Forem: peter muriya

OLAP vs OLTP: Understanding the Backbone of Modern Data Systems

peter muriya — Mon, 04 May 2026 05:47:24 +0000

In today’s data-driven world, organizations rely on efficient data systems to manage daily operations and support decision-making. Two fundamental types of data processing systems, OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing), play distinct but complementary roles in this ecosystem.

OLTP systems are designed to handle real-time transactional data. These systems support day-to-day operations such as order processing, banking transactions, and customer record management. For example, when you withdraw money from an ATM or place an order on an e-commerce platform, an OLTP system ensures that the transaction is processed instantly and accurately. OLTP databases are typically optimized for speed and consistency, handling a large number of short, simple queries like INSERT, UPDATE, and DELETE. They maintain data integrity through normalization and strict ACID (Atomicity, Consistency, Isolation, Durability) properties.

On the other hand, OLAP systems are built for complex data analysis and decision support. They allow users to query large volumes of historical data to identify trends, patterns, and insights. For instance, a company might use OLAP to analyze sales data over several years to determine seasonal trends or evaluate the performance of different regions. Unlike OLTP, OLAP systems are optimized for read-heavy operations and complex queries involving aggregations, joins, and multidimensional analysis.

Another key difference lies in data structure. OLTP systems use normalized databases to reduce redundancy and improve transactional efficiency. In contrast, OLAP systems often use denormalized structures such as star or snowflake schemas, which enhance query performance and simplify reporting.

Performance requirements also differ significantly. OLTP systems prioritize fast response times and concurrency, as multiple users may perform transactions simultaneously. OLAP systems, however, focus on query performance and can tolerate longer processing times due to the complexity of analytical queries.

In practice, organizations use both systems together. Data from OLTP systems is periodically extracted, transformed, and loaded (ETL) into OLAP systems for analysis. This separation ensures that operational workloads do not interfere with analytical processing.

In conclusion, while OLTP systems keep businesses running smoothly by managing real-time transactions, OLAP systems empower organizations to make informed strategic decisions. Understanding the differences between the two is essential for designing efficient and scalable data architectures.

Automating ETL Workflows with Apache Airflow: From Python Script to Scheduled Pipeline

peter muriya — Sun, 26 Apr 2026 23:03:59 +0000

Modern data engineering revolves around automation, reliability, and scalability. Writing an ETL script in Python is only the beginning. To transform that script into a production-grade data pipeline, you need orchestration, scheduling, monitoring, and error handling. This is where Apache Airflow shines.

Apache Airflow is one of the most popular workflow orchestration tools in data engineering. It allows you to define, schedule, and monitor workflows programmatically using Python. Instead of manually running your ETL scripts, Airflow automates the entire process and ensures your data pipelines execute reliably.

Why Apache Airflow Matters

After developing an ETL pipeline in Python, several challenges remain:

• How do you schedule it to run automatically?
• How do you monitor failures?
• How do you retry failed tasks?
• How do you manage dependencies?
• How do you scale multiple workflows?

Apache Airflow solves all these problems by acting as the orchestrator for your ETL workflows.

Prerequisites

Before using Airflow, ensure you have:

• A working Python ETL script
• Python 3.9 or newer
• Apache Airflow installed
• A database (PostgreSQL, MySQL, or SQLite)
• Basic understanding of DAGs

Step 1: Install Apache Airflow

Install Apache Airflow using pip:

pip install apache-airflow

Initialize the Airflow metadata database:

airflow db init

Step 2: Verify Your ETL Script

Suppose you already have an ETL script named etl_pipeline.py:

import pandas as pd

def extract():
    return pd.read_csv("sales.csv")

def transform(df):
    df["total"] = df["quantity"] * df["price"]
    return df

def load(df):
    df.to_csv("processed_sales.csv", index=False)

def run_etl():
    data = extract()
    transformed = transform(data)
    load(transformed)

if __name__ == "__main__":
    run_etl()

Step 3: Create Your Airflow DAG

Airflow workflows are defined using DAGs (Directed Acyclic Graphs). Create a file inside the dags folder:

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime
from etl_pipeline import run_etl

default_args = {
    "owner": "airflow",
    "start_date": datetime(2026, 1, 1),
    "retries": 2
}

with DAG(
    dag_id="sales_etl_pipeline",
    default_args=default_args,
    schedule="@daily",
    catchup=False
) as dag:

    etl_task = PythonOperator(
        task_id="run_sales_etl",
        python_callable=run_etl
    )

Step 4: Start Airflow Services

Run the following commands in separate terminals:

airflow scheduler
airflow webserver --port 8080

Step 5: Access the Airflow UI

Open your browser and navigate to:

http://localhost:8080

From the Airflow dashboard, you can:

• View all DAGs
• Trigger pipelines manually
• Monitor execution history
• Investigate failures
• View logs

Step 6: Enable Your DAG

Place your DAG file in the dags directory. Airflow automatically discovers it.

Toggle the DAG switch in the Airflow UI to activate scheduling.

Step 7: Add Task Dependencies

For complex pipelines, separate ETL into multiple tasks:

extract_task >> transform_task >> load_task

Step 8: Monitor and Debug

Airflow provides detailed execution logs, retry mechanisms, and alerting.

Key features include:

• Automatic retries
• Task-level logs
• SLA monitoring
• Email notifications
• Failure alerts

Step 9: Production Best Practices

To build robust production pipelines:

• Store credentials securely using Airflow Connections
• Use environment variables
• Enable logging
• Implement idempotent ETL logic
• Add data quality checks
• Use a production-grade metadata database

Step 10: Scale Your Pipeline

As your data platform grows, Airflow can orchestrate:

• Multiple data sources
• Complex dependencies
• Machine learning workflows
• Data warehouse loads
• Real-time integrations

Conclusion

Apache Airflow transforms standalone Python ETL scripts into fully automated, scheduled, and monitored data pipelines. It handles orchestration, dependency management, retries, and observability, making it an essential tool for modern data engineers.

Once your ETL logic is complete, Airflow becomes the engine that runs it reliably in production. Whether you're processing daily reports or managing enterprise-scale data workflows, mastering Airflow is a critical skill in any data engineering toolkit.

ETL vs ELT: Which One Should You Use and Why?

peter muriya — Tue, 14 Apr 2026 11:21:50 +0000

Introduction

In today’s data-driven world, organizations rely on accurate and timely information to make informed decisions. Whether it’s predicting customer behavior, optimizing supply chains, or tracking business performance, data plays a crucial role. However, raw data is often messy, unstructured, and scattered across multiple systems. This is where data integration techniques such as ETL and ELT come into play.

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two foundational approaches used in data engineering to move and prepare data for analysis. While they share similar goals, they differ significantly in execution, tools, and use cases. Understanding these differences is essential for developers, data analysts, and organizations seeking to build efficient and scalable data pipelines.

This article explores ETL and ELT in depth, highlighting their definitions, processes, differences, tools, and real-world applications. By the end, you will have a clear understanding of which approach to use and why.

What is ETL?

ETL stands for Extract, Transform, Load. It is a data integration process in which data is extracted from various sources, transformed into a suitable format, and then loaded into a target system such as a data warehouse.

ETL has been the traditional approach to data processing for decades and is widely used in structured environments. It ensures that data is cleaned, validated, and standardized before being stored, making it highly reliable for reporting and analytics.

The ETL Process

1. Extract
In this phase, data is collected from multiple sources such as databases, APIs, flat files, and cloud applications. These sources may include CRM systems, ERP platforms, spreadsheets, and transactional databases.

2. Transform
The transformation phase involves cleaning, filtering, aggregating, and structuring the data. Common transformations include:

Removing duplicates
Standardizing formats
Applying business rules
Aggregating data
Handling missing values

3. Load
Once transformed, the data is loaded into a target system such as a data warehouse or data mart for analysis and reporting.

A simple ETL diagram

Example of an ETL Pipeline

Consider an e-commerce company that wants to analyze daily sales. Data is extracted from a MySQL database, transformed to calculate total sales and remove inconsistencies, and loaded into a data warehouse such as Amazon Redshift.

Python Example (Conceptual):

Extract:
sales_data = read_from_database()

Transform:
clean_data = remove_duplicates(sales_data)
aggregated_data = calculate_daily_sales(clean_data)

Load:
load_into_warehouse(aggregated_data)

Benefits of ETL

Ensures high data quality and consistency
Suitable for structured data environments
Supports compliance and governance requirements
Reduces storage costs by transforming data before loading
Ideal for legacy systems

Challenges of ETL

Time-consuming due to pre-loading transformations
Requires significant infrastructure and maintenance
Less scalable with massive datasets
Slower compared to modern approaches

What is ELT?

ELT stands for Extract, Load, Transform. It is a modern approach to data integration where raw data is first loaded into a data warehouse or data lake and transformed afterward using the processing power of the target system.

ELT gained popularity with the rise of cloud computing and big data technologies. Unlike ETL, ELT leverages scalable cloud platforms such as Snowflake, Google BigQuery, and Amazon Redshift to perform transformations efficiently.

The ELT Process

1. Extract
Data is collected from various sources, just like in ETL.

2. Load
Instead of transforming data beforehand, raw data is loaded directly into a cloud data warehouse or data lake.

3. Transform
Transformations are performed within the target system using SQL or specialized tools.

Simple ELT Diagram

Example of an ELT Pipeline

A streaming platform collects user activity data. The raw data is loaded into Snowflake and later transformed using SQL queries to generate insights such as user engagement and retention metrics.

SQL Example:

SELECT
DATE(event_time) AS event_date,
COUNT(*) AS total_events
FROM user_events
GROUP BY event_date;

Benefits of ELT

Faster data ingestion
Highly scalable and suitable for big data
Supports real-time analytics
Stores raw data for future analysis
Leverages cloud computing power

Challenges of ELT

Requires modern cloud infrastructure
May increase storage costs
Needs strong governance and security controls
Requires skilled data engineers

Key Differences Between ETL and ELT

Feature: Processing Order
ETL: Transform before loading
ELT: Transform after loading

Feature: Performance
ETL: Slower for large datasets
ELT: Faster with cloud scalability

Feature: Storage
ETL: Stores processed data
ELT: Stores raw and processed data

Feature: Scalability
ETL: Limited scalability
ELT: Highly scalable

Feature: Infrastructure
ETL: Traditional on-premise systems
ELT: Cloud-based data platforms

Feature: Use Cases
ETL: Structured data and compliance reporting
ELT: Big data, analytics, and machine learning

Real-World Use Cases

ETL Use Cases

1. Banking and Financial Services
Banks use ETL to clean and validate transaction data for regulatory compliance and reporting.

2. Healthcare Systems
Hospitals rely on ETL to standardize patient records and ensure data accuracy.

3. Retail Reporting
Retailers use ETL to generate daily sales reports from transactional databases.

4. Enterprise Resource Planning (ERP)
Organizations integrate data from ERP systems into centralized warehouses using ETL.

ELT Use Cases

1. Big Data Analytics
Companies like Netflix and Uber analyze massive datasets using ELT pipelines.

2. Machine Learning and AI
Data scientists use ELT to access raw data for model training and experimentation.

3. Real-Time Analytics
ELT supports real-time dashboards and insights in cloud-based systems.

4. IoT and Streaming Data
Sensor data from connected devices is stored and transformed using ELT.

Tools Used in ETL

Informatica PowerCenter
Microsoft SQL Server Integration Services (SSIS)
Talend
Apache NiFi
IBM DataStage
Pentaho Data Integration

Tools Used in ELT

Snowflake
Google BigQuery
Amazon Redshift
dbt (Data Build Tool)
Fivetran
Stitch
Apache Spark
Azure Synapse Analytics

ETL vs ELT: A Side-by-Side Illustration

ETL:
Sources → Extract → Transform → Load → Warehouse → BI

ELT:
Sources → Extract → Load → Warehouse → Transform → BI

How to Choose Between ETL and ELT

Choose ETL if:

You require strict data governance and compliance.
You work with structured and legacy systems.
Data volumes are moderate.
Data quality must be ensured before storage.

Choose ELT if:

You handle large-scale or unstructured data.
You use cloud-based data warehouses.
You need real-time or near-real-time analytics.
You want to retain raw data for advanced analytics and machine learning.
Scalability and flexibility are priorities.

Hybrid Approach: The Best of Both Worlds

Many modern organizations adopt a hybrid approach that combines ETL and ELT. For example, sensitive data may be cleaned using ETL before loading, while large datasets are processed using ELT within cloud platforms.

This approach ensures efficiency, scalability, and compliance while leveraging the strengths of both methodologies.

Best Practices

Understand your data sources and business requirements.
Prioritize data governance and security.
Choose scalable tools that align with your infrastructure.
Automate workflows using orchestration tools such as Apache Airflow.
Monitor and optimize data pipelines regularly.
Document processes for maintainability and collaboration.

Conclusion

ETL and ELT are essential methodologies in modern data engineering. ETL transforms data before loading it into a storage system, making it ideal for structured environments and regulatory compliance. ELT, on the other hand, loads raw data first and transforms it later, leveraging the scalability and performance of cloud computing.

The choice between ETL and ELT depends on your organization’s data volume, infrastructure, performance requirements, and analytical goals. While ETL remains relevant for traditional systems, ELT has emerged as the preferred approach for big data, cloud analytics, and machine learning.

Ultimately, understanding both techniques empowers developers and data professionals to design efficient, scalable, and future-ready data pipelines.

By mastering ETL and ELT, you take a significant step toward becoming a proficient data engineer or analytics professional in the modern digital landscape.

From Joins to Window Functions: Unlocking Powerful SQL Techniques

peter muriya — Mon, 02 Mar 2026 14:59:58 +0000

Structured Query Language (SQL) is one of the most powerful tools for working with data. Two essential concepts that help transform raw data into meaningful insights are Joins and Window Functions. If you’re just getting started, mastering these techniques will significantly improve your ability to analyze and report on data.

Understanding Joins

Joins allow you to combine data from two or more tables based on a related column. For example, imagine you have a Customers table and an Orders table. A join helps you connect a customer to their respective orders.

The most common types include:
• INNER JOIN – Returns only matching records from both tables.
• LEFT JOIN – Returns all records from the left table and matches from the right table.
• RIGHT JOIN – Returns all records from the right table and matches from the left table.
• FULL JOIN – Returns all records from both tables, whether they match or not.

Joins are essential for relational databases because real-world data is often stored across multiple tables. Instead of duplicating information, you connect it when needed.

Exploring Window Functions

Window functions take SQL analysis to the next level. Unlike aggregate functions (such as COUNT or SUM) that group rows, window functions perform calculations across a set of rows related to the current row — without collapsing them.

Common window functions include:

• ROW_NUMBER() – Assigns a unique number to each row.
• RANK() – Ranks rows within a partition.
• SUM() OVER() – Calculates running totals.

For example, you can rank sales employees by monthly revenue or calculate cumulative sales over time.

Bringing It All Together

When you combine Joins and Window Functions, you unlock advanced insights. You can first join tables to gather all relevant data, then apply window functions to rank, compare, or analyze trends.

Start small, practice often, and soon you’ll move from basic queries to writing powerful analytical SQL.

From Messy Data to Meaningful Decisions: How Analysts Turn Power BI into Business Impact

peter muriya — Wed, 11 Feb 2026 06:33:42 +0000

Introduction

In today’s data-driven organizations, insight is not created by dashboards alone. It is created when analysts transform messy, fragmented data into reliable models, apply DAX to extract meaning, and design dashboards that drive measurable action. Power BI is not just a visualization tool; it is a decision-making engine in the hands of a skilled analyst.

1. Cleaning the Chaos: Turning Raw Data into Reliable Models

Business data is rarely clean. It comes from multiple systems: CRMs, ERPs, spreadsheets, APIs; often containing duplicates, inconsistent formats, missing values, and conflicting definitions.

Using Power Query in Power BI, analysts standardize formats, remove duplicates, merge datasets, and establish consistent business definitions. Clean data builds trust and reduces reporting disputes.

Business Impact: Reliable data reduces reporting errors and accelerates decision-making.

2. Modeling for Meaning: Structuring Data for Insight

After cleaning, analysts design structured data models, often using star schemas that link fact tables (like Sales) to dimension tables (like Customers or Dates).

A strong data model improves performance, ensures calculation accuracy, and allows flexible analysis across different business dimensions.

3. DAX: Turning Numbers into Intelligence

DAX (Data Analysis Expressions) allows analysts to move beyond basic totals. With DAX, analysts build metrics such as Year-over-Year growth, rolling averages, customer lifetime value, and profit margins.

These measures help organizations understand not just what happened, but why it happened and what to do next.

Business Impact: Smarter KPIs support strategic actions like reallocating budgets, adjusting pricing, or improving sales performance.

Here's a DAX Guide containing all the DAX formulas and expressions.

4. Designing Dashboards that Drive Action

Effective dashboards highlight key performance indicators, use conditional formatting to flag risks, and provide drill-through capabilities for deeper insights.

Good design reduces cognitive overload and focuses users on actionable information.

Business Impact: Managers move from reactive decision-making to proactive strategy execution.

5. From Insight to Measurable Results

Power BI delivers real value when insights lead to measurable outcomes, such as cost reductions, improved sales performance, better inventory management, or optimized marketing spend.

By continuously measuring performance and refining dashboards, analysts ensure data remains aligned with business goals.

Conclusion

Power BI expertise is not just technical; it is strategic. Analysts bridge the gap between raw data and executive decisions.

When messy data is transformed into structured models, enhanced with DAX intelligence, and presented through purposeful dashboards, businesses gain clarity, confidence, and measurable impact.

Schemas and Data Modelling in Power BI

peter muriya — Mon, 02 Feb 2026 03:58:19 +0000

Effective data modelling is the foundation of any successful Power BI solution. A well-designed model improves performance, simplifies DAX calculations, and ensures accurate, reliable reporting. Poor modelling leads to slow reports, confusing measures, and incorrect insights. Understanding schemas, table roles, and relationships is therefore critical.

What Is Data Modelling in Power BI?
Data modelling in Power BI is the process of structuring tables and relationships in a way that reflects how the business views its data. Rather than mirroring raw source systems, Power BI models are optimized for analytics, not transactions. This usually means reshaping data into clear fact and dimension tables and applying a well-known schema.

Fact and Dimension Table

Fact Tables

Fact tables store quantitative, measurable data generated by business events. They are typically large and grow over time. Examples include sales transactions, invoice lines, website visits, and expense records. Fact tables contain numeric columns such as amount, quantity, or cost, and include foreign keys that link to dimension tables.

Dimension Tables

Dimension tables provide context for facts by describing who, what, when, where, and how. Common dimensions include date, customer, product, employee, and location. They are generally smaller than fact tables and contain descriptive attributes used for filtering and grouping.

Schemas

Star Schema

A star schema is the most recommended modelling approach in Power BI. It consists of one central fact table connected directly to multiple dimension tables, forming a star-like structure. Relationships are one-to-many from dimension to fact, usually with single-direction filtering.

Benefits of a star schema include excellent performance, simpler DAX calculations, ease of understanding, and reduced ambiguity in relationships.

Snowflake Schema

A snowflake schema is a variation of the star schema where dimension tables are normalized into multiple related tables. While this reduces data redundancy and can represent complex hierarchies, it introduces more joins and relationships.

In Power BI, snowflake schemas often result in reduced performance and more complex DAX. For this reason, flattening dimensions into a star schema is usually preferred unless normalization is unavoidable.

Relationships in Power BI

Relationships define how tables interact within the model. Key concepts include cardinality, cross-filter direction, and active versus inactive relationships. Best practices include using one-to-many relationships, keeping filter direction single where possible, and avoiding many-to-many relationships unless necessary.

Why Good Data Modelling Is Critical

Good data modelling improves report performance, ensures accurate aggregations, simplifies DAX expressions, and enhances the end-user experience. Power BI’s VertiPaq engine is optimized for star schemas, making clean and simple models essential for scalable and reliable reporting.

Conclusion

Strong data modelling is essential in Power BI. By using fact and dimension tables, favoring star schemas, minimizing unnecessary complexity, and carefully managing relationships, developers can create fast, accurate, and maintainable reports that deliver meaningful business insights.

Here is a 16-page Power BI cheetsheat.

Introduction to Linux for Data Engineers

peter muriya — Mon, 26 Jan 2026 07:34:28 +0000

1. What is Linux, and Why Data Engineers Use It

Linux is a widely used operating system for servers and the cloud. Most data platforms — such as Hadoop, Spark, Kafka, Airflow, and cloud machines — run on Linux.

For data engineers, Linux is important because:

Most data systems run on Linux servers – If you deploy data pipelines, databases, or analytics platforms, you are almost always working on Linux.
It is efficient and stable – Linux handles large data processing jobs well and can run continuously without frequent restarts.
It gives you control – You can automate tasks, manage files, and inspect logs directly from the terminal.
Cloud platforms use Linux – AWS, Azure, and Google Cloud primarily use Linux-based virtual machines.

In simple terms: if you work with data at scale, Linux is the environment where that work lives.

2. The Linux Terminal (Command Line)

Linux is often used through the terminal. Instead of clicking buttons, you type commands. This may feel strange at first, but it is powerful and fast once you get used to it.

2.1. Basic Linux Commands Every Beginner Should Know

Below are some common commands data engineers use daily:

pwd - check current directory
ls - list files and folders
mkdir new_directory - create a new directory
cd new_directory - move into the directory
touch empty_file - create an empty file
cat empty_file - view the file

3. Why Text Editors Matter in Data Engineering

As a data engineer, you constantly edit:

Configuration files
SQL scripts
Python or Bash scripts
Log files

On Linux, you often edit on the command line without a graphical editor.

The two most common terminal editors are:
Vi or Vim - Very powerful, with a steep learning curve
Nano - Simple and beginner-friendly

Using Nano (Best for Beginners) 4.1 Opening Nano

To create or open a file with Nano:

nano pipeline_notes.txt

You will see a simple editor with instructions at the bottom.

4.2 Editing a File in Nano

Inside Nano, type the following:

This file documents our data pipeline. Source: CSV files Destination: Data Warehouse

Nano works like a normal editor, just type.

4.3 Saving and Exiting Nano

Press Ctrl + 0 to save the file Press Enter to confirm the filename Press Ctrl + X to exit Nano.

This simplicity makes Nano great for Linux users.

5. Using Vi(Very Common on Servers)

The image below shows different commands used to navigate servers using Vi:

Vi is available on almost every Linux system. It has different modes, which is what confuses most people.

5.1 Opening a File with Vi

vi pipeline_notes.txt

You start in Normal Mode (You cannot type text yet)

5.2 Entering Insert Mode

To start typing:

Press i (insert mode)

Now type:

Processed daily using a cron job
Owner: Data Engineering Team

5.3 Saving and Exiting Vi

Press Esc (return to normal mode)
Type: wq
Press Enter

Explanation: -:w>write(save)-:q>quit

5.4 If You Make a Mistake

To exit without saving:

:q!

6. Viewing the Final File from the Terminal

After editing with Nano or Vi, you can confirm the contents:

cat pipeline_notes.txt

Output:

This file documents our data pipeline.
Source: CSV files
Destination: Data Warehouse
Processed daily using a cron job
Owner: Data Engineering Team

7. How This Connects to Real Data Engineering Work
In real projects, data engineers use Linux to:

SSH into cloud servers
Edit Airflow DAGs using Vi or Nano
Check pipeline logs
Automate jobs using shell scripts
Manage data files and folders

For example:

ssh user@data-server cd /opt/airflow/dags vi daily_sales_pipeline.py

This is very common in production environments.

8. Summary

Linux is the default environment for data engineering work
Knowing Linux commands helps you move faster and troubleshoot issues
Nano is simple and ideal for beginners
Vi is powerful and widely available on servers
Text editing in the terminal is a core practical skill for data engineers

If you are new to Linux, start with Nano, learn the basics of Vi, and practice daily.

Beginner’s Guide to Git: Version Control, Push, Pull, and Tracking Changes

peter muriya — Sun, 18 Jan 2026 22:19:45 +0000

Introduction

Git is a version control system that helps you track changes, collaborate with others, and safely manage your code.This guide is written for beginners and explains the core ideas behind Git in simple terms.

What is Version Control?

Version control keeps a history of changes to files. It allows you to:

Go back to earlier versions
See what changed and who changed it
Work with others without overwriting work

What is Git?

Git is a distributed version control system. Every developer has a full copy of the project and its history. Git is different from Github, which is just a hosting platform for Git projects.

Key Concepts

Repository: A folder tracked by Git
Commit: A saved snapshot of changes
Branch: A parallel line of development
Remote: A copy of the repository hosted online

Tracking Changes

Check status: git status
Stage files: git add .
Save changes: git commit -m "message"

Pushing Code

Pushing sends your commits to a remote repository: git push origin main

Pulling Code

Pulling brings the latest changes from a remote repository: git pull origin main

Daily Workflow

Pull latest changes
Edit files
Add changes
Commit changes
Push to remote

Why learn Git?

Git helps you work safely, collaborate easily, and is a required skill for most developers.