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Visualizing a SQL query in 5 mins SQL statements are executed by the database system in several steps, including: - Parsing the SQL statement and checking its validity - Transforming the SQL into an internal representation, such as relational algebra - Optimizing the internal representation and creating an execution... show more

Alex Xu

294,518 subscribers

23,200 görüntüleme • 1 ay önce •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

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Petar Ivanov profil fotoğrafı

Petar Ivanov1 ay önce

@bytebytego @sahnlam I always find it eye-opening to peek at actual execution plans in the database console. That moment when you spot an unexpected table scan is humbling. 💪

Adel Bucetta profil fotoğrafı

Adel Bucetta1 ay önce

@bytebytego @sahnlam the hard part is understanding which optimization to choose, not just transforming sql into algebra, that's a solved problem

Derrick Simpson profil fotoğrafı

Derrick Simpson1 ay önce

@bytebytego @sahnlam Also, "Predicate Pushdown" can allow filtering and removal of rows prior to a join. All records are not always joined/merged prior to filtering.

Buswe profil fotoğrafı

Buswe1 ay önce

@bytebytego @sahnlam EXPLAIN ANALYZE remata bien ese recorrido: enseña filas y tiempos reales de cada paso, no solo el plan estimado.

Trebor profil fotoğrafı

Trebor1 ay önce

@bytebytego @sahnlam Btw, great overview.

TechP profil fotoğrafı

TechP1 ay önce

@bytebytego @sahnlam Thanks for sharing this wonderful insight Very helpful

Trebor profil fotoğrafı

Trebor1 ay önce

@bytebytego @sahnlam Execution plan also considers histogram statistics to determine and use more efficient execution plans. An example is to skip using an index when a full tab scan would be less costly.

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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.

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

18,584 görüntüleme • 11 ay önce

Your AI work assistant should understand you. Ask Amazon Quick a question in plain English. Quick translates the prompt into a query across your CRM, pipeline tables, and dashboards, then returns the answer. Pipeline information, ranked leads, and account insights are delivered in seconds. No SQL, no filters, no pivots, no BI tool expertise required.

Your AI work assistant should understand you. Ask Amazon Quick a question in plain English. Quick translates the prompt into a query across your CRM, pipeline tables, and dashboards, then returns the answer. Pipeline information, ranked leads, and account insights are delivered in seconds. No SQL, no filters, no pivots, no BI tool expertise required.

Amazon Web Services

8,787,033 görüntüleme • 5 ay önce

We built a SQL and NoSQL co-pilot: Generate an answer with accompanying code by typing a question to ask your database - built in code editor - AI sanity check - schema explorer - shareable perma-links to past queries Join the private beta here:

We built a SQL and NoSQL co-pilot: Generate an answer with accompanying code by typing a question to ask your database - built in code editor - AI sanity check - schema explorer - shareable perma-links to past queries Join the private beta here:

Use Bink

74,965 görüntüleme • 3 yıl önce

Another insane Jev use case! Traditional database filters need precise, predefined conditions. But many questions are semantic: - Is this article mainly about software engineering? - Which topic best describes it? - How technically deep does it appear? These usually require moving rows into application code, invoking a model, parsing its output, and writing the result back. pg-jev is an open-source Postgres extension that exposes Jev through SQL functions. - jev() works inside WHERE - jev_prob() returns a probability - jev_choice() selects a label - jev_score() ranks rows across ordered levels. It needs no vector column or embedding index. Under the hood, pg-jev batches rows, sends them to Jev, and returns typed answers that SQL can filter, sort, group, and combine with exact predicates. The recording below runs it against real Hacker News stories stored in Postgres. It first shows the rows, then asks Jev to find software-engineering stories, classify them by topic, and rank them by technical depth. The final query reports requests, tokens, cost, and cache hits from the database session. GitHub repo: (don't forget to star it ⭐) Postgres keeps the data and controls the query, while Jev handles the part SQL cannot express as a deterministic condition. If you want to understand what Jev is doing under the hood, I also wrote a hands-on guide to building a Jev-style model with open models, entirely locally. Read it below.

Another insane Jev use case! Traditional database filters need precise, predefined conditions. But many questions are semantic: - Is this article mainly about software engineering? - Which topic best describes it? - How technically deep does it appear? These usually require moving rows into application code, invoking a model, parsing its output, and writing the result back. pg-jev is an open-source Postgres extension that exposes Jev through SQL functions. - jev() works inside WHERE - jev_prob() returns a probability - jev_choice() selects a label - jev_score() ranks rows across ordered levels. It needs no vector column or embedding index. Under the hood, pg-jev batches rows, sends them to Jev, and returns typed answers that SQL can filter, sort, group, and combine with exact predicates. The recording below runs it against real Hacker News stories stored in Postgres. It first shows the rows, then asks Jev to find software-engineering stories, classify them by topic, and rank them by technical depth. The final query reports requests, tokens, cost, and cache hits from the database session. GitHub repo: (don't forget to star it ⭐) Postgres keeps the data and controls the query, while Jev handles the part SQL cannot express as a deterministic condition. If you want to understand what Jev is doing under the hood, I also wrote a hands-on guide to building a Jev-style model with open models, entirely locally. Read it below.

Avi Chawla

298,637 görüntüleme • 16 gün 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.

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

Great to see this formalized Fable 5 is really great at refactoring / optimizing PRs, but often I want to understand why Rather than asking follow ups at $10 / $50 per million tokens, I hand these off to Cursor Composer 2.5 (5% of the cost) Recently, I asked Fable 5 to refactor a PR - I noticed it made a large change to a SQL statement and added a CTE I handed off a transcript of the chat to Composer and asked "Why did you change the SQL statement for the queued message updates?" Fable 5 works while the Cursor harness explains The same thing can be done with a plan + implement approach (Fable 5 -> GPT 5.5) Since 5.5 is ~50% cheaper, the math still holds

Great to see this formalized Fable 5 is really great at refactoring / optimizing PRs, but often I want to understand why Rather than asking follow ups at $10 / $50 per million tokens, I hand these off to Cursor Composer 2.5 (5% of the cost) Recently, I asked Fable 5 to refactor a PR - I noticed it made a large change to a SQL statement and added a CTE I handed off a transcript of the chat to Composer and asked "Why did you change the SQL statement for the queued message updates?" Fable 5 works while the Cursor harness explains The same thing can be done with a plan + implement approach (Fable 5 -> GPT 5.5) Since 5.5 is ~50% cheaper, the math still holds

matt palmer

22,035 görüntüleme • 4 ay önce

We've updated the Massive MCP server from 53 tools down to just 4. This results in significantly less context overhead on every request. What your agent does now: -> Searches for any Massive endpoint by natural language -> Read its full docs automatically -> Call the API and store results -> Query everything with SQL No bloated schemas. No stale tool definitions.

We've updated the Massive MCP server from 53 tools down to just 4. This results in significantly less context overhead on every request. What your agent does now: -> Searches for any Massive endpoint by natural language -> Read its full docs automatically -> Call the API and store results -> Query everything with SQL No bloated schemas. No stale tool definitions.

Massive

3,893,184 görüntüleme • 6 ay önce

Prisma Next (soon Prisma 8) now lets you inspect a database’s applied migration history in Prisma Studio, with the schema changes and exact SQL that ran.

Prisma Next (soon Prisma 8) now lets you inspect a database’s applied migration history in Prisma Studio, with the schema changes and exact SQL that ran.

Prisma

10,815 görüntüleme • 3 ay önce

You can work with large SQL scripts easily in DBeaver using the Outline panel. The panel displays the full structure of your script, including all data types, and lets you quickly navigate between statements. This feature is available in desktop editions starting from version 24.0.

You can work with large SQL scripts easily in DBeaver using the Outline panel. The panel displays the full structure of your script, including all data types, and lets you quickly navigate between statements. This feature is available in desktop editions starting from version 24.0.

DBeaver

19,210 görüntüleme • 10 ay önce