How to Leverage GenAI for Data Analytics

Data agents and AI agents are transforming how we interact with data, enabling conversational question-and-answer experiences similar to messaging apps. Users can ask questions in plain English about data stored in OneLake and receive accurate answers without needing technical expertise or writing code. This approach leverages parsing and validation to query tables based on the data source, generating SQL queries or DAX statements to provide summaries and insights, making data interaction more intuitive and efficient. #AI #DataAgents #OneLake #SQL #DAX #DataScience https://lnkd.in/gMATdEvh

Ever spent an hour writing a SQL query that should’ve taken five minutes? That used to be me — until I asked AI to do it for me. After experimenting with ChatGPT to debug SQL, I wondered — could AI go one step further and actually write my queries for me? That’s when I found Cursor. I described what I needed in plain English — “Get me retention metrics by month.” Seconds later, Cursor wrote a working SQL query. 🚀 From 60 minutes → to 5. AI literally became my coding partner. But here’s the unfiltered truth — it wasn’t magic: Some queries failed on complex joins I had to tweak prompts 2–3 times for accuracy It looked perfect… until syntax quirks showed up during execution Still, the time saved was real. I went from writing queries to reviewing and optimizing them — and that shifted how I approached automation altogether. I realized: AI doesn’t replace SQL mastery — it amplifies it. 💡 Next up in this series: How I trained Cursor to write even cleaner, smarter SQL — and remember my prompts for future queries. 💬 If you could automate one part of your daily SQL workflow, what would it be? 👇 Comment below — I’d love to hear what’s the first thing you’d hand off to AI. #AIInAnalytics #SQLAutomation #DataOps #CursorAI #AIWorkflow #Productivity

🔥 One SQL editor. Many wins. Zero friction. Just wrapped “SQL Analytics on Databricks” — and honestly, this feels like the future of analytics. Governance ✅ Performance ✅ AI ✅ Speed ✅ Highlights that instantly leveled up my workflow: ✨ Parameters — preset date ranges, editable at runtime, no more hard-coded filters. 🤖 AI SQL Assist — natural-language to query, instant fixes, ask follow-ups like ChatGPT. 📊 Instant Visuals — charts appear right below results — insights without switching tools. 🕒 Inline Query History — compare past runs & performance inside the editor itself. ⚡ Faster Flow — command palette, tab switching, versioned outputs — speed maxed. Outcome: cleaner governance, faster insights, and shareable SQL that scales across teams. If AI + analytics + user speed is the future — Databricks is already living in it. 🚀 #Databricks #SQL #UnityCatalog #AI #Analytics #BusinessIntelligence #DataEngineering

🪦 SQL Programming (1970–2025) I still remember the first time I wrote a SQL query. It wasn’t pretty — but when that first dataset came to life, I felt like I had superpowers. Fast forward to today, and the conversation has shifted. Now it’s all about AI. “AI will replace analysts.” “SQL is dead.” “Why learn to query when ChatGPT can do it for you?” But here’s the thing — AI isn’t replacing SQL. It’s redefining how we use it. Yes, AI can write a query faster than any of us. But it doesn’t know why you’re writing it. It can join tables — but it can’t join business logic with data meaning. Our value as analysts has never been about perfect syntax. It’s about context. It’s about asking the right questions. About translating numbers into narratives that inspire action. So no, SQL isn’t dying. It’s evolving — just like we are. The future isn’t AI vs. SQL. It’s AI + SQL + Human Intelligence. That’s the trio that will power the next era of data-driven decision-making. Adapt. Evolve. Thrive. 💡 Follow me for more insights on AI, Data, and the Future of Analytics. — #DataAnalytics #SQL #AI #DataScience #MachineLearning #PowerBI #DataAnalyst #FutureOfWork #StorytellingWithData #priyankahalpatrao

How to Build a Powerful Deep Research System | Towards Data Science: Deep research is a popular feature you can activate in apps such as ChatGPT and Google Gemini. It allows users to ask a query as usual, ... #bigdata #cdo #cto

New partnership between Tableau, OpenAI and Anthropic will see Tableau deliver dashboards and AI insights directly into ChatGPT and Claude, for faster insights to action in every workflow. This will allow you to upload a dataset, ask a question in natural language, and get an instant Tableau Visualisation all within your LLM conversation. Watch the Tableau Keynote at DF25 on Salesforce+ to learn more: tabsoft.co/4mXxQDb

An AI Agent can write SQL queries from natural language — and it does it flawlessly. All it takes is a couple of system prompts and a clear description, and boom — the accuracy is surprisingly high. I’ve been testing this in a project, and I’m genuinely impressed. No complex syntax, no manual tweaking — just clean, reliable SQL from plain English. Once the project wraps up, I’ll definitely do a demo to show how powerful this is in real-world automation. This is the kind of stuff that makes you rethink how we interact with data. #AI #SQL #Automation #NoCode #n8n #Zapier #Make #DataOps #TechSimplified

Day 3 of #30DaysOfPrompts - let’s dive in! Most people blame AI when their prompts fail… But 𝟵𝟬% 𝗼𝗳 𝘁𝗵𝗲 𝘁𝗶𝗺𝗲, 𝗶𝘁’𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗺𝗽𝘁, not the model. Here’s a quick checklist to fix that 👇 ✅ 𝗗𝗲𝗳𝗶𝗻𝗲𝗱 𝗔𝗜'𝘀 𝗿𝗼𝗹𝗲 𝗮𝗻𝗱 𝗲𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 → Tell the AI who it is (e.g., “You’re a data analyst with 5 years of experience”). ✅ 𝗣𝗿𝗼𝘃𝗶𝗱𝗲𝗱 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 → Include the background or purpose so AI understands why you’re asking. ✅ 𝗜𝗻𝗰𝗹𝘂𝗱𝗲𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 𝗶𝗳 𝗻𝗲𝗲𝗱𝗲𝗱 → Show what a good answer looks like to guide the tone and style. ✅ 𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗲𝗱 𝗼𝘂𝘁𝗽𝘂𝘁 𝗳𝗼𝗿𝗺𝗮𝘁/𝗹𝗲𝗻𝗴𝘁𝗵 → Be clear: do you want a list, a table, a paragraph, or a tweet? ✅ 𝗔𝘀𝗸𝗲𝗱 𝗔𝗜 𝘁𝗼 𝘁𝗵𝗶𝗻𝗸 𝗳𝗶𝗿𝘀𝘁 (𝗳𝗼𝗿 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝘁𝗮𝘀𝗸𝘀) → Tell it to reason before answering — helps improve accuracy and depth. ✅ 𝗕𝗿𝗼𝗸𝗲 𝗱𝗼𝘄𝗻 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 → Split multi-step tasks into smaller, clear parts for better responses. ✅ 𝗦𝗲𝘁 𝗮𝗽𝗽𝗿𝗼𝗽𝗿𝗶𝗮𝘁𝗲 𝘁𝗲𝗺𝗽𝗲𝗿𝗮𝘁𝘂𝗿𝗲 → Lower = factual & precise; higher = creative & varied. ✅ 𝗨𝘀𝗲𝗱 𝘀𝘆𝘀𝘁𝗲𝗺 𝗺𝗲𝘀𝘀𝗮𝗴𝗲 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆 → Use it to define rules, roles, or tone that stay consistent throughout the chat. Over to you: What tricks help you get better results from your prompts? —- I share daily insights on 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, 𝗟𝗟𝗠𝘀, 𝗥𝗔𝗚, 𝗮𝗻𝗱 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗗𝗲𝘃. Consider following for more. Consider Following: @khushalkumar #PromptEngineering #ArtificialIntelligence

As an analyst, debugging SQL errors used to be a time sink. Run a query → hit an error → Google it → open 10 different forums → scroll endlessly to find one answer that might work. That was my reality — until I tried ChatGPT. ✅ The first time I pasted an error (with the database I was using — BigQuery, MySQL, etc.), it gave me the fix in seconds. ✅ No rabbit holes. No endless scrolling. Just the solution. Slowly, I started leaning on it for more: - Adjusting column logic - Fixing syntax - Reformatting queries It didn’t save me hours right away — but it saved me from constant context-switching. And that was my first real productivity boost. Over time, it became clear: AI could handle repetitive query tasks while I focused on analysis and strategy. That realization led me to explore even more advanced tools — like Cursor — to go beyond fixes and actually write queries end-to-end. 💡 That was my turning point. 👉 What about you? What was your first experience with AI? #AIInAnalytics #SQL #DataAnalytics #Automation #FutureOfWork

Tableau is democratizing data analysis with OpenAI ChatGPT and Anthropic Claude — enabling anyone to have access to real-time data insights. Catch all the announcements from the Tableau Keynote at Dreamforce #DF25: sforce.co/47bMVeq