World of SQL

SQL isn’t just surviving the AI wave, it’s becoming the trusted engine inside modern RAG systems. In 2026, the best RAG setups don’t rely only on vectors. They route to SQL for precise, structured truth. Here’s the mental model 🧵 How SQL powers reliable Retrieval-Augmented Generation #Sql #RAG #AgenticAI

12/12 - Final Mental Model Daily SQL Tip 💡 Human → defines meaning SQL → defines truth AI → explains it That’s the stack. Everything else is tooling. #SQL #AI #SQLforAI

11/12 - Human + AI + SQL Daily SQL Tip 💡 The future is not AI replacing SQL engineers. It’s collaboration. Human: - understands the data model - designs the schema - validates results AI: - drafts queries - explores data - explains insights SQL remains the language of truth. #SQL #AI #SQLforAI #DataEngineering

10/12 - AI Observability Daily SQL Tip 💡 AI systems generate queries automatically. Someone still needs to monitor performance. Classic SQL tools become AI tools: - explain - query plans - indexes - statistics Understanding query execution will be even more valuable in the AI era. #SQL #AIEngineering #DatabasePerformance

PostgreSQL: Why Engineers Love It Daily SQL Tip 💡 PostgreSQL isn’t just another database. It’s a powerhouse for serious data work. What makes it stand out: • Advanced SQL support Window functions, CTEs, JSON, arrays, all native. • Extensible by design Add custom types, functions, even languages (Python, etc.) • MVCC (no locking drama) Reads don’t block writes → smooth concurrency • JSON + relational = hybrid power Perfect for modern apps & APIs • Rock-solid reliability ACID compliant, battle-tested for decades • Extensions ecosystem PostGIS (geo), Timescale (time-series), pgvector (AI) • AI-ready Vector search + structured data in one place PostgreSQL is not just a database. It’s a data platform. #SQL #PostgreSQL #DataEngineering #AI #Database

9/12 — SQL as AI Infrastructure Daily SQL Tip 💡 In the AI stack, SQL is an infrastructure. Every AI system eventually needs: - structured storage - reliable joins - aggregations - consistency LLMs generate ideas why SQL generates trusted answers. #SQL #AIInfrastructure #DataEngineering

8/12 - Schema Design for AI Daily SQL Tip 💡 AI struggles with messy schemas. Clear naming makes AI better at writing queries. Good schema design means: • clear table names • descriptive columns • meaningful keys • documented metadata Think of schema design as prompt engineering for databases. #SQL #DatabaseDesign #AIEngineering

7/12 - RAG + SQL Daily SQL Tip 💡 RAG fixes hallucinations by grounding AI in real data. But vector search alone is not enough. Modern stack: Vector search → retrieve context SQL → compute exact answers Vectors find relevant information. SQL guarantees correct results. Together they power reliable AI systems. #SQL #RAG #SQLforAI #AIEngineering

6/12 - AI Agents Daily SQL Tip 💡 AI Agents don’t replace SQL. They orchestrate it. AI agent workflow: Question ↓ SQL query generation ↓ Database execution ↓ Result interpretation ↓ Action SQL remains the system of truth. Agents are just the interface. #SQL #AI #DataEngineering #Analytics

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AI vs SQL #AI #SQL

Why DBeaver beat MySQL Workbench (for me): 1️⃣ Multi-database support MySQL, Postgres, SQLite, Oracle - one tool. 2️⃣ Better data grid Editing, filtering, exporting feels faster and cleaner. 3️⃣ Smarter result tabs Multiple queries → multiple tabs → less chaos. 4️⃣ UI consistency Feels like a real data tool, not a server admin panel. Workbench feels like infrastructure while DBeaver feels like analytics. Tools don’t make you better, but friction makes you slower. #DBeaver #SQL

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#DataModeling #SQL #Analytics #AI

@Python_Dv Data Analyst explains what happened. Business Analyst explains why it matters. The best can do both.

Data Analyst OR Business Analyst ?? 🤔 📊Data Analyst 📊 • Role: Analyze existing data, communicate the results to help make business decisions. 📈 • Skills: SQL, data visualization, reporting, basic statistics, data wrangling. 🛠️ • Tools: Excel, Tableau, Power BI, SQL, Python. 🖥️ • Growth: High demand, rapid growth due to AI/ML advancements. 🚀 💼Business Analyst 💼 • Role: Understand business needs, processes, & challenges to suggest solutions using data & technology. 🏢 • Skills: Communication, stakeholder management, business process analysis. 🛠️ • Tools: Microsoft Office Suite, Visio, business intelligence tools. 🖥️ • Growth: Continuous demand, bridging business and technology gap. 🌉

Most SQL beginners mix this up: DDL ≠ DML. DDL defines the structure of your database. DML works with the data inside it. If you don’t separate schema thinking from data thinking, you’ll never design clean systems. Great SQL starts with structure. #SQL #DataEngineering #LearnSQL

@chocodonte @mdancho84 100% agree. The hardest part isn’t SQL or AI, it’s aligning on definitions. AI only works if the semantic layer is right.

@SQL_feed @mdancho84 Alright also, semantic models require iterations with the business, and sometimes they don’t really know what they want either. Let’s not assume the business knows. Many times they’re lost and we have to help them. AI won’t replace this modeling work.

RIP BI Dashboards. Tools like Tableau and PowerBI are about to become extinct. This is what's coming (and how to prepare):

@mdancho84 AI doesn’t replace BI. It replaces clicking. But the moment the underlying schema is messy, the AI becomes confidently wrong. Data modeling just became even more important.

I've never been a fan of Tableau and PowerBI. Static dashboards don't answer dynamic business questions. That's why a new breed of analytics is coming: AI Analytics.

Multimodal AI isn’t magic. It’s structured data shown in multiple forms. SQL defines the truth. AI explains it. That’s the stack in 2026. #SQL #SQLforAI #DataEngineering #RAG #MultimodalAI

WHERE filters rows. ON controls matching logic. If you filter the right table in WHERE, you remove NULL rows and lose the LEFT behavior. #SQLMentalModels #DataAnalytics

If you want to filter the joined table but keep unmatched rows, move the condition into the ON clause: LEFT JOIN orders o ON c.id = o.customer_id AND o.status = 'completed';

LEFT JOIN + WHERE = Accidental INNER JOIN One of the most common SQL bugs: You write a LEFT JOIN… and accidentally turn it into an INNER JOIN. Here’s the mental model that prevents it 👇 #SQL #LearnSQL