Learning Data and Databases in 30 Days of Code

Day 9 of 21 Days of CS Fundamentals 🚀 Day 9 focused on some of the most important DBMS concepts that ensure data remains structured, reliable, and efficient in real-world systems. 🔹 Day 9 Topic: Database Management Systems (DBMS) ✨ Concepts covered: Normalization and its role in reducing data redundancy Transactions and their importance in database consistency Atomicity and Durability (ACID properties) Indexing and how it improves data retrieval performance This session helped me understand how databases maintain data integrity, reliability, and performance, especially when handling large volumes of data. 🙏 Mentor: Niyati Mishra Thank you for explaining complex DBMS concepts in a clear and structured way. Excited to keep learning and strengthening my CS fundamentals 🚀💻 TechNeeds IGDTUW #CSFundamentals #DBMS #DatabaseSystems #LearningJourney #StudentDeveloper #TechCommunity #WomenInTech #EngineeringStudent

𝐒𝐭𝐢𝐥𝐥 𝐝𝐨𝐢𝐧𝐠 𝐒𝐐𝐋 𝐒𝐞𝐫𝐯𝐞𝐫 𝐭𝐚𝐬𝐤𝐬 𝐦𝐚𝐧𝐮𝐚𝐥𝐥𝐲 𝐢𝐧 2026? That’s not a workload problem — that’s an 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐠𝐚𝐩. I’m breaking that gap with 100 𝐃𝐚𝐲𝐬 – 100 𝐝𝐛𝐚𝐭𝐨𝐨𝐥𝐬 𝐒𝐜𝐫𝐢𝐩𝐭𝐬, showing how real DBAs automate production using 𝐝𝐛𝐚𝐭𝐨𝐨𝐥𝐬 & 𝐏𝐨𝐰𝐞𝐫𝐒𝐡𝐞𝐥𝐥. 𝗕𝗮𝗰𝗸𝘂𝗽𝘀, 𝐡𝐞𝐚𝐥𝐭𝐡 𝐜𝐡𝐞𝐜𝐤𝐬, 𝐦𝐢𝐠𝐫𝐚𝐭𝐢𝐨𝐧𝐬, 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐫𝐞𝐯𝐢𝐞𝐰𝐬, 𝐫𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠, if these are still manual, you’re losing valuable time every week. I’m sharing a 100 𝐃𝐚𝐲𝐬 – 100 𝐝𝐛𝐚𝐭𝐨𝐨𝐥𝐬 𝐒𝐜𝐫𝐢𝐩𝐭𝐬 series focused on: • Real SQL Server production scenarios • Automation using 𝐝𝐛𝐚𝐭𝐨𝐨𝐥𝐬 & 𝐏𝐨𝐰𝐞𝐫𝐒𝐡𝐞𝐥𝐥 • One practical command or script per day • Skills every modern DBA must have 🎯 𝐆𝐨𝐚𝐥: Help DBAs move from 𝐦𝐚𝐧𝐮𝐚𝐥 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧-𝐟𝐢𝐫𝐬𝐭 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬. 👉 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐡𝐞𝐫𝐞: \[https://lnkd.in/gdAC3meQ) 🎓 𝐃𝐁𝐀𝐓𝐎𝐎𝐋𝐒 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞: https://lnkd.in/gwdMQvuj 🎓 𝐏𝐨𝐰𝐞𝐫𝐒𝐡𝐞𝐥𝐥 𝐟𝐨𝐫 𝐃𝐁𝐀𝐬: https://lnkd.in/gK2jSVye 🎓 𝐀𝐥𝐥 𝐂𝐨𝐮𝐫𝐬𝐞𝐬: https://lnkd.in/gJfZABBw If you’re a SQL Server DBA or an IT professional serious about automation, this series is for you. Comment 𝐀𝐔𝐓𝐎𝐌𝐀𝐓𝐄 if you’re joining the journey. #SQLServer #SQLServerDBA #DatabaseAdministrator #DBA #dbatools #PowerShell #PowerShellAutomation #SQLServerAutomation #Automation #ITAutomation #DevOps #DevOpsForDBA #CloudComputing #AzureSQL #EnterpriseIT #TechCareers #CareerGrowth #Upskilling #ContinuousLearning #SelfLearning #ITProfessionals #SoftwareEngineering #SystemsAdministration #DataEngineering #100DaysOfLearning #100DaysOfCode #LearningJourney #YouTubeLearning #TechYouTube #FreeLearning #OnlineLearning #AutomationMindset #ModernDBA #ProductionDBA #DatabaseAutomation #SQLCommunity #ITCommunity #TechEducation #LearningEveryday #GrowInTech #AutomationSkills #FutureSkills #DBALife #SQLTraining #PowerShellScripts #TechGrowth

Peace be upon you Excited to share my latest project: a Database Management System (DBMS) implemented entirely in Bash, with a Zenity-based GUI for easier and more interactive usage. # What makes this project unique? . It supports multiple users and enforces foreign key relationships between tables — all while running in a simple shell environment. # Key Features 1. Multi-User Support . - Each user logs in with a unique username . - Users work independently with isolated workspaces . - Data integrity is maintained across users 2. Foreign Key Relationships . - Supports PRIMARY KEY and FOREIGN KEY constraints . - Enforced behaviors: . - DROP PREVENT – Prevents deleting a referenced table . - UPDATE CASCADE – Updates propagate automatically . - SET NULL – FK values become NULL when the referenced record is deleted 3. Functionality . - Create and manage databases and tables Insert, update, delete, and query data with validation 4. Zenity GUI for user-friendly interaction المشروع ده ناس كتير عملته قبل كده، لكنه فعلًا مشروع قوي جدًا، واتعلمت منه حاجات كتير. حاولت أضيف لمستي الخاصة خصوصًا في: دعم الـ Multi-User التعامل مع Foreign Keys بشكل قريب من الـ real DBMS كمان عامل Tag لنسخة CLI للي حابب يشتغل على المشروع من غير GUI، ولو حد حابب يطوره أكتر، فإضافة JOIN و Aggregate Functions هتكون خطوة قوية جدًا. 🙌 Special Thanks Ahmed Ali – for collaboration and support Mahmoud El-Mahmoudy – for supervision and guidance Project Repository: https://lnkd.in/dQSm8yWQ #Bash #DBMS #Linux #ShellScripting #DatabaseSystems #OpenSource #SoftwareEngineering #Backend #CLI #GUI #LearningByDoing

𝐇𝐚𝐫𝐮𝐃𝐁 — 𝐀 𝐂𝐮𝐬𝐭𝐨𝐦 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐁𝐮𝐢𝐥𝐭 𝐟𝐫𝐨𝐦 𝐒𝐜𝐫𝐚𝐭𝐜𝐡 𝐢𝐧 𝐆𝐨 This is not production-ready — it’s a learning-focused, from-scratch implementation of core database concepts. 💡 Why HaruDB? This project is about learning database internals by building one: • 𝐖𝐀𝐋 (𝐖𝐫𝐢𝐭𝐞-𝐀𝐡𝐞𝐚𝐝 𝐋𝐨𝐠𝐠𝐢𝐧𝐠) • 𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧𝐬 • 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠 • 𝐂𝐫𝐚𝐬𝐡 𝐫𝐞𝐜𝐨𝐯𝐞𝐫𝐲 • 𝐀𝐂𝐈𝐃 𝐠𝐮𝐚𝐫𝐚𝐧𝐭𝐞𝐞𝐬 If you’re interested in databases, Go, or systems engineering, feel free to explore or contribute. 🌐 Website: https://haru-db.vercel.app 💻 GitHub: https://lnkd.in/dUTqfe3B #GoLang #Databases #SystemDesign #Backend #OpenSource #ACID #WAL #PostgreSQL #SQLite #Experimental

📚 New Repository: Database‑Course‑documentation I’ve compiled Database‑Course‑documentation — a structured and easy‑to‑follow documentation for database courses. This repo includes: ✔️ Concept summaries ✔️ Schema diagrams ✔️ SQL examples ✔️ Best practices for normalization and querying Perfect for students and professionals who want a solid foundation in databases 🎓 #Documentation #DatabaseCourse #TechEducation #GitHub

📘 DBMS Learning Log — Implementing Atomicity & Durability Today I learned how Atomicity and Durability are actually implemented inside a DBMS. 🔹 What I learned: 1️⃣ Implementing Atomicity Atomicity ensures that a transaction is all-or-nothing. I learned that DBMS achieves this using: Transaction logs Undo operations If a transaction fails before commit: All partial changes are rolled back The database is restored to its previous consistent state This prevents half-executed transactions. 2️⃣ Implementing Durability Durability ensures that once a transaction commits, its changes are permanent. I learned that this is achieved by: Writing changes to stable storage (disk) Using Write-Ahead Logging (WAL) Replaying logs during system recovery after a crash Even if the system crashes immediately after commit, the data can be recovered correctly.

Multi-Version Concurrency Control (MVCC) in DBMS Multi-Version Concurrency Control (MVCC) is a database technique that allows multiple users to read and write data simultaneously without blocking each other. Instead of updating data in place, the database creates multiple versions of a record. This allows: Readers to access older committed versions Writers to create new versions without blocking reads High concurrency with minimal locking MVCC is widely used to maintain performance, consistency, and scalability in modern databases. Why MVCC is used Prevents read/write blocking Supports safe concurrent transactions Improves performance under heavy workloads How it works (high level) Each transaction sees a consistent snapshot of the data Writes create new versions instead of overwriting existing records Older versions are cleaned up later by background processes Advantages Higher concurrency Faster read operations Strong data consistency Trade-offs Additional storage overhead Complexity in version management Requires efficient garbage collection MVCC is a key reason why databases like PostgreSQL handle concurrent workloads efficiently. #DataEngineering #DBMS #Databases #SQL #SystemDesign

This Week’s Deep Dive: Why Database Health Decides Pipeline Performance This week, I consciously shifted my focus from “why is my pipeline slow?” to 👉 “what condition is my database actually in?” That mindset change itself was a big learning. 1. Database Health ≠ Database Running A database being up doesn’t mean it’s healthy. I learned to look at: Long-running and “still running” queries Session locks and blocked transactions Effects of uncommitted transactions on readers and writers This helped me understand why some pipelines wait even when CPU looks idle. 2. Optimization Is Mostly About Cleanliness Most performance issues were not “complex logic” problems: Dead tuples accumulating after UPDATE/DELETE Query planner working with outdated statistics Auto-vacuum not always running when you expect it to Running VACUUM / ANALYZE with intent made a visible difference. 3. Space Awareness Before Failure One important lesson: storage issues show symptoms much later than their cause. I learned to: Check table and schema-level size trends Identify heavy tables in ETL workflows Understand how table bloat quietly slows scans and writes Prevention here is much cheaper than firefighting. 4. Why Linux Knowledge Matters for Data Engineers Databases don’t live in isolation. At the OS level, I started correlating: Disk availability (df) with pipeline failures Directory-level usage (du) with sudden performance drops Temp files and write-ahead logs with unexpected space consumption This connected the dots between Ubuntu → Database → Pipeline. 💡 Biggest Takeaway Pipeline optimization doesn’t start in Spark or ETL tools. It starts with observability, discipline, and system awareness. Understanding the full stack — OS, database, and data flow — changed how I debug performance issues. #DatabaseHealth #PostgreSQL #ETL #Linux #PerformanceTuning #DataPipelines #LearningJourney

Hi, Episode: 18/100 – Topic: ✅ RANGE Keyword in DDS (Logical Files) ➡️ Purpose: Used in select/omit specifications in DDS to compare a field’s value against a range of constants. ➡️ Where: Appears in logical file DDS, at the record level, combined with S (Select) or O (Omit). ➡️ How It Works: Instead of checking for a single value (like with COMP) or multiple discrete values (like VALUES), you can specify a range. Records whose field value falls within the specified range are included (for Select) or excluded (for Omit). ➡️ Syntax Example: S STUDMARK RANGE(90 99) This selects records where MARKS is between 94 and 99 (inclusive). You can also use Omit like: O STUDMARK RANGE(90 99) This omits records where MARKSis between 94 and 99. ➡️ Behavior: Records matching Select conditions are included in the LF. Records matching Omit conditions are excluded. Conditions are evaluated when the access path is built, not dynamically. ➡️ Creation: Please watch my 15th Episode – Logical File creation for steps. In this example, I create a logical file that selects student records where MARKS are between 94 and 99 – you can use any range you need. ✅ Key Points: Works only in logical files, not physical files. For dynamic filtering, use SQL views or queries instead. Can be combined with other keywords like DYNSLT for dynamic selection. If you face any issues while creating file, please refer to the attached video for step-by-step guidance. #Day18Growth #AS400 #IBMi #PowerSystems #DB2 #RPG #SQLRPGLE #RPGLE #CLProgramming #SEU #MidrangeComputing #TechCommunity #100DaysChallenge #100DaysOfAS400 #LearningJourney #SkillDevelopment #ContinuousLearning

Introduction to Relational Databases (RDBMS) — IBM (Coursera) Working on real-world projects before taking this course made the fundamentals of relational databases much easier to grasp. Concepts like table design, keys, relationships, and normalization were already familiar from hands-on experience, and this course helped formalize that understanding with strong theoretical grounding. A solid reminder that building first accelerates learning, and certifications help validate and refine that knowledge. #RDBMS #SQL #Databases #DataEngineering #IBM #Coursera #BackendDevelopment #LearningByDoing