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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

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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. 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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. 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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.

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$Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.$

Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.

Akshay 🚀

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Proof of SQL just hit a million rows sub-second. You read that right. Proof of SQL is a ZK coprocessor that uses @NVIDIA's accelerated computing and its own custom GPU acceleration framework to process large data volumes faster than any other solution on the market.

Proof of SQL just hit a million rows sub-second. You read that right. Proof of SQL is a ZK coprocessor that uses @NVIDIA's accelerated computing and its own custom GPU acceleration framework to process large data volumes faster than any other solution on the market.

Space and Time

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MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

MCP is mind-blowing. Here is a genius setup that will earn you a promotion with your manager. I have multiple data sources: data in S3, MySQL, and local files. Using Spice (an open-source project), I can co-locate all of that data behind a single SQL endpoint and augment it using MCP. Before, accessing data was slow, and I had to make multiple requests to get anything. After using Spice, I can query the data much faster (since it's mirrored locally), and I don't have to worry about where the data is coming from. Spice acts as an MCP server, so I can connect to it from my IDE or the Claude Desktop app and have access to all the data. But even more cool than that: Spice also acts as an MCP client, so it can connect to other MCP servers. This is where things get really interesting! Watch the video. I'm querying multiple data sources from my IDE and using the Google Maps MCP server for driving directions.

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