Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

Formatting is not an advanced SQL skill, but all advanced users have good SQL formatting 😉 If you have a SQL test in your data analyst interview, here are two formatting tricks that show you’ve actually used SQL on the job 👇 1️⃣ WHERE 1=1 Add this to your...

codingfortherestofus's profile picture

brooke | learn coding + ai

35,400 subscribers

139,100 görüntüleme • 5 ay önce •via TikTok

EğitimBilim & Teknoloji#sql#dataanalyst#interviewtips
LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

What work do data analysts do all day? Here’s a day in the life of a data analyst. Your experience may be different as there are many different types of data analysts roles but most are using Excel and SQL to answer business questions 🔥 🤓📊 #TikTokCreatorSearchInsightsIncentive #creatorsearchinsights #dataanalytics #dataanalyst
0:24

What work do data analysts do all day? Here’s a day in the life of a data analyst. Your experience may be different as there are many different types of data analysts roles but most are using Excel and SQL to answer business questions 🔥 🤓📊 #TikTokCreatorSearchInsightsIncentive #creatorsearchinsights #dataanalytics #dataanalyst

brooke | learn coding + ai

87,000 görüntüleme • 4 ay önce

Dear Friend, I wrote this book for you. For the past year, I have labored to create a product that will help you learn and master SQL. I have been there. I have felt the frustration of trying to learn SQL and not knowing where to begin. I have lived through the struggle of setting up a platform to run SQL queries. Most platforms require sign-ups and logins that create a headache for learners. I also know the challenge of finding proper SQL exercises that mirror the real-world experience of a data analyst. Yes, I have been in your shoes. That’s why I created SQL Essentials for Data Analysis: A 50-Day Hands-on Challenge Book (Go From Beginner to Pro). Yes, to give you a clear, practical path from beginner to confident SQL user. ✅Why SQL Still Matters You may be wondering if SQL still matters in 2025. The answer: it has never mattered more. SQL is the lingua franca of data. Data still lives in databases, and the only language it truly understands is SQL. Think about it, even in Python, SQL is there. You’ve probably heard about the powerful pandas library. Guess what? It also has some SQL. And don’t get me started on BigQuery, Tableau, Power BI, and Databricks; the answer is the same: they all rely on SQL. SQL is the big shadow that hovers over everything data. This is why learning SQL is a must for data analysts, engineers, scientists, and anyone working with data. SQL connects everything: exploration, extraction, transformation, modeling, validation, and reporting. ✅Why I Wrote This Book Dear friend, I wanted to create a resource that gives you everything you need to learn SQL for data analysis. Quite often, resources are scattered across different places. You might learn theory in one place, search for datasets in another, and hunt for questions somewhere else. More often than not, the only place you can tackle SQL challenges is online. But online platforms usually focus on syntax and don’t reflect the messiness of real-world data. I wrote this book to give you the best of both worlds: theory and practice. I don’t want you to be worrying about where to find resources. I want you to focus only on learning SQL. If you are new to SQL or need a refresher on the fundamentals, Part 1 of the book has you covered. If you are looking for practice, Part 2 is 49 days of hands-on SQL challenges designed to mirror real-world tasks. Each day in the book is designed to feel like a mini project, rather than isolated exercises. Take Day 15: Standardize Climbers Data, for example: On this day, you’re not just writing a single query; you’re working with a dataset from start to finish. By combining these tasks, you experience a full data preprocessing workflow, just like a real project. You get to practice loading, transforming, cleaning, and validating data, all in one challenge. This approach makes every day a hands-on project, not just an isolated query. You’re learning how SQL is used in real-world scenarios, not just memorizing syntax. By the end of each day, you’ve solved a problem that feels meaningful and practical: yes, something that mirrors data analysts’ and engineers’ work in real life. In this book I use SQLite. I chose SQLite because it’s simple, lightweight, and runs on any system without complicated setups or cloud accounts. You don’t need to worry about complex configurations. SQLite allows you to focus entirely on learning SQL concepts, queries, and logic without distractions. You will just have to import it. I also structured the book for use in Jupyter or Google Colab notebooks. These are playgrounds for data analysts, engineers, and scientists. These environments are interactive and flexible. They let you run queries, visualize results, and experiment in real time. Using notebooks ensures that you can practice SQL while documenting your work and learning at your own pace, all in one place. No need for sign-ups. ✅Why 50 Days? I chose 50 days intentionally. Learning SQL isn’t a sprint; it’s a habit. You can’t truly master a language by cramming a few queries in one sitting. 50 days creates a commitment. You attach yourself to a goal, a tangible outcome. Every day is a small win, a step forward, and by the end of the journey, you’ve transformed your understanding of SQL. By spreading the learning over 50 days, you build momentum, consistency, and confidence. Think of it like training for a marathon. You don’t run 26 miles on the first day. You run a little each day, gradually building strength, endurance, and skill. By the end of the 50 days, you’ll have tackled a wide range of SQL tasks: from simple filtering to window functions, date operations, joins, and performance tuning. You’ll have not just learned SQL but truly internalized it. The goal isn’t to overwhelm you. It’s to give you a structured, achievable path that fits into your daily routine, so learning SQL becomes natural, steady, and rewarding. Even if you don’t finish within 50 days, the 50-day structure gives you a rhythm, a habit, and a sense of accomplishment. The kind of outcome that sticks long after the book is finished. In summary, I wrote the book to address these pain points: 🔶Not knowing where to start: The book gives you a clear roadmap that guides you day by day. 🔶Too much theory, not enough practice: Reading about SQL is not the same as doing SQL. This book includes hands-on challenges that mirror real-world scenarios, so you’re not just memorizing commands; you’re learning to think like a data analyst. 🔶Complex setup: Many learners get stuck setting up databases or configuring environments. You will not worry about complex setups; everything runs in SQLite3 inside Jupyter Notebook, so you start immediately. 🔶Disconnected learning: The challenges mirror real-world analytics problems. Every day here is like a mini project, giving you the experience of exploring, cleaning, transforming, and analyzing data ✅What I ask of You I wrote this book for you because I want you to succeed, but books alone don’t create mastery; your effort does. I have provided the tools. All I ask is that you show up every day. Even if it’s just 20–30 minutes, take the challenge seriously. Tackle the problems, experiment with your queries, make mistakes, and fix them. That’s how real learning happens. I also ask that you trust the process. The book is designed to guide you from beginner to confident SQL user, step by step. Some days will feel "easy" and others "hard." Stay the course, and by the end, you’ll see how all the pieces fit together. Finally, I ask that you bring curiosity and persistence. SQL is a language of logic and structure, but it’s also a language of insight. The more you explore, the more patterns you’ll discover, and the more confident you’ll become in solving real-world problems. Don’t be scared to experiment. If you commit to this, I promise you’ll finish 50 days with more than just knowledge. You’ll have the skills, confidence, and habit of thinking like a data analyst. To make starting even easier, as a subscriber to this newsletter, I’m giving you an exclusive 35% launch discount. You can grab your copy today and start the 50-day journey at a reduced price. Grab SQL Essentials for Data Analysis here: I can’t wait to hear about your progress, the insights you uncover, and the confidence you gain along the way. If you have any questions, feel free to reach out to me or post them in the comments section. Let’s start this journey together: one challenge, one query, one day at a time. Warmly, Benjamin PS. Please repost.
2:03

Dear Friend, I wrote this book for you. For the past year, I have labored to create a product that will help you learn and master SQL. I have been there. I have felt the frustration of trying to learn SQL and not knowing where to begin. I have lived through the struggle of setting up a platform to run SQL queries. Most platforms require sign-ups and logins that create a headache for learners. I also know the challenge of finding proper SQL exercises that mirror the real-world experience of a data analyst. Yes, I have been in your shoes. That’s why I created SQL Essentials for Data Analysis: A 50-Day Hands-on Challenge Book (Go From Beginner to Pro). Yes, to give you a clear, practical path from beginner to confident SQL user. ✅Why SQL Still Matters You may be wondering if SQL still matters in 2025. The answer: it has never mattered more. SQL is the lingua franca of data. Data still lives in databases, and the only language it truly understands is SQL. Think about it, even in Python, SQL is there. You’ve probably heard about the powerful pandas library. Guess what? It also has some SQL. And don’t get me started on BigQuery, Tableau, Power BI, and Databricks; the answer is the same: they all rely on SQL. SQL is the big shadow that hovers over everything data. This is why learning SQL is a must for data analysts, engineers, scientists, and anyone working with data. SQL connects everything: exploration, extraction, transformation, modeling, validation, and reporting. ✅Why I Wrote This Book Dear friend, I wanted to create a resource that gives you everything you need to learn SQL for data analysis. Quite often, resources are scattered across different places. You might learn theory in one place, search for datasets in another, and hunt for questions somewhere else. More often than not, the only place you can tackle SQL challenges is online. But online platforms usually focus on syntax and don’t reflect the messiness of real-world data. I wrote this book to give you the best of both worlds: theory and practice. I don’t want you to be worrying about where to find resources. I want you to focus only on learning SQL. If you are new to SQL or need a refresher on the fundamentals, Part 1 of the book has you covered. If you are looking for practice, Part 2 is 49 days of hands-on SQL challenges designed to mirror real-world tasks. Each day in the book is designed to feel like a mini project, rather than isolated exercises. Take Day 15: Standardize Climbers Data, for example: On this day, you’re not just writing a single query; you’re working with a dataset from start to finish. By combining these tasks, you experience a full data preprocessing workflow, just like a real project. You get to practice loading, transforming, cleaning, and validating data, all in one challenge. This approach makes every day a hands-on project, not just an isolated query. You’re learning how SQL is used in real-world scenarios, not just memorizing syntax. By the end of each day, you’ve solved a problem that feels meaningful and practical: yes, something that mirrors data analysts’ and engineers’ work in real life. In this book I use SQLite. I chose SQLite because it’s simple, lightweight, and runs on any system without complicated setups or cloud accounts. You don’t need to worry about complex configurations. SQLite allows you to focus entirely on learning SQL concepts, queries, and logic without distractions. You will just have to import it. I also structured the book for use in Jupyter or Google Colab notebooks. These are playgrounds for data analysts, engineers, and scientists. These environments are interactive and flexible. They let you run queries, visualize results, and experiment in real time. Using notebooks ensures that you can practice SQL while documenting your work and learning at your own pace, all in one place. No need for sign-ups. ✅Why 50 Days? I chose 50 days intentionally. Learning SQL isn’t a sprint; it’s a habit. You can’t truly master a language by cramming a few queries in one sitting. 50 days creates a commitment. You attach yourself to a goal, a tangible outcome. Every day is a small win, a step forward, and by the end of the journey, you’ve transformed your understanding of SQL. By spreading the learning over 50 days, you build momentum, consistency, and confidence. Think of it like training for a marathon. You don’t run 26 miles on the first day. You run a little each day, gradually building strength, endurance, and skill. By the end of the 50 days, you’ll have tackled a wide range of SQL tasks: from simple filtering to window functions, date operations, joins, and performance tuning. You’ll have not just learned SQL but truly internalized it. The goal isn’t to overwhelm you. It’s to give you a structured, achievable path that fits into your daily routine, so learning SQL becomes natural, steady, and rewarding. Even if you don’t finish within 50 days, the 50-day structure gives you a rhythm, a habit, and a sense of accomplishment. The kind of outcome that sticks long after the book is finished. In summary, I wrote the book to address these pain points: 🔶Not knowing where to start: The book gives you a clear roadmap that guides you day by day. 🔶Too much theory, not enough practice: Reading about SQL is not the same as doing SQL. This book includes hands-on challenges that mirror real-world scenarios, so you’re not just memorizing commands; you’re learning to think like a data analyst. 🔶Complex setup: Many learners get stuck setting up databases or configuring environments. You will not worry about complex setups; everything runs in SQLite3 inside Jupyter Notebook, so you start immediately. 🔶Disconnected learning: The challenges mirror real-world analytics problems. Every day here is like a mini project, giving you the experience of exploring, cleaning, transforming, and analyzing data ✅What I ask of You I wrote this book for you because I want you to succeed, but books alone don’t create mastery; your effort does. I have provided the tools. All I ask is that you show up every day. Even if it’s just 20–30 minutes, take the challenge seriously. Tackle the problems, experiment with your queries, make mistakes, and fix them. That’s how real learning happens. I also ask that you trust the process. The book is designed to guide you from beginner to confident SQL user, step by step. Some days will feel "easy" and others "hard." Stay the course, and by the end, you’ll see how all the pieces fit together. Finally, I ask that you bring curiosity and persistence. SQL is a language of logic and structure, but it’s also a language of insight. The more you explore, the more patterns you’ll discover, and the more confident you’ll become in solving real-world problems. Don’t be scared to experiment. If you commit to this, I promise you’ll finish 50 days with more than just knowledge. You’ll have the skills, confidence, and habit of thinking like a data analyst. To make starting even easier, as a subscriber to this newsletter, I’m giving you an exclusive 35% launch discount. You can grab your copy today and start the 50-day journey at a reduced price. Grab SQL Essentials for Data Analysis here: I can’t wait to hear about your progress, the insights you uncover, and the confidence you gain along the way. If you have any questions, feel free to reach out to me or post them in the comments section. Let’s start this journey together: one challenge, one query, one day at a time. Warmly, Benjamin PS. Please repost.

Benjamin Bennett Alexander

19,840 görüntüleme • 1 yıl önce

WHERE 1 = 1 in SQL Explained! (And why you should use it) #dataanalyst #dataanalysis #sqltips #sql #sqlsyntax
1:28

WHERE 1 = 1 in SQL Explained! (And why you should use it) #dataanalyst #dataanalysis #sqltips #sql #sqlsyntax

Elijah Butler

227,800 görüntüleme • 3 ay önce

(includes partner resources) the links for the resources are listed here for easy access! the link is also in my bio! resources: learn SQL (beginner to advanced) in 4 hours: SQL toolkit for data engineering: SQL murder mystery: SQL scavenger hunt: ##learnsql##sql##learncoding##yay##softwareengineer
0:45

(includes partner resources) the links for the resources are listed here for easy access! the link is also in my bio! resources: learn SQL (beginner to advanced) in 4 hours: SQL toolkit for data engineering: SQL murder mystery: SQL scavenger hunt: ##learnsql##sql##learncoding##yay##softwareengineer

eggintech

288,600 görüntüleme • 9 ay önce

It's launch week . 5 days 5 launches Day 1: SQL Server Data Connector Now you can connect your SQL Server to Julius in just a few clicks and instantly explore your data. Track adoption, analyze performance, and access KPIs all without writing a single line of SQL
1:32

It's launch week . 5 days 5 launches Day 1: SQL Server Data Connector Now you can connect your SQL Server to Julius in just a few clicks and instantly explore your data. Track adoption, analyze performance, and access KPIs all without writing a single line of SQL

rahul

48,216 görüntüleme • 1 yıl önce

🌊TrellisInsights (YC W24) makes unstructured data SQL-ready. It extracts and transforms your unstructured data to SQL-compliant tables with schema you define with natural language. Congrats on the launch, Mac Klinkachorn and Jacky Lin!
0:24

🌊TrellisInsights (YC W24) makes unstructured data SQL-ready. It extracts and transforms your unstructured data to SQL-compliant tables with schema you define with natural language. Congrats on the launch, Mac Klinkachorn and Jacky Lin!

Y Combinator

14,213 görüntüleme • 2 yıl önce

How to do a simple JOIN in SQL #dataanalyst #dataanalysis #dataanalytics #data #analyst #sql #joins #sqljoin #sqljoins #sqlcode #coding #programming #sequel #breakintotech #techjob #techjobs #techtok #datatok
2:31

How to do a simple JOIN in SQL #dataanalyst #dataanalysis #dataanalytics #data #analyst #sql #joins #sqljoin #sqljoins #sqlcode #coding #programming #sequel #breakintotech #techjob #techjobs #techtok #datatok

Mary | Data Analyst

80,600 görüntüleme • 2 yıl önce

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!
9:28

Your agents can't keep up with real-time data. Especially when it's scattered across dozens of sources. Most teams waste weeks building custom connectors for every database, API, and data warehouse. Then they build ETL pipelines to sync everything. By the time your agent retrieves the data, it's already outdated. Picture this: Your Postgres database updated 5 minutes ago. Your MongoDB collection changed 2 minutes ago. Your agent is still pulling from yesterday's snapshot. This is why most production RAG systems fail. There's a better approach: MindsDB is an open-source AI platform with a federated data engine that lets you query multiple data sources in real-time using SQL - without moving any data. Here's what makes it different: ↳ Your data stays in place. No ETL pipelines or data duplication ↳ Query Postgres, MongoDB, REST APIs, and more using consistent SQL ↳ JOIN across different sources in real-time with a unified interface ↳ Works with both structured and un-structured data And here's the best part: You don't even need to write SQL. Just describe what you want in plain English, and MindsDB converts it to SQL automatically. The system does all the heavy lifting. The breakthrough for AI agents is simple: When data updates at the source, your agent gets fresh results immediately. No sync delays. No stale embeddings. No custom code for each integration. You can literally write a SQL query that joins a Postgres table with a MongoDB collection and gets live results. This is what production AI applications need but rarely get. In this video, I give you a complete walkthrough of what we just discussed and how to actually do it. Make sure you watch this till the end. I've shared the link to MindsDB's GitHub repo in the next tweet!

Akshay 🚀

65,672 görüntüleme • 1 yıl önce

SQL injection! 💉 After finding the number of columns using the NULL trick, you can take it a step further and identify which column takes which data type. Swap 'abc' into one position at a time. The one that comes back without an error is your string column, and that's where your data goes. Want to practice this technique? Hack our FREE labs! 👇
0:59

SQL injection! 💉 After finding the number of columns using the NULL trick, you can take it a step further and identify which column takes which data type. Swap 'abc' into one position at a time. The one that comes back without an error is your string column, and that's where your data goes. Want to practice this technique? Hack our FREE labs! 👇

Web Security Academy

20,968 görüntüleme • 3 ay önce

Introducing my SQL Agent: How to automate SQL with AI. Today I'll share how to make a SQL AI Agent that can automatically connect to databases, understand the tables and schema, and write and execute high-quality SQL queries. I'll guide you through setting up the SQL Agent, creating dozens of SQL queries, and returning the data from those queries. This AI is a huge help!! Table of Contents: 00:00 Introduction 01:30 How to Get The AI Data Science Agents 03:23 AI Tips Newsletter: Get The Code 04:00 Project Setup 06:30 Setting Up Your First SQL Agent 11:00 Running SQL Queries with the SQL Agent 14:25 Next Steps, Project Roadmap, & AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, February 12th, I'm sharing one of my best AI Projects: SQL-Writing Business Intelligence Team Register here (570+ registered):
17:04

Introducing my SQL Agent: How to automate SQL with AI. Today I'll share how to make a SQL AI Agent that can automatically connect to databases, understand the tables and schema, and write and execute high-quality SQL queries. I'll guide you through setting up the SQL Agent, creating dozens of SQL queries, and returning the data from those queries. This AI is a huge help!! Table of Contents: 00:00 Introduction 01:30 How to Get The AI Data Science Agents 03:23 AI Tips Newsletter: Get The Code 04:00 Project Setup 06:30 Setting Up Your First SQL Agent 11:00 Running SQL Queries with the SQL Agent 14:25 Next Steps, Project Roadmap, & AI Bootcamp Github to AI Data Science Team (Army of Copilots): Get the Code by Joining my Python AI/ML Tips Newsletter: P.S. - Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, February 12th, I'm sharing one of my best AI Projects: SQL-Writing Business Intelligence Team Register here (570+ registered):

Matt Dancho (Business Science)

77,564 görüntüleme • 1 yıl önce

Here’s a StudyBot that can help you learn SQL in 28 Days! With the help of Vondy, I have made a ChatBot that can help you learn SQL for Data Science with a Custom Study plan in 28 days! (Link to the StudyBot below 👇)
0:44

Here’s a StudyBot that can help you learn SQL in 28 Days! With the help of Vondy, I have made a ChatBot that can help you learn SQL for Data Science with a Custom Study plan in 28 days! (Link to the StudyBot below 👇)

Sasi 📊📈

24,169 görüntüleme • 3 yıl önce

IYour login form can be hacked with 1 line 💉👇 This is SQL Injection — the #1 backend attack that still owns companies in 2025. ' OR '1'='1 That's it. That's all it takes to dump your entire database if you do this: ❌ query = "SELECT * FROM users WHERE email = '" + input + "'" ✅ FIX IT LIKE THIS: Use prepared statements / parameterized queries. Always. I explained the full attack + how to prevent it in this reel. Save this before your next interview. Share with that friend who still concatenates SQL strings.
0:53

IYour login form can be hacked with 1 line 💉👇 This is SQL Injection — the #1 backend attack that still owns companies in 2025. ' OR '1'='1 That's it. That's all it takes to dump your entire database if you do this: ❌ query = "SELECT * FROM users WHERE email = '" + input + "'" ✅ FIX IT LIKE THIS: Use prepared statements / parameterized queries. Always. I explained the full attack + how to prevent it in this reel. Save this before your next interview. Share with that friend who still concatenates SQL strings.

codedevguy

19,300 görüntüleme • 3 ay önce

English Is The New SQL LLMs have made it easy for everyone to create on-the-fly dashboards and plots. You can connect to your database and then use a SOTA LLM like 4o, Opus, or Gemini to generate SQL and Python code to create on-the-fly dashboards and plots. ChatLLM makes it possible across all state-of-the-art LLMs -
0:27

English Is The New SQL LLMs have made it easy for everyone to create on-the-fly dashboards and plots. You can connect to your database and then use a SOTA LLM like 4o, Opus, or Gemini to generate SQL and Python code to create on-the-fly dashboards and plots. ChatLLM makes it possible across all state-of-the-art LLMs -

Bindu Reddy

50,569 görüntüleme • 2 yıl önce

How to use the RANK() window function in SQL 🌟 #dataanalyst #dataanalytics #dataanalysis #data #analyst #sql #rank #windowfunction #windowfunctions #sqlcode #coding #programming #techjobs #breakintotech #techtok #wfh #workfromhome #wfhjobs #remotejobs #remotework
1:57

How to use the RANK() window function in SQL 🌟 #dataanalyst #dataanalytics #dataanalysis #data #analyst #sql #rank #windowfunction #windowfunctions #sqlcode #coding #programming #techjobs #breakintotech #techtok #wfh #workfromhome #wfhjobs #remotejobs #remotework

Mary | Data Analyst

51,800 görüntüleme • 1 yıl önce

This is a very clever idea to use an LLM with your SQL data. SQL + AI has been tried before, but one of the best parts of this solution is getting the same exact OpenAI's completion API. In other words: You are now talking to an LLM that knows everything about your database and you don't even notice it! I recorded a 2-min video to show you how it works. Thanks to the team for the collaboration, listening to my feedback, and helping me understand what they built. Go to to try this yourself.
2:07

This is a very clever idea to use an LLM with your SQL data. SQL + AI has been tried before, but one of the best parts of this solution is getting the same exact OpenAI's completion API. In other words: You are now talking to an LLM that knows everything about your database and you don't even notice it! I recorded a 2-min video to show you how it works. Thanks to the team for the collaboration, listening to my feedback, and helping me understand what they built. Go to to try this yourself.

Santiago

230,551 görüntüleme • 2 yıl önce

💻🗄️ SQL TUTORIAL FOR BEGINNERS: LEARN HOW DATABASES REALLY WORK ⚠️ EDUCATIONAL DISCLAIMER: This content is strictly for educational purposes, database learning, and cybersecurity awareness. Practice using sample databases, your own projects, or authorized lab environments. Never access, modify, extract, or delete data from databases without explicit permission. Always protect sensitive information and follow applicable laws. 🔍 WHAT IS SQL? SQL (Structured Query Language) is used to communicate with relational databases. It allows developers to retrieve, organize, insert, update, and delete data. ### 🔑 8 SQL CONCEPTS EVERY BEGINNER SHOULD KNOW 1️⃣ Tables and Databases Databases organize information into tables containing rows and columns. For example, a customers table might store customer IDs, names, and email addresses. 2️⃣ SELECT — Retrieve Data Used to choose which columns you want to view. 3️⃣ WHERE — Filter Records Allows you to retrieve records that match specific conditions. 4️⃣ ORDER BY — Sort Results Organizes results in ascending or descending order. 5️⃣ Aggregate Functions Functions such as COUNT(), SUM(), and AVG() help summarize data. 6️⃣ GROUP BY — Organize Data into Groups Groups records with common values so you can calculate totals and statistics for each group. 7️⃣ JOIN — Connect Related Tables Combines related information from multiple tables using matching columns. 8️⃣ Primary and Foreign Keys Primary keys identify records uniquely, while foreign keys help establish relationships between tables. ### 🛡️ WHY SQL MATTERS IN CYBERSECURITY Understanding SQL helps security professionals recognize database risks, investigate application behavior, and understand vulnerabilities such as SQL injection. Developers can reduce SQL injection risks by using parameterized queries, validating input appropriately, and restricting database permissions. 🎯 KEY LESSON: Don't just memorize SQL commands. Understand how data is stored, how queries retrieve it, and how databases should be protected. Start with sample data, practise each concept, and build your skills step by step. 🔐 HACKWITHBANKS | DATABASE & CYBERSECURITY EDUCATION #Hackwithbanks #SQL #tutorial #database #Cybersecurity
44:56

💻🗄️ SQL TUTORIAL FOR BEGINNERS: LEARN HOW DATABASES REALLY WORK ⚠️ EDUCATIONAL DISCLAIMER: This content is strictly for educational purposes, database learning, and cybersecurity awareness. Practice using sample databases, your own projects, or authorized lab environments. Never access, modify, extract, or delete data from databases without explicit permission. Always protect sensitive information and follow applicable laws. 🔍 WHAT IS SQL? SQL (Structured Query Language) is used to communicate with relational databases. It allows developers to retrieve, organize, insert, update, and delete data. ### 🔑 8 SQL CONCEPTS EVERY BEGINNER SHOULD KNOW 1️⃣ Tables and Databases Databases organize information into tables containing rows and columns. For example, a customers table might store customer IDs, names, and email addresses. 2️⃣ SELECT — Retrieve Data Used to choose which columns you want to view. 3️⃣ WHERE — Filter Records Allows you to retrieve records that match specific conditions. 4️⃣ ORDER BY — Sort Results Organizes results in ascending or descending order. 5️⃣ Aggregate Functions Functions such as COUNT(), SUM(), and AVG() help summarize data. 6️⃣ GROUP BY — Organize Data into Groups Groups records with common values so you can calculate totals and statistics for each group. 7️⃣ JOIN — Connect Related Tables Combines related information from multiple tables using matching columns. 8️⃣ Primary and Foreign Keys Primary keys identify records uniquely, while foreign keys help establish relationships between tables. ### 🛡️ WHY SQL MATTERS IN CYBERSECURITY Understanding SQL helps security professionals recognize database risks, investigate application behavior, and understand vulnerabilities such as SQL injection. Developers can reduce SQL injection risks by using parameterized queries, validating input appropriately, and restricting database permissions. 🎯 KEY LESSON: Don't just memorize SQL commands. Understand how data is stored, how queries retrieve it, and how databases should be protected. Start with sample data, practise each concept, and build your skills step by step. 🔐 HACKWITHBANKS | DATABASE & CYBERSECURITY EDUCATION #Hackwithbanks #SQL #tutorial #database #Cybersecurity

Hackswithbanks

25,000 görüntüleme • 9 gün önce

LiveSQL is your new SQL bestie - allowing you to learn, write and execute SQL all in your browser, with ZERO database setup or installations! (& did I mention it's free???) 😝
5:24

LiveSQL is your new SQL bestie - allowing you to learn, write and execute SQL all in your browser, with ZERO database setup or installations! (& did I mention it's free???) 😝

layla

31,341 görüntüleme • 1 yıl önce

Introducing Judgment vSQL: Your coding agent SHOULD query all your agent data in standard SQL, with no made-up DSL or limited look-up tools. > Ask your coding agent "Compare my last few agent versions, did anything regress?" > It writes one SQL query that joins your traces, the steps inside them, and the labels our judges gave them > It runs the query in a sandbox you can inspect > It brings back a ranked list of where your agents are failing
0:24

Introducing Judgment vSQL: Your coding agent SHOULD query all your agent data in standard SQL, with no made-up DSL or limited look-up tools. > Ask your coding agent "Compare my last few agent versions, did anything regress?" > It writes one SQL query that joins your traces, the steps inside them, and the labels our judges gave them > It runs the query in a sandbox you can inspect > It brings back a ranked list of where your agents are failing

Judgment Labs

14,792 görüntüleme • 9 gün önce