redundant data and streamlines relationships, which keeps the model lean, reduces memory usage, and ensures smoother, faster user experiences.
Scalability
A robust model is designed to grow with the business. It accommodates increasing data volumes and new data sources without requiring a full redesign or significant performance degradation.
Efficient Indexing
Proper physical modeling allows for the implementation of effective indexing and partitioning strategies, allowing the database to scan only necessary data rather than the entire dataset.

INTODUCTION TO MICROSOFT EXCEL FOR DATA ANALYSIS

Jedidah Ondiso — Sun, 25 Jan 2026 20:15:25 +0000

WHAT IS EXCEL?
Microsoft Excel is a powerful spreadsheet program developed by Microsoft, designed for organizing, calculating, analyzing, and visualizing data using a grid of rows and columns.

DEFINITION OF TERMS IN EXCEL
Excel Terms You Will Encounter (Excel & Data Analytics)
Here are simple explanations of key terms used in this article:

*Cell * - A single box in Excel where a row and column meet (example: A1).
Column - A vertical group of cells labeled with letters (A, B, C…).
Row - A horizontal group of cells labeled with numbers (1, 2, 3…).
Worksheet - One page inside an Excel file where you enter and analyze data.
Workbook - An Excel file that contains one or more worksheets.
Dataset - A collection of related data arranged in rows and columns.
Formula - An equation used in Excel to perform calculations (example: =A1+B1).
*Function *- A built-in formula in Excel that performs a specific task (example: SUM, AVERAGE).
Range - A group of selected cells (example: A1:A10).
Filter - A tool used to display only the data that meets certain conditions.
Sort - Arranging data in a specific order (A–Z, smallest to largest, etc.).
Pivot Table - A tool that summarizes and analyzes large datasets by grouping and calculating data automatically.
Chart - A visual representation of data, such as a bar chart or line graph.
Dashboard - A single page that shows key data insights using charts, numbers, and summaries.
Data Cleaning - The process of fixing or organizing data so it is accurate and ready for analysis.

WHY USE EXCELFOR DATA ANALYSIS?

Easy to learn: No coding required.
Widely available: Comes with most Microsoft Office packages.
Versatile: Handles everything from simple calculations to complex data models.
Visualization: Built-in charts and graphs make data easier to understand.

Excel Functions (Basic Calculations)
Functions are built-in formulas that help you perform calculations easily.

SUM – Add Numbers Adds a range of numbers.

=SUM(B2:B6)

This adds all values from cell B2 to B6.

AVERAGE – Find the Mean Calculates the average of numbers.

=AVERAGE(C5:C10)

3.COUNT – Count Numbers
Counts how many cells contain numbers.

=COUNT(A2:A5)

Use case: Number of transactions recorded.

COUNTA – Count Non-Empty Cells
Counts cells that are not empty (text or numbers).

=COUNTA(B2:B5)

Use case: Number of customers or products listed.

IF – Logical Function Performs a test and returns different results depending on the outcome.

=IF(B2>=50, "Passed", "Failed")

This checks if the value in B2 is greater than or equal to 50.
** Use case** Categorizing sales as High/Low, Pass/Fail

Data Cleaning in Excel
Before analysis, data often needs cleaning.
Common cleaning tasks:

Removing duplicates Data -> Remove Duplicates
Sorting data Data -> Sort A to Z
Filtering data Data -> Filter

Pivot Tables (Powerful Data Summaries)
Pivot Tables help you summarize large data quickly without complex formulas.

You can answer questions like:

Total sales per product
Sales per month
Quantity sold by category
Steps to create a Pivot Table:
Select your data
Go to Insert -> PivotTable
Choose where to place it
Drag fields into Rows, Columns, and Values
Example:

Rows -> Product
Values -> Sum of Total Sales
Excel will automatically calculate total sales for each product.

Pivot Charts in Excel
Charts help turn numbers into visuals that are easier to understand.

Common chart types:
Column Chart – Compares categories
Line Chart – Shows trends over time
Pie Chart – Shows proportions
How to insert a chart:
Select your data
Click Insert
Choose a chart type
Example: Sales by product displayed as a column chart.

Dashboards in Excel
A dashboard is a single page that shows key insights using charts, numbers, and summaries.

A simple Excel dashboard might include:

Total Sales (big number)
Sales by Product (chart)
Sales by Month (line chart)
Top Product (text or card)
Dashboards help decision-makers understand performance quickly.

Tools used in dashboards:
Pivot Tables
Charts
Slicers (interactive filters)

Final Thoughts
Excel is more than just a spreadsheet—it’s a gateway into the world of data analysis. With a little practice, you’ll be able to transform raw data into insights that help you make better decisions.

Git and GitHub for Beginners: A Friendly Guide

Jedidah Ondiso — Sat, 17 Jan 2026 20:37:08 +0000

Installing Git Bash

Google Git Bash

Download the Windows installer

Run the installer with these recommended settings:

Select "Use Git from Git Bash only "
Choose "Use the OpenSSL library"
Select "Checkout Windows-style, commit Unix-style line endings"
Choose "Use MinTTY"
Leave other options as default

Connecting Git to Your GitHub Account
Check git verson

git --version

Configure Your Identity

git config --global user.name " your name"
git config --global user.email "your email"

Generate a new SSH key

ssh-keygen -t ed25519 -C " email"

Generate an agent for the SSH key

 eval "$(agent-ssh -s)"

Publish agent

cat ~/.ssh/id_ed25519.pub

Add SSH Key to GitHub

Go to your GitHub account
Go to settings then click SSH and GPG keys
Click "New SSH key"
Paste your key and give it a descriptive name
Click "Add SSH key"

Test Your Connection
ssh -T git@github.com

WHAT IS GIT AND WHY IS VERSION CONTROL IMPORTANT

Git is a free, open-source distributed version control system (DVCS) used by developers to track changes in source code and other files during software development. It allows multiple people to collaborate on the same project without overwriting each other's work and enables users to revert to previous versions if needed.

IMPORTANCE OF VERSION CONTROL
• Prevents losing work when mistakes happen.
• Makes collaboration smooth and organized.
• Helps track who made which changes and when.
• Encourages experimentation—you can try new ideas without fear of breaking the main project.

HOW TO PUSH CODE TO GITHUB
GitHub is a platform that hosts your Git repositories online. Pushing code means sending your local changes to GitHub so others (or future you) can access them.
Steps to push code:
. Initialize Git in your project (if not already):

         Bash
        git init

. Add your files to Git’s staging area:

        Bash
        git add

. Commit your changes with a message:

         Bash
         git commit -m "Initial commit"

. Connect your project to GitHub:

        Bash
        git remote add origin

https://github.com/username/repository.git

. Push your code:

    Bash
    git push -u origin main

HOW TOP PULL CODE FROM GitHub
Pulling means downloading the latest changes from GitHub to your local machine.
Steps to pull code:
. Make sure you’re inside your project folder.
. Run:
Bash
git pull origin main
This updates your local project with any new commits from GitHub.

HOW TO TRACK CHANGES USING GIT
Git makes it easy to see what’s happening in your project.
• Check the status of your files:
Bash
git status

• Shows which files are new, modified, or staged.
• View commit history:
Bash
git log
• Displays a list of commits with messages,authors and timestamps.
• Compare changes before committing:
Bash
git diff
Shows the exact lines that were added or removed.

Understanding Your Data: The Essentials of Exploratory Data Analysis

Jedidah Ondiso — Mon, 12 Aug 2024 15:34:44 +0000

Exploratory data analysis is one of the basic and essential steps of a data science project.
A data scientist involves almost 70% of his work in doing the EDA of the dataset.
Exploratory Data Analysis (EDA) is a crucial initial step in data science and data analysis projects.
It involves analyzing and visualizing data to understand its key characteristics,
uncover patterns, and identify relationships between variables refers to the method
of studying and exploring record sets to apprehend their predominant traits, discover
patterns, locate outliers, and identify relationships between variables.

Key aspects of EDA include:

1.Distribution of Data:
Examining the distribution of data points to understand their range, central tendencies (mean, median), and dispersion (variance, standard deviation).
2.Graphical Representations:
Utilizing charts such as histograms, box plots, scatter plots, and bar charts to visualize relationships within the data and distributions of variables.
3.Outlier Detection:
Identifying unusual values that deviate from other data points. Outliers can influence statistical analyses and might indicate data entry errors or unique cases.
4.Correlation Analysis:
Checking the relationships between variables to understand how they might affect each other. This includes computing correlation coefficients and creating correlation matrices.
5.Handling Missing Values:
Detecting and deciding how to address missing data points, whether by imputation or removal, depending on their impact and the amount of missing data.
6.Summary Statistics:
Calculating key statistics that provide insight into data trends and nuances.
7.Testing Assumptions:
Many statistical tests and models assume the data meet certain conditions (like normality or homoscedasticity). EDA helps verify these assumptions.

Importance of Exploratory Data Analysis

1.Understanding Data Structures: EDA helps in getting familiar with the dataset, understanding the number of features, the type of data in each feature, and the distribution of data points. This understanding is crucial for selecting appropriate analysis or prediction techniques.

2.Identifying Patterns and Relationships: Through visualizations and statistical summaries, EDA can reveal hidden patterns and intrinsic relationships between variables. These insights can guide further analysis and enable more effective feature engineering and model building.

3.Detecting Anomalies and Outliers: EDA is essential for identifying errors or unusual data points that may adversely affect the results of your analysis. Detecting these early can prevent costly mistakes in predictive modeling and analysis.

4.Testing Assumptions: Many statistical models assume that data follow a certain distribution or that variables are independent. EDA involves checking these assumptions. If the assumptions do not hold, the conclusions drawn from the model could be invalid.

5.Informing Feature Selection and Engineering: Insights gained from EDA can inform which features are most relevant to include in a model and how to transform them (scaling, encoding) to improve model performance.

6.Optimizing Model Design: By understanding the data’s characteristics, analysts can choose appropriate modeling techniques, decide on the complexity of the model, and better tune model parameters.

7.Facilitating Data Cleaning: EDA helps in spotting missing values and errors in the data, which are critical to address before further analysis to improve data quality and integrity.

8.Enhancing Communication: Visual and statistical summaries from EDA can make it easier to communicate findings and convince others of the validity of your conclusions, particularly when explaining data-driven insights to stakeholders without technical backgrounds.

Python Libraries for performing Exploratory Data Analysis
Pandas: Provides extensive functions for data manipulation and analysis, including data structure handling and time series functionality.
Matplotlib: A plotting library for creating static, interactive, and animated visualizations in Python.
Seaborn: Built on top of Matplotlib, it provides a high-level interface for drawing attractive and informative statistical graphics.
Plotly: An interactive graphing library for making interactive plots and offers more sophisticated visualization capabilities.

The Ultimate Guide for Data Analytics Techniques and Tools

Jedidah Ondiso — Sun, 04 Aug 2024 16:57:57 +0000

Data Analytics is the process of cleaning, transformation and modelling of data with the goal of getting meaningful insights of data.

Types of Data Analytics in data analysis

Descriptive analytics
Describes what has happened over a given period of time for instance the number of views for a month.
Diagnostic Analytics
Answers why something happened and it involves more diverse data inputs and a bit of hypothesizing.

Tools for Data Analytics

Excel
Excel is a spreadsheet program from microsoft that enables users to format , organize and calculate data in a spreadsheet.
Equipped with built-in pivot tables, they are without a doubt the most sought-after analytic tool available.
It is an all-in-one data management software that allows you to easily import, explore, clean, analyze, and visualize your data.
Structured Query Language SQL
SQL is a programming language used to communicate with and manage relational databases.
Relational databases are used to store, retrieve and manage data in a structured format with data organized into tables with predefined columns and rows.
It allows users to create, modify and delete database tables as well as insert , update and retrieve data from the tables. SQL provide powerful querying capabilities that allows users to filter, sort , aggregate and join data from multiple tables to extract meaningful insights from large data sets.
SQL is used in various database management systems i.e. MySQL, Microsoft SQL Server, Oracle Database, PostgreSQL and SQLite.

SQL Commands
DDL - Data Definition Language
DQL - Data Query Language
DML - Data Manipulation Language
DCL - Data Control Language
TCL - Transaction Control Language

Power BI Power BI is a business analytics service provided by Microsoft that allows you to connect, analyze and visualize data from various sources. It can create interactive reports and dashboards to make data-driven decisions.

4.Data Storytelling
Data storytelling is the ability to effectively communicate insights from a dataset using narratives and visualizations.
It can be used to put data insights into context for and inspire action from your audience.