#Ddl Database Creation

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#Ddl Database Creation Reel by @visualcoders - 🗄️ SQL Command Types Explained 🧱 DDL (Data Definition Language) Defines database structure. Examples: CREATE, ALTER, DROP, TRUNCATE ✍️ DML (Data M
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@visualcoders
🗄️ SQL Command Types Explained 🧱 DDL (Data Definition Language) Defines database structure. Examples: CREATE, ALTER, DROP, TRUNCATE ✍️ DML (Data Manipulation Language) Works with table data. Examples: INSERT, UPDATE, DELETE, SELECT 🔐 DCL (Data Control Language) Controls access and permissions. Examples: GRANT, REVOKE 🔄 TCL (Transaction Control Language) Manages database transactions. Examples: COMMIT, ROLLBACK, SAVEPOINT #SQL #DDL #softwareengineer #coder #Database #DBMS #CodingReels #LearnSQL #ComputerScience
#Ddl Database Creation Reel by @techninjaah - Want my full toolkit? ➡️💬 Comment "LINK" and I will share all the other websites that I use when I am doing DSA! Kindly FOLLOW the account and then
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@techninjaah
Want my full toolkit? ➡️💬 Comment “LINK” and I will share all the other websites that I use when I am doing DSA! Kindly FOLLOW the account and then comment LINK as instagram does not allow sending DMs to non-followers. DSA doesn’t have to be boring text on a screen. 🙅‍♂️💻 If you are struggling to understand trees, graphs, or sorting algorithms, you need to see them in action. Visual learning > Rote memorization. Follow @techninjaah to level up your dev journey! ✅
#Ddl Database Creation Reel by @jessramosdata (verified account) - comment "AI" for my full synthetic data tutorial Youtube video! save for later & follow for more! You can customize any dataset for any industry, bus
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@jessramosdata
comment “AI” for my full synthetic data tutorial Youtube video! save for later & follow for more! You can customize any dataset for any industry, business problem, or project and get way more interesting data than Kaggle. Plus, you can ask for imperfect data with inconsistent values, duplicates, or nulls to make it feel more realistic to the real world. You just have to know how to specify your requirements and constraints when prompt engineering. Here’s what you should specify: ✨ size of dataset(s) (rows / columns) ✨ column names and data types ✨ primary keys and foreign keys ✨ distribution and allowed values ✨ variation of datapoints ✨ downloadable as CSVs ✨ anything else that may impact your project! Full example below: You are a data engineer generating a realistic synthetic dataset for [INDUSTRY] and [PROJECT TYPE OR PURPOSE].Can you generate [NUMBER] realistic datasets with the following requirements.Create an [TABLE NAME] table with [ROW COUNT] rows and columns: [LIST REQUIRED COLUMNS], plus any additional realistic columns you think would be useful. [PRIMARY KEY] is the primary key. [FOREIGN KEY 1] and [FOREIGN KEY 2] are foreign keys that connect to the [RELATED TABLE NAME] table. Ensure that [NUMBER] foreign key values exist in the related table but do not appear in this table (to simulate missing relationships).Create a [DIMENSION TABLE NAME] table with [ROW COUNT] rows and columns: [LIST REQUIRED COLUMNS], plus any additional realistic columns. [PRIMARY KEY] is the primary key and connects to the first table. Ensure that [NUMBER] records in this table have no matching rows in the first table.For both tables, include high variation across values, non-even category distributions, and realistic data patterns. All ID fields should be random numeric values only (no letters).[Add in any other requirements, constraints, or behavior rules]Return each table as a separate, downloadable CSV file. Have you tried this hack and said goodbye to Kaggle yet?
#Ddl Database Creation Reel by @thatcodergirlie (verified account) - Comment "blog" & I'll share the blog link with you in your DM 🤝🏻 (Make sure to follow else automation won't work) Topic: Redis (database that answ
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@thatcodergirlie
Comment “blog” & I’ll share the blog link with you in your DM 🤝🏻 (Make sure to follow else automation won’t work) Topic: Redis (database that answers millions of request in microseconds that too with a single thread) Save for your future interviews 📩 #dsa #systemdesign #tech #coding #codinglife [dsa, system design, redis, tech]
#Ddl Database Creation Reel by @pirknn (verified account) - Comment "LINK" to get links! 🚀 Want to learn Data Structures and Algorithms in a way that actually sticks? This mini roadmap helps you go from confu
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@pirknn
Comment “LINK” to get links! 🚀 Want to learn Data Structures and Algorithms in a way that actually sticks? This mini roadmap helps you go from confused beginner to solving problems confidently with the right mental models. 🎓 DSA Visualizer Perfect first step if you get lost in theory. You can visually understand how stacks, queues, trees, heaps, and sorting actually move step by step. Great for building intuition before you grind LeetCode. 📘 VisuAlgo DSA Now level up your understanding with interactive animations and explanations for classic algorithms and data structures. This is amazing for topics like BFS, DFS, shortest paths, hashing, heaps, segment trees, and complexity intuition. 💻 USFCA CS Lectures Time to learn the real foundations. These university style notes and visuals help you understand data structures, recursion, runtime analysis, and algorithm design patterns properly so you are not just memorizing solutions. 💡 With these DSA resources you will: Understand core data structures with visual intuition Learn common algorithm patterns for interviews Improve problem solving for LeetCode and coding assessments Build a strong base for system design and backend engineering If you are serious about software engineering interviews, competitive programming, or becoming a stronger developer, mastering DSA is one of the highest ROI skills. 📌 Save this post so you do not lose the roadmap. 💬 Comment “LINK” and I will send you all the links. 👉 Follow for more content on DSA, coding interviews, and software engineering.
#Ddl Database Creation Reel by @volkan.js (verified account) - Comment "DATABASE" for the links. You Will Finally Understand Databases & SQL 📌 Watch these high-quality database videos: 1️⃣ Harvard CS50's Intro
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@volkan.js
Comment “DATABASE” for the links. You Will Finally Understand Databases & SQL 📌 Watch these high-quality database videos: 1️⃣ Harvard CS50’s Intro to Databases with SQL A full university-level course covering SQL, relational databases, schemas, queries, joins, indexes, and real-world database fundamentals. 2️⃣ 7 Database Paradigms (Fireship) A fast, clear breakdown of different database types including relational, NoSQL, key-value, document, graph, and when to use each. 3️⃣ Database Design Course (Caleb Curry) A beginner-friendly guide to database design, normalization, relationships, primary keys, and planning databases the right way. If SQL feels confusing, database design feels abstract, or you’re unsure how real systems store and organize data, these videos connect everything together step by step. Great for learning SQL basics, understanding relational vs NoSQL databases, improving backend fundamentals, preparing for interviews, and building real projects that use databases properly. Save this if you want database concepts to finally make sense instead of memorizing queries.
#Ddl Database Creation Reel by @pirknn (verified account) - Comment "LINK" to get links! 🚀 Want to learn database design in a way that actually sticks? This mini roadmap takes you from beginner fundamentals t
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@pirknn
Comment “LINK” to get links! 🚀 Want to learn database design in a way that actually sticks? This mini roadmap takes you from beginner fundamentals to designing production ready schemas you can confidently use in real apps. 🎓 Idea to Prod DB Perfect starting point if you are new to database design. You will understand how to go from a product idea to a clean data model, how to identify entities and relationships, and how to avoid common beginner mistakes. Great for learning the basics of schema thinking, constraints and tradeoffs. 📘 DBs in Depth Now deepen your understanding. This resource helps you build a strong mental model for how databases actually work under the hood. You will learn core concepts like indexing, query planning, transactions, isolation levels and normalization vs denormalization so you stop guessing and start designing with confidence. 💻 DB Design Course Time to go end to end. You will apply what you learned by designing schemas for real world features like users, payments, orders and analytics. You will learn how to model one to many and many to many relationships, choose data types, set keys and constraints, and prepare your database for real production workflows. 💡 With these database resources you will: Design clean schemas that scale with your product Understand normalization, indexes and transaction safety Build portfolio ready backend projects with production style database design If you are serious about backend engineering, system design interviews or building real products, database design is a must have skill. 📌 Save this post so you do not lose the roadmap. 💬 Comment “LINK” and I will send you all the links. 👉 Follow for more content on databases, backend engineering and system design.
#Ddl Database Creation Reel by @kreggscode (verified account) - How Databases Work - Internals Deep Dive 🚀✨ Check out this new visualization! 💻 7 animated steps - • Auto-start, Web Audio, animated B-tree, Let
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@kreggscode
How Databases Work — Internals Deep Dive 🚀✨ Check out this new visualization! 💻 7 animated steps — • Auto-start, Web Audio, animated B-tree, Let me know what you think in the comments! 👇 #api #systemdesign #visualizer #datastructures #computerscience #developer #stem #algorithms #coding #programming #kreggscode #backend #webdev
#Ddl Database Creation Reel by @jganesh.ai (verified account) - When someone says "RAG over millions of PDFs", they're really asking about a search system + LLM, not a vector DB demo. I'd break it into five parts:
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@jganesh.ai
When someone says “RAG over millions of PDFs”, they’re really asking about a search system + LLM, not a vector DB demo. I’d break it into five parts: ingestion, embeddings, retrieval, generation, and monitoring. 1️⃣ Ingestion is offline, not request-time At this scale, ingestion must be async. ➤ Stream PDFs from object storage (S3/GCS) ➤ OCR only when needed ➤ Normalize to clean text ➤ Chunk meaningfully (≈512–2k tokens with overlap) ➤ Attach rich metadata: doc ID, page, section, product, timestamp None of this runs in the user path. 2️⃣ Embeddings + index built for scale You don’t embed on demand. ➤ Dedicated embedding jobs (GPU or batched) ➤ Distributed ANN index (Milvus, Qdrant, Vespa, Elastic) ➤ Sharding + HNSW / IVF / PQ for tens of millions of vectors ➤ Metadata stored alongside vectors Metadata filtering is the first gate — vector search is the fallback. 3️⃣ Tight online retrieval + generation The request path stays minimal: query → metadata filter → cache → ANN search → rerank → LLM ➤ Most queries are narrowed by metadata before vector search ➤ Many never hit the vector DB at all ➤ Rerank only a small candidate set ➤ Send just 5–10 chunks to the LLM More chunks usually hurt. 4️⃣ Where caching actually fits Caching controls cost and p95 latency. ➤ Query → answer cache (FAQs, repeats) ➤ Query → retrieval cache (top-k chunk IDs) ➤ Index / data cache (hot vectors + parsed PDFs) Real path becomes: query → cache → (miss) retrieve + LLM → write back 5️⃣ Monitoring closes the loop Without this, drift is silent. ➤ Retrieval quality (recall@k) ➤ Answer quality / feedback ➤ Latency + cache hit rates ➤ Re-embed and re-shard as data or models change BOTTOM LINE: RAG at this scale isn’t an LLM problem. It’s a metadata-first search + caching architecture problem. TAGS: #rag #engineering #trend #ai
#Ddl Database Creation Reel by @sarthaksavvy - Free open-source database design tool ✅ Share this reel with your friends ✅ Follow @sarthaksavvy Free, simple, and intuitive database design tool an
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@sarthaksavvy
Free open-source database design tool ✅ Share this reel with your friends ✅ Follow @sarthaksavvy Free, simple, and intuitive database design tool and SQL generator. https://drawdb.vercel.app/ #database #databasetool #databasedesign #bitfumes #mysql #postgresql #sqlite #mongodb #dynamodb

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