Databricks error in free edition Cannot create serverless connection, try again later

 The error you're seeing — "Cannot create serverless connection, try again later" — usually occurs in Databricks Community (free) edition because serverless SQL endpoints and premium features are not supported on that tier.

🔍 Why This Happens:

• Serverless SQL endpoints are part of Databricks SQL Pro or Enterprise tiers, and are not available in the Community Edition.

• The Community Edition supports only a limited set of features, including a single cluster and no access to advanced networking, serverless SQL, or Unity Catalog.

---

✅ What You Can Do:

1. Use a Standard Cluster Instead

Instead of serverless, use a standard interactive cluster:

• Go to Compute > Create Cluster

• Use the default settings (e.g., Runtime: DBR 11.x or 12.x)

• Make sure the cluster is started

• Then go to your SQL or notebook and run your queries using this cluster.

2. Upgrade to a Paid Plan (Optional)

If you need serverless SQL or Unity Catalog, consider:

• Upgrading to a Databricks Pro or Enterprise plan

• Use it through Azure Databricks, AWS, or GCP with a paid subscription

---

🛠 Alternative Workaround (SQL Access in Community Edition)

You can still write and run SQL using a notebook:

1. Create a notebook.

2. Use %%sql magic command in a cell:

   %%sql
   SELECT * FROM your_table_name
   

3. Run it on a running interactive cluster.
   
   Solution by OpenAI

🧠 Databricks Notebook Keyboard Shortcuts & Tricks

---

🧠 Databricks Notebook Keyboard Shortcuts & Tricks

Boost your productivity with these handy tips—plus gear recommendations for your data workspace!

🔑 Notebook Editor Shortcuts

Action	Shortcut (Windows/Linux)	Shortcut (Mac)
Run cell	Shift + Enter	Shift + Enter
Run cell and insert below	Alt + Enter	Option + Enter
Run all cells above	Ctrl + Shift + ↑	Cmd + Shift + ↑
Run all cells below	Ctrl + Shift + ↓	Cmd + Shift + ↓
Insert cell above	Ctrl + Shift + A	Cmd + Shift + A
Insert cell below	Ctrl + Shift + B	Cmd + Shift + B
Delete current cell	Ctrl + Shift + D	Cmd + Shift + D
Undo delete cell	Ctrl + Z	Cmd + Z
Move cell up	Ctrl + Shift + ↑	Cmd + Shift + ↑
Move cell down	Ctrl + Shift + ↓	Cmd + Shift + ↓
Toggle line numbers	Ctrl + M L	Cmd + M L
Comment/uncomment line	Ctrl + /	Cmd + /
Search in notebook	Ctrl + F	Cmd + F
Find and replace	Ctrl + H	Cmd + H

---

✍️ Markdown & Formatting Tricks

Use %md to write clean documentation or visual notes:

• # → H1

• **bold**, *italic*, `code`

• [Amazon Gear](https://www.amazon.com/s?k=developer+workspace&tag=vishwa2025-20)

• Lists with - or *

• Link to [Databricks Guide](https://www.amazon.com/s?k=databricks&tag=vishwa2025-20)

---

⚙️ Magic Commands

• %sql, %python, %scala, %r

• %run ./path/to/notebook

• %pip install pandas

• %fs ls, %fs cp, %fs head

---

💡 Boost Your Productivity

Optimize your work with these essentials:

• 🧠 Mechanical Keyboard

• 🖥️ Coding Monitor

• 💻 MacBook for Data Science

• 🪑 Laptop Stand

• 👟 Brooks Ghost Shoes

---

What's New in AI & Data Science – 2025 Trends

🔮 What's New in AI & Data Science – 2025 Trends

Stay ahead of the curve with the most important innovations shaping AI, BI, and analytics this year. These advances are redefining how forecasting, automation, and insights are delivered across industries.

---

🔍 Retrieval-Augmented Generation (RAG) 2.0

• What’s new: RAG models like GPT-4o integrate real-time external data sources (SQL, documents, APIs) at runtime.

• Use case: Business reporting tools powered by LLMs now pull live analytics from your warehouse and provide conversational explanations.

---

🧠 Agentic AI Systems

• Autonomous agents that reason, plan, and use external tools like SQL, Excel, and Notion to deliver insights.

• Use case: Generate, summarize, and email stakeholder-ready reports without human intervention.

---

🧮 Synthetic Data for ML Training

• Synthetic datasets mimic real data for training or testing without privacy concerns.

• Use case: Use in healthcare and finance to avoid HIPAA or GDPR risk.

---

📊 AI-Powered BI Dashboards

• Tools like Power BI Copilot and Tableau Pulse enable natural language questions like:

  “Why did revenue drop in April?”

• Use case: Smart dashboards for execs with zero SQL required.

---

🧮 Small Language Models (SLMs)

• Models like LLaMA 3, Mistral, and Gemma are used on-premise for secure AI deployment.

• Use case: Internal chatbots, report generators, and risk assessors.

---

📐 AI-Generated Code for Data Pipelines

• Tools like Databricks Genie and Hex Magic turn plain English into optimized SQL and ETL scripts.

• Use case: Describe your KPI — get auto-generated logic.

---

🌐 AI + Graph Analytics

• Use graph + LLMs for network analysis in fraud, provider networks, or supply chain.

• Use case: Detect patterns traditional SQL can’t reveal.

---

🧬 Multimodal Models in Analytics

• GPT-4o and Gemini 1.5 can interpret tables, images, dashboards, and explain anomalies visually.

• Use case: Upload reports and let AI act as your analyst.

---

📦 Foundation Models for Tabular Data

• Models like TabPFN, AutoGluon, and RT-DETR are purpose-built for structured data science tasks.

• Use case: Faster, simpler training for business analysts.

---

🛡️ Responsible AI

• Use tools like Fairlearn, WhyLabs, and IBM AI FactSheets to ensure fairness, explainability, and auditability.

• Use case: Build trust and pass compliance checks.

---

ChatGPT JD Edwards Finance reports

 https://www.youtube.com/watch?v=22-VY8niW6Y

What is a Multi-Agent AI System

🧠 What is a Multi-Agent System?

A Multi-Agent System (MAS) is a system composed of two or more intelligent agents that:

• Interact with each other (via messages, APIs, databases, etc.)

• Share or divide tasks

• May have different roles, goals, or knowledge

• Can work in parallel to increase performance and intelligence

---

🤖 Example of Multi-Agent Use Cases:

Use Case	Agents Involved	What They Do
Data Pipeline	Ingest Agent, Clean Agent, Analyze Agent	Each performs a step in a data workflow
Customer Support Bot	Product Bot, Billing Bot, Shipping Bot	Specialized bots hand off to each other
Smart Factory	Robot Agents, Scheduler Agent, Supervisor Agent	Coordinate production lines and resources
Multi-Tool AI Chat	SQL Agent, Python Agent, Web Search Agent	Choose the best tool for each user query

---

🧩 How They Interact

Agents can:

• Pass tasks to each other (e.g., like a relay team)

• Negotiate or vote on actions (collaborative AI)

• Compete (e.g., in simulations or games)

• Work under a controller agent or be fully decentralized

---

🛠️ Mini Example: Multi-Agent Workflow (Pseudocode)

# Agent 1: Data Reader
def agent_data_reader():
    data = read_sales_data()
    return data

# Agent 2: Analyzer
def agent_analyzer(data):
    return analyze_sales_trend(data)

# Agent 3: Reporter
def agent_reporter(analysis):
    generate_report(analysis)

# Workflow
data = agent_data_reader()
analysis = agent_analyzer(data)
agent_reporter(analysis)

---

🧠 Multi-Agent AI with LLMs

In modern AI (like LangChain, AutoGen, or OpenAgents), you can assign roles like:

• 👩‍💼 Manager Agent: Breaks down the task and assigns subtasks

• 🧮 SQL Agent: Writes SQL queries

• 📊 Analytics Agent: Interprets and explains data

• 📝 Writing Agent: Writes reports

They talk to each other through prompts and messages, like a team of experts.

---

📚 Tools to Build Multi-Agent Systems

Tool	Purpose
LangGraph / LangChain Agents	Chain agents with memory/tools
Autogen by Microsoft	LLM-based multi-agent framework
CrewAI	Define agents with roles and workflows
ReAct / Plan-and-Solve	Reasoning + Tool-use agents
Databricks AI	Cloud-native platform for AI agents

---

🚀 Want an Example?

Would you like a real multi-agent demo in Python or Databricks using OpenAI? For example:

• One agent reads DB

• Second agent analyzes data

• Third agent explains results in natural language

Let me know the scenario you'd like!

Databricks AI/BI 2025 Summit

Just returned from the Databricks AI 2025 Conference – and wow, the future is here.

From AI-native assistants to zero-code data pipelines, Databricks is redefining how we work with data.

Here are some of the highlights that blew me away:

🧞 Genie – An LLM-powered AI assistant built into the Lakehouse
🧠 LakehouseIQ 2.0 – Understands your data contextually, across silos
📥 Lakebase – Zero-code ingestion and transformation
🤖 GenAI Agents – Build your own enterprise copilots
🧱 Mosaic AI Workflows – Drag-and-drop LLM pipeline builder
📦 Unity Catalog Everywhere – Unified governance across clouds
📊 AutoMetric (2026) – AI-generated KPIs and metric monitoring
💸 Free Databricks Version – Yes, FREE! Learn, build, explore at no cost!

🔥 Whether you're in data engineering, ML, finance, or compliance—this platform is evolving to empower everyone with AI-first capabilities.

💡 Databricks isn’t just AI-ready. It’s AI-native.

Introduction to Agentic AI in Healthcare Finance

 https://youtube.com/shorts/bprUUOOMDwc

Agentic AI Books-  https://amzn.to/4lfgSjx