Your First AI Experiment: Run It Locally, Hassle-Free – Part 2 – Workflow Implementation

Don’t Worry About the Code

If you’re thinking, “I’m a SQL Developer or a DBA — how will I ever understand Python or Streamlit configurations?” — don’t worry. The code is intentionally kept very simple with line-by-line comments to guide you. Once you walk through it, you’ll realize how easy it actually is. You don’t need to memorize anything; just sit with it, focus for a little while, and everything will make sense.

Step 1: Install Python (if not installed)

1. Go to: https://www.python.org/downloads/windows/
2. Click Download Python 3.x.x (latest version).

3. Run the installer:
   • Check the box: “Add Python to PATH”
   • Choose “Install Now”

4. After installation, open Command Prompt and run:

python –version

It should show something like: Python 3.12.1

Step 2: Install Pip (Python’s package installer)

Usually installed with Python automatically. To confirm:

pip –version

If it shows a version, you’re good. If not, reinstall Python and make sure “Add to PATH” is checked.

Step 3: Install Streamlit and Required Packages

Open Command Prompt and run:

pip install streamlit pyodbc pandas

This installs:

• streamlit for building the web UI
• pyodbc to connect to a database server (SQL Server)
• pandas to display tables nicely

Step 4: Install Ollama and Pull LLM

1. Go to: https://ollama.ai/download
2. Download the Windows version and install.

3. After installation, open Command Prompt and run:

ollama run llama3

It will download the model (llama3:8b). This may take a few minutes.

After the download, it will show a chat prompt like: >>>, which means it’s working.

Exit using Ctrl + d.

Step 4.1: Try Ollama in your PC

You have successfully installed an opensource LLM on your PC. Now lets try to ask some questions and see how it responds.

Check what are the LLMs installed in your local using list command

> ollma list

Now, lets try to interact with this model by asking some questions:

Step 5: Create a Demo SQL Server Database

Open SQL Server Management Studio (SSMS) or Azure Data Studio and run this script to create a test DB:

CREATE DATABASE InsuranceDemo;
GO
USE InsuranceDemo;

CREATE TABLE Customers (
    CustomerID INT PRIMARY KEY,
    Name NVARCHAR(50),
    City NVARCHAR(50),
    Age INT
);

CREATE TABLE Policies (
    PolicyID INT PRIMARY KEY,
    CustomerID INT,
    PolicyType NVARCHAR(50),
    PremiumAmount DECIMAL(10,2),
    PurchaseDate DATE,
    FOREIGN KEY (CustomerID) REFERENCES Customers(CustomerID)
);

-- Insert sample data
INSERT INTO Customers VALUES
(1, 'Alice', 'New York', 30),
(2, 'Bob', 'Chicago', 45),
(3, 'Charlie', 'New York', 28);

INSERT INTO Policies VALUES
(101, 1, 'Life', 500.00, '2025-06-15'),
(102, 1, 'Health', 300.00, '2025-07-01'),
(103, 2, 'Auto', 400.00, '2025-06-25'),
(104, 3, 'Life', 700.00, '2025-07-10');

This creates two tables and adds a few records for testing.

Step 6: Create the Streamlit App (app.py)

1. Open Notepad or VS Code
2. Paste this code and save as app.py in a folder of your choice.

# =====================================================================
# The One AI - Your First AI Agent (Auto SQL or General Answer)
# - Ask any question in plain English.
# - If it's about your data (SQL-ish intent), the app asks the local LLM
#   to write SQL, runs it on SQL Server, and shows the results.
# - If it's a general knowledge question, the app asks the local LLM
#   to answer directly (no database involved).
#
# Runs 100% locally with Ollama (no cloud APIs, no openai module needed).
# Author: Uday Arumilli
# Date: 04/Dec/2025
# =====================================================================

import streamlit as st
import pyodbc               # Connect to SQL Server
import subprocess           # Call Ollama CLI locally
import pandas as pd
import re                   # Extract clean SQL from LLM output

# -----------------------------
# PAGE APPEARANCE / HEADER
# -----------------------------
st.set_page_config(page_title="The One AI", page_icon="🧠", layout="centered")

# Simple CSS header block for a polished look
st.markdown("""
    <style>
    .title-container {
        text-align: center;
        padding: 20px;
        background-color: #f0f2f6;
        border-radius: 12px;
        margin-bottom: 20px;
    }
    .main-title {
        font-size: 36px;
        font-weight: 800;
        color: #31333f;
        margin-bottom: 5px;
    }
    .sub-title {
        font-size: 18px;
        color: #5c5f66;
    }
    </style>

    <div class="title-container">
        <div class="main-title">🧠 The One AI - Your First AI Agent</div>
        <div class="sub-title">Ask general questions or query SQL Server with plain English</div>
    </div>
""", unsafe_allow_html=True)

# -----------------------------
# DATABASE CONNECTION
# -----------------------------
def get_connection():
    """
    Returns a live connection to your local SQL Server database using Windows Authentication.
    BEGINNER TIP:
      - Make sure you created the InsuranceDemo DB and its tables in SSMS.
      - If you use SQL Authentication (username/password), see the commented option below.
    """
    # Windows Authentication (Trusted_Connection)
    conn_str = 'DRIVER={SQL Server};SERVER=localhost;DATABASE=InsuranceDemo;Trusted_Connection=yes;'

    # --- If you need SQL Authentication instead, use this style (uncomment & edit):
    # conn_str = 'DRIVER={SQL Server};SERVER=localhost;DATABASE=InsuranceDemo;UID=your_user;PWD=your_password;'

    return pyodbc.connect(conn_str)

# -----------------------------
# OLLAMA CALL (LOCAL LLM)
# -----------------------------
def ask_ollama(prompt: str, model: str = "llama3") -> str:
    """
    Sends a prompt to your local LLM via Ollama CLI and returns its text output.
    BEGINNER TIP:
      - This DOES NOT use the internet or paid APIs.
      - Ensure you've pulled a model once: `ollama pull llama3`
      - You can change `model` to "mistral" or others you've pulled.
    """
    cmd = ["ollama", "run", model]
    # We open a subprocess, write the prompt to its stdin, and read the output from stdout.
    process = subprocess.Popen(
        cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True
    )
    output, _ = process.communicate(prompt)
    return output.strip()


# -----------------------------
# SQL CLEANUP / EXTRACTION
# -----------------------------
def extract_sql(text: str) -> str | None:
    """
    Cleans an LLM response and tries to extract the FIRST SQL statement.
    Handles extra explanation like "Here is the SQL..." or code fences ```sql.
    Returns the SQL string if found, else None.

    BEGINNER TIP:
      - LLMs sometimes add explanations or Markdown fences.
      - We strip those and capture only the part that looks like SQL.
    """
    # 1) Remove Markdown code fences like ``` or ```sql
    lines = text.strip().splitlines()
    clean_lines = [line for line in lines if not line.strip().startswith("```")]
    cleaned = "\n".join(clean_lines).strip()

    # 2) Remove "Here is the query"/explanations at the start of lines
    noisy_prefixes = ("here is", "sql query", "the query", "this query", "explanation")
    filtered = []
    for line in cleaned.splitlines():
        if any(line.strip().lower().startswith(p) for p in noisy_prefixes):
            continue
        filtered.append(line)
    cleaned = "\n".join(filtered).strip()

    # 3) Capture the first statement starting with SELECT/INSERT/UPDATE/DELETE up to the first semicolon
    m = re.search(r"(SELECT|INSERT|UPDATE|DELETE)\b.*?;", cleaned, flags=re.IGNORECASE | re.DOTALL)
    if m:
        return m.group(0).strip()

    # 4) Fallback: if no semicolon, but starts with SQL keyword, return the remainder
    m2 = re.search(r"^(SELECT|INSERT|UPDATE|DELETE)\b.*", cleaned, flags=re.IGNORECASE | re.DOTALL)
    if m2:
        return m2.group(0).strip()

    return None  # No SQL found

# -----------------------------
# LIGHTWEIGHT INTENT DETECTION
# -----------------------------
def is_sql_intent(user_question: str) -> bool:
    """
    Guess whether the question is about your demo database (SQL intent).
    BEGINNER TIP:
      - This is a simple keyword check. It's fast and works well enough for demos.
      - For production, you could ask the LLM to classify, but this is fine to start with.
    """
    domain_keywords = [
        "customer", "customers", "policy", "policies", "premium", "purchase", "city",
        "table", "tables", "sql", "database", "report", "count", "sum", "group by",
        "list all", "show me", "get me", "top", "total", "per city", "per year"
    ]
    uq = user_question.lower()
    return any(k in uq for k in domain_keywords)

# -----------------------------
# UI: INPUTS
# -----------------------------
st.caption("Type any question. The agent will automatically choose whether to answer directly or query the database.")

# Using a form prevents running on every keystroke
with st.form("ask_form"):
    user_input = st.text_input(
        "Your question",
        placeholder="e.g., What is the capital of India?  OR  Get total premium per city"
    )
    concise = st.checkbox("Prefer concise answers (for general questions)", value=True)
    submitted = st.form_submit_button("Ask")

# -----------------------------
# MAIN LOGIC
# -----------------------------
if submitted and user_input.strip():
    with st.spinner("Thinking..."):
        if is_sql_intent(user_input):
            # -----------------------------
            # SQL MODE: generate SQL + run it
            # -----------------------------
            st.markdown("**Mode:** SQL (database query)")

            # Prompt tells the LLM your schema and asks for ONLY SQL output
            sql_prompt = f"""
Convert the user's request into a valid SQL Server query.
Return only the SQL, no explanation.

Tables and columns:
- Customers(CustomerID, Name, City, Age)
- Policies(PolicyID, CustomerID, PolicyType, PremiumAmount, PurchaseDate)

User request: "{user_input}"
            """.strip()

            llm_raw = ask_ollama(sql_prompt)      # raw LLM output (may include explanations)
            sql_query = extract_sql(llm_raw)      # attempt to get just the SQL

            # Show raw output for learning/debugging
            with st.expander("🔎 Raw LLM output (for debugging)"):
                st.code(llm_raw)

            if not sql_query:
                st.warning("I couldn't detect a valid SQL query from the model's response. Please rephrase your request.")
            else:
                st.subheader("Generated SQL")
                st.code(sql_query, language="sql")

                # Execute the SQL safely
                try:
                    conn = get_connection()
                    cursor = conn.cursor()
                    cursor.execute(sql_query)

                    # Fetch rows and column names
                    columns = [col[0] for col in cursor.description]
                    rows = cursor.fetchall()
                    df = pd.DataFrame.from_records(rows, columns=columns)

                    st.subheader("Results")
                    st.dataframe(df, use_container_width=True)

                    # Auto bar chart for simple 2+ column aggregated results
                    if len(df.columns) >= 2 and pd.api.types.is_numeric_dtype(df.iloc[:, 1]):
                        st.caption("Auto chart (if it makes sense):")
                        st.bar_chart(data=df.set_index(df.columns[0]))

                except Exception as e:
                    st.error(f"Error running SQL: {e}")
                    with st.expander("Troubleshooting tips"):
                        st.markdown("""
- Ensure the **ODBC SQL Server driver** and **SQL Server** are installed and running.
- Confirm the **InsuranceDemo** DB and its **Customers** and **Policies** tables exist.
- If using **SQL Authentication**, edit the connection string in `get_connection()`.
- Check that the generated SQL is valid for SQL Server syntax.
                        """)

        else:
            # -----------------------------
            # GENERAL Q&A MODE: answer directly with LLM
            # -----------------------------
            st.markdown("**Mode:** General knowledge (LLM answer)")

            # Keep answers short if the checkbox is enabled
            style = " Answer briefly in one sentence." if concise else ""
            general_prompt = f"Question: {user_input}\nProvide a clear and correct answer.{style}"

            response = ask_ollama(general_prompt)
            st.subheader("Answer")
            st.write(response)

            # Show the exact prompt we sent (good for learning)
            with st.expander("🔎 Prompt sent to LLM"):
                st.code(general_prompt)

# -----------------------------
# END
# -----------------------------

# QUICK RECAP FOR BEGINNERS:
# - The app auto-detects intent using keywords (fast & simple).
# - SQL intent → LLM writes SQL → app runs it on SQL Server → shows results (and chart).
# - General intent → LLM answers directly (no database call).
# - No cloud APIs. Everything is local via Ollama.

Note: I am using VS Code, you can also use notepad++ or Jupytor Notebook or any other idea of your choice.

Step 7: Run the App

1. Open Command Prompt
2. Navigate to the folder where app.py is saved:

cd path\to\your\folder

3. Run the app:

streamlit run app.py

It will open a browser window at:
http://localhost:8501

Step 8: Ask Questions!

Try questions like:

• List all customers from New York
• Show policies purchased after July 2025
• Get total premium by city

The app will:

1. Use LLM to generate SQL.
2. Run it on SQL Server.
3. Show results in a nice table.

Now, I want to beautify my streamlit UI, its just a normal HTML markup language, I added to the app.py

General Knowledge Questions:

This time, let’s try asking a general question and observe how the system responds. Based on the input, our AI Agent correctly identifies it as a general knowledge query. Instead of querying the database, it uses the general knowledge prompt, passes the question directly to the LLM, and then displays the response returned by the LLM.

Sample Questions:

• What is the difference between a Data Lake and a Data Lakehouse? – Provide a one-line answer.
• Can you suggest some of the best cafés in Chicago Downtown?

Note: You may notice a few minor changes in the Streamlit UI, as I was experimenting with some CSS adjustments. Please ignore them. The latest code file is attached with this blog

Code Explanation:

1. PAGE APPEARANCE / HEADER

• This section is about how the Streamlit app looks when you open it.
• You set the page title, favicon icon, and layout (whether it should stretch wide across the screen or stay centered).
• Then you add a big title/header like “The One AI – Your First AI Agent”.
• This heading acts like a banner for your app, giving users a clear idea of what the app does as soon as they open it.
• It’s like setting up the front page of a website — making it neat, professional, and user-friendly.

2. IMPORTS AND INITIAL SETUP

• In this section, you import the Python libraries that your app will use.
• For example:
  • streamlit → for creating the web interface.
  • pandas → for working with SQL data results in table format.
  • pyodbc → for connecting to SQL Server.
  • ollama (or OpenAI client) → for sending queries to your local LLM (like Llama3).
• This section is like gathering your tools before starting the actual work. Without these imports, the code can’t run because Python won’t know what functions to use.

3. DATABASE CONNECTION FUNCTION

• This section defines how the app connects to your SQL Server database.
• It usually contains:
  • Driver details (SQL Server ODBC driver).
  • Server name (your PC or remote SQL server).
  • Database name.
  • Authentication details (like Windows auth or SQL login).
• The function creates a connection so the app can run queries and fetch results.
• Think of it like plugging a cable between your app and the SQL database — once connected, you can ask the database for any data you want.

4. LLM (AI ENGINE) SETUP

• This section sets up how your app communicates with your local AI model (running in Ollama).
• It defines the model name (like llama3) and the API endpoint.
• The LLM is the “AI brain” in your app — it helps interpret user questions, generate SQL queries, or even provide extra explanations in natural language.
• Example: If a user types “Get me premium collected city-wise”, the AI can generate the right SQL query or provide summaries.
• In short: This is the bridge between the user’s plain-English input and the actual database query.

5. USER INPUT SECTION

• This is the interactive part of the Streamlit app where users type their queries.
• A text box (st.text_input) is used so the user can enter something like:
  “Get me the total premium amount per city per year.”
• When the user presses Enter, the input is captured and passed to the AI engine or directly to the database.
• This is the conversation entry point — it’s where the user tells the AI what they want.

6. GENERATING SQL QUERY WITH AI

• Once the user provides input, the app uses the AI model to turn that input into a valid SQL query.
• Example:
  Input → “Get me total premium per city per year”
  AI Output →
• SELECT City, YEAR(PolicyDate) AS Year, SUM(PremiumAmount) AS TotalPremium
• FROM Premiums
• GROUP BY City, YEAR(PolicyDate);
• This step is important because most users don’t know SQL syntax.
• The AI translates natural language → database query.

7. EXECUTING SQL QUERY

• The app now takes the generated SQL query and sends it to the database.
• It runs the query and fetches the results into a pandas DataFrame.
• If there are any errors (e.g., wrong column names, missing tables), the app shows an error message instead of crashing.
• This part is like the engine room: it actually goes to the database, pulls out the data, and brings it back for display.

8. DISPLAYING RESULTS

• The results from the SQL query are displayed inside the Streamlit app.
• This is done using a table format, so the user can see rows and columns just like in SQL Server Management Studio (SSMS).
• Additionally, you can add visualizations (like bar charts, line charts, pie charts) using Streamlit’s charting features.
• Example: If the result is Premium per City per Year, you can show both the table and a bar chart comparing the cities.
• This makes the data much easier to understand at a glance.

9. EXTRA RESPONSES FROM AI (OPTIONAL)

• Apart from SQL results, the app can also ask the AI model to give additional context or insights.
• Example: If the query result shows Hyderabad collected the most premium, the AI might add:
  “Hyderabad is leading in premium collections consistently for the last 3 years.”
• This helps users get not just raw data but also meaningful insights.
• This section makes the app feel more like a real AI Agent instead of just a database viewer.

10. END

• The final part usually cleans up connections or just marks the end of the script.
• It ensures everything is wrapped up neatly.
• In Streamlit, you don’t always need explicit cleanup since the app reloads on every change, but it’s good practice to separate logic and keep the code modular.

Summary

Depending on your PC’s computing power, the response from the LLM may take anywhere from a few seconds to a few minutes. The purpose of this blog is simple—to demonstrate how easily and elegantly we can integrate different components with an LLM to build AI agents. Even with a basic agent code, an open-source LLM, and a regular CPU on a local machine, we can achieve meaningful functionality. The same principles can be scaled up to create powerful, enterprise-grade AI systems using licensed LLMs, high-performance compute, advanced programming and millisecond-response APIs.

The post Talk to Your Database with an AI Agent – No Cloud, No GPT License Needed – Part 2 appeared first on Data Meets AI.

Your First AI Experiment: Run It Locally, Hassle-Free – Part 1 – Workflow Explanation

Most people think working with AI requires deep knowledge of machine learning, large language models, or cloud services. That’s not true—at least, not for your first experiment.

The goal of this blog is simple

• To give you a hands-on AI experience right on your personal computer—no cloud, no license, no complicated setup. You don’t need to be an AI expert or even know the fundamentals of ML or LLMs. Instead, we’ll jump straight into building a beginner-friendly AI agent that can talk to your database in plain English.
• This experiment is intentionally lightweight. You’ll see how an AI model can take natural language, translate it into SQL queries, run them on a real database, and return structured results—all through a simple Streamlit app.
• Of course, in real-world large-scale systems, implementing AI requires more: production-grade models, robust design, enterprise services, clean code, and a strong UI/UX. But before we get there, it’s important to first experience how easy it is to start experimenting with AI on a single PC.

This blog is your entry point. By the end, you’ll have built your first working AI agent—something you can try, modify, and learn from—without touching the cloud or paying for a license.

• Title: Natural Language Querying of SQL Server using Agentic AI (Ollama + Streamlit)
• Environment: Windows PC (SQL Server already installed)
• Goal: Build a simple Streamlit app where users type questions in plain English, and the app uses a local AI model to convert it into SQL, run it, and show results.

Expected AI Workflow Explanation (Step by Step):

• 1. User Query the Streamlit Web App: The journey starts with the user. They open a simple website (built with Streamlit) and type a question in plain English, such as:
  • “What is the capital of India?” (General Knowledge)
  • “Show me all customers from New York with more than one insurance policy.” (Database-Related)
• 2. Internally triggers “User Query Understanding”: Once the user submits the question, the system doesn’t directly jump to answering. Instead, it first activates an internal process that tries to “understand” what type of question this is.
• 3. Keyword Detection (Intent Classification): The AI Agent now plays detective. It looks at the keywords in the question and tries to figure out the intent:
  • Is this a general knowledge question? (“What is the capital of India?”)
  • Or is this a database-related question that needs real data from our SQL Server? (“Show me all customers from New York…”)
• 4. Two Paths Based on the Query: Depending on the intent, the query flows into one of two pipelines:
  • General Query Path → for common, knowledge-based questions.
  • Database Query Path → for questions that require pulling actual business data from the SQL database.
• 5. Prompt Construction: Once the path is chosen, the system prepares instructions (a prompt) for the AI model. Think of a prompt as a “well-written request letter” to the AI.
• 6. Build Simple or SQL-Specific Prompt
  • If it’s a General Query, the prompt is short and direct (e.g., “Answer the following general knowledge question…”).
  • If it’s a Database Query, the prompt includes extra context such as the database schema and metadata, so the AI knows how to frame the SQL code correctly.
• 7. Send Prompt to LLM: This carefully written prompt is sent to a Large Language Model (LLM), like a powerful AI brain (LLaMA in this case), which interprets and prepares the response.
• 8. LLM Prepares the Answer: The AI brain now works on the query:
  • For General Queries → it produces a direct answer in plain English.
  • For Database Queries → it creates an SQL query to fetch the right information.
• 9. AI Agent Receives LLM Response: The AI Agent gets the answer back. If it’s general text, that’s easy. But if it’s SQL code, some extra steps are needed before showing results.
  • 9.A. Clean SQL: Sometimes the AI adds extra words like “Here is your query: …” which won’t work in the database. The Agent carefully cleans this so only the valid SQL statement remains.
  • 9.B. Connect to SQL Server: The Agent then connects to the actual SQL Server database where the actual data is stored.
  • 9.C. Execute SQL Query: It runs the cleaned SQL code against the database.
  • 9.D. Retrieve Results: The raw data comes back from the database, but it’s usually not user-friendly.
  • 9.E. Format Results: The Agent uses “Pandas” framework to neatly format the results into a readable table.
  • 9.F. Send results back to the Web UI: Send the prepared results data to the web UI
• 10. Display Final Answer on Web UI: Finally, whether it is a general knowledge answer or database query results, the system sends the final output back to the web page. The user sees a clear, easy-to-read answer or a table — ready to analyze, without needing to know any coding or SQL.

Implementation Workflow

1. Prepare Your Database
   • Install or use an existing SQL Server database.
   • Run the provided SQL script to create a schema and load some sample data (e.g., customers, policies).
2. Set Up Your AI Agent
   • Install Ollama to run a local large language model (LLM).
   • We’ll use LLaMA 3, a free and open LLM, running completely on your PC (no internet or cloud required).
3. Build the Backend Logic: Write a Python program that connects three things:
   • Your SQL Server database
   • The LLaMA 3 model (via Ollama)
   • A web interface (Streamlit)
4. Design the Web App
   • Use Streamlit to create a simple web application where you type questions in plain English.
   • Example: “Show customers from New York who purchased more than 1 policy.”
5. How the Query Flows
   • Step 1: You type the question into the web app.
   • Step 2: Agent code determines whether it is a General Knowledge Question or a Database related question.
   • Step 3: The app sends your text to Ollama, which runs the LLaMA 3 model.
   • Step 4: If it is a GK question, LLM answer will be displayed in the Streamlit UI and skips the remaining steps.
   • Step 5: If it is SQL related, the LLM converts your question into a valid SQL query.
   • Step 6: Python executes this query on SQL Server.
   • Step 7: The results are displayed in the Streamlit UI in a nice table format.
6. Run & Enjoy
   • Everything runs locally on your PC.
   • No cloud subscription, no license, no external API.

Tools You’ll Use

Tool	What It Is	Why You Need It
	A relational database system where we store and query structured data (like customers, policies).	To hold your sample insurance data that you’ll query with AI.
SQL Scripts	A set of .sql files containing commands to create a database, schema, and insert sample records.	Saves time — no need to manually type queries to set up data.
	A lightweight tool that lets you run large language models (LLMs) on your computer locally.	It’s the engine that powers your AI, making natural language → SQL translation possible.
	A modern open-source LLM created by Meta. Runs through Ollama on your PC.	The actual “AI brain” that understands your question and generates SQL.
	A programming language widely used in AI/ML and backend development.	Acts as the glue between the database, LLM, and UI.
	A Python library for quickly building web apps with minimal code.	Provides a simple, interactive interface to type questions and display results.
	Text editors/IDEs to write your Python and SQL code.	Helps you build and edit your app comfortably.

Continues…..

#AI, #GenerativeAI, #AgenticAI, #AIFreeCourse, #OpenAI, #LLM, #Llama, #SQL, #DBA, #DataEngineer, #DataAnalyst, #DataScience, #CloudData, #AIForDataProfessionals, #LearnAI,

The post Talk to Your Database with an AI Agent – No Cloud, No GPT License Needed – Part 1 appeared first on Data Meets AI.

How AI Grew From a 1950s Dream to Today’s LLMs, Agents, and Data-Powered Future

Imagine this

The year is 1956. Computers fill entire rooms. Data is on punch cards.

A group of scientists meet at Dartmouth and declare: “In a generation, machines will think like humans.” What sounded like sci-fi then is reality today.

• AI writes your SQL queries, summarizes execution plans, suggests indexes, and even automates pipeline monitoring — all inside the tools you already use.
• But how did we go from rule-based chess programs to Copilot, GPT, and Agentic AI?
  Let’s rewind and see how AI’s evolution unlocked this new reality.

Act 1 — The Birth (1950s–1970s): The Dream of “Thinking Machines”

• 1950: Alan Turing poses the famous Turing Test — can a machine fool a human into believing it’s intelligent?
• 1956: The term Artificial Intelligence is born at the Dartmouth Conference.
• Early systems can play games like chess or solve algebra puzzles using rules.

These were purely symbolic systems:

• If condition, then action.
• Like enormous stored procedures filled with IF-ELSE logic.

Limitation: Computers were too slow and too small.

“The dream was alive, but the machines weren’t ready.”

Act 2 — The Expert Systems Era (1980s): Rules Go Corporate

The 1980s brought Expert Systems:

• Rule-driven AI for businesses — diagnosing diseases, approving loans, troubleshooting machines.
• Knowledge was encoded as decision trees:
  • IF symptom = X AND age > 40 THEN diagnose Y.

For DBAs and Developers, this felt like static business rules hardcoded into logic.
Limitation: Every change meant manually editing rules — no “learning,” no “adaptation.”

Act 3 — The Machine Learning Era (1990s–2010s): Data Takes the Wheel

With more powerful computers and the rise of the internet, the game changed.
AI moved from static rules to Machine Learning (ML) — systems that learn from data.

• Algorithms could now predict:
  • Which customers might churn
  • What credit risk a person posed
  • Detect credit card fraud
  • Which email is spam
• Techniques like decision trees, regression, and neural networks became mainstream.

Limitation: ML models were powerful, but:

• Required data scientists, heavy math and Custom infrastructure
• Were complex for everyday Data people to use

Act 4 — The Deep Learning & Big Data Explosion (2010s): Machines Get “Vision and Voice”

This is when AI became mainstream.
With Big Data (Hadoop, Spark) and GPUs, neural networks grew deep enough to:

• Recognize images (self-driving cars)
• Understand speech (Siri, Alexa)
• Translate languages (Google Translate)

Data People started working alongside:

• Data Lakes, PySpark, and TensorFlow
• Handling huge datasets for AI training

Limitation: But the tools still felt specialized — not something a SQL Developer, a BI Developer or an Analyst could casually use.

Act 5 — The Transformer Revolution (2017): The Spark for LLMs

Here’s where everything changed.
In 2017, Google researchers published a paper called: “Attention Is All You Need.”

This introduced the Transformer architecture:

• A new way for AI to “understand” sequences of text.
• Instead of reading text word by word, it used attention mechanisms to see the whole context at once.

Why it matters:

• It made language models scalable.
• It powered the birth of BERT, GPT, T5, and all modern LLMs.

Think of it as:

• The indexing breakthrough of AI — suddenly, AI could “query” massive text quantities efficiently and contextually.

Act 6 — The Rise of Generative AI (2020–Present): AI That Writes, Codes, and Creates

Post-transformer, LLMs exploded:

• GPT (OpenAI), Claude (Anthropic), Gemini (Google), LLaMA(Meta), Grok (xAI), etc.
• Models that can:
  • Write any programming language
  • Summarize logs and database health checks
  • Document ETL pipelines
  • Generate dashboards or Power BI visuals on the fly

Generative AI doesn’t just analyze data. It creates:

• Text
• Code
• Images, music, videos
• Even entire pipeline configs

Act 7 — Agentic AI (The Present & Future): AI That Acts Like a Teammate

Today, AI is moving beyond being a passive assistant to being an active agent:

• An Agent can:
  • Read a requirement (plain English)
  • Query databases
  • Generate SQL or Python
  • Test and validate results
  • Trigger actions (alerts, dashboards, emails)
  • Loop until the task is done

For Data People:

• Think of it as a super-powered stored procedure that:
  • Writes its own code
  • Executes it
  • Decides what to do next — all under your supervision.

Types of AI You Need to Know

To make sense of today’s buzzwords and the broader AI landscape:

1. Narrow AI (or Weak AI / Classic AI) AI systems designed and trained for a specific task or a narrow set of tasks. They excel at their designated function but cannot perform beyond it.
   • Example: Spam filters, recommendation engines (e.g., Netflix, Amazon), voice assistants (Siri, Alexa) for specific commands, image recognition for classifying objects, query tuning, medical diagnosis systems for a specific disease.
   • Key Point: The vast majority of AI in practical use today is Narrow AI.
2. Generative AI (Gen AI) A powerful subset of Narrow AI that focuses on creating new, original content (text, images, audio, video, code, etc.) based on patterns learned from vast datasets.
   • Example: Generating human-like text (e.g., for articles, documentation, creative writing), creating realistic images from text prompts, synthesizing new music, generating code snippets, creating synthetic data.
3. Agentic AI (AI Agents) AI systems designed to reason, plan, and execute multi-step actions to achieve a specific goal, often by interacting with external tools, APIs, or other AI models. They can break down complex problems into smaller, manageable tasks. Agentic AI often leverages and orchestrates various Narrow AI components (including Generative AI) to achieve its objectives.
   • Example: AI systems that can independently research a topic, summarize findings, generate reports, send alerts, perform tests, optimize business processes by coordinating various tools, or even develop and refine software.

Future (Theoretical Types – long-term goal of AI research):

4. Artificial General Intelligence (AGI or Strong AI) A hypothetical type of AI that possesses human-level cognitive abilities across a wide range of tasks, including reasoning, problem-solving, learning from experience, understanding complex ideas, and adapting to new situations.
   • Examples: Currently theoretical. If it existed, it could perform any intellectual task that a human being can.
5. Artificial Superintelligence (ASI) A hypothetical AI that would surpass human intelligence in virtually every aspect, including creativity, general knowledge, and social skills.
   • Examples: Purely theoretical. Often depicted in science fiction as an entity with vastly superior cognitive capabilities to all human minds combined.

Why This Evolution Matters for Data Pros

Each phase of AI made it easier for everyday professionals to use:

• From static rules →
• To data-driven models →
• To AI that writes and acts for you →
• To Future (Strong → AI Superintelligence)

Now:

• You don’t need to be a data scientist.
• You just need to know how to use AI tools with your data skills.

And that’s what this series, “AI The One — With Uday,” will teach you.

Coming Next…

Even before diving into AI concepts and internals, try building a simple agent right on your local PC.

#learn #ai #sql #dba #llm #dataengineer #dataanalyst #genai #agenticai #openai #llama #aiagent

The post From Chessboards to Chatbots — The Fascinating Evolution of AI appeared first on Data Meets AI.

Interpreting AI for Data People

When people hear “AI,” they often imagine something mysterious — almost like magic. A black box that thinks and decides on its own. But the truth is much simpler:

AI isn’t magic. It’s just math, logic, and lots of data — packaged in clever ways.

If you’re a Data Pro (SQL Developer, DBA, Data Engineer, or Data Analyst) you already work with data every day.

The good news? That’s the foundation of AI.

In this post, let’s break AI into concepts you already understand, so you can see it for what it really is — and how you can work with it.

What Is AI Really Doing?

At its core, AI doesn’t actually ‘think’ like a human—at least not yet. It identifies patterns in data, applies probabilistic reasoning, and generates outputs based on probability.

How?By using:

1. Math — linear algebra, statistics, probability
2. Logic — rules, if-then conditions, optimization steps
3. Data — massive datasets to “learn” relationships

Think of it like:

• SQL Query Optimization: The optimizer finds the “best plan” based on statistics.
• Index Recommendations: Database engine suggests indexes by analyzing patterns in query usage.
• Power BI’s Key Influencers Visual: Uses regression and decision trees to explain what drives an outcome.

Everyday AI in Action:

• Spam Filter in Email → Learns patterns in words/senders and predicts what looks like spam.
• Google Maps Traffic Prediction → Uses past traffic data + real-time signals to estimate travel time.
• Netflix / Prime Recommendations → Suggests movies by finding patterns in what you and others watched.
• Voice Assistants (Alexa, Siri, Google): Understand and respond to natural language.
• Self-Driving Cars: Detect objects, predict movement, and make driving decisions in real time.
• ChatGPT, Gemini, Grok, Claud, Llama & Copilot etc.: Generate human-like text, code, image, video etc.
• Medical Imaging AI: Identify tumours or diseases from X-rays and MRIs.
• Customer Support Chatbots: Resolve queries with conversational AI instead of scripts.
• Personalized Ads: Predict what products you’re most likely to click on.

None of these are magic. They’re algorithms, math, and data-driven decisions — the same building blocks AI uses.

How AI Relates to What You Already Know

If you’ve ever:

• Used a CASE statement in SQL
• Written a WHERE filter to segment data
• Created a KPI or trend report in Power BI
• Tuned a query using statistics

…you’ve already touched on the core principles AI relies on.

AI just:

• Scales it up (billions of rows, huge dimensions)
• Automates the math (no manual regression or probability calculations)
• Learns patterns dynamically (not just from fixed rules)

Breaking Down AI in Simple Data Terms

For most Data People, AI feels mysterious because people throw around words like neural networks, embeddings, and transformers. But here’s how to think about it in terms you already know:

1. Machine Learning (ML) — Like Advanced GROUP BY and Pattern Detection

Imagine you’re analyzing customer churn (when customers stop using your service or leave your business). Normally, you’d:

• Write a GROUP BY query to segment customers.
• Calculate churn rates manually.
• Decide thresholds for “at-risk” customers.

Machine Learning does this automatically:

• It looks at historical data (who churned, who stayed).
• It finds patterns — age, region, purchase frequency, etc.
• It predicts who might churn next, without you manually coding the logic.

Think of ML as: An automated analyst that builds “smart” GROUP BYs and CASE rules based on past data.

2. Large Language Models (LLMs) — Like Super-Powered IntelliSense

You know how IntelliSense in IDEs like SSMS or VS Code helps you autocomplete code?
LLMs like GPT work the same way — but for any text.

• You type: “Get me all policies with claims above $10,000 this year.”
• It predicts the SQL query you want, using patterns it learned from billions of examples.

It doesn’t “understand” like a human — it just guesses the most likely text (or SQL or python) based on context.

Think of LLMs as: A hyper-intelligent autocomplete system, trained on massive datasets.

3. AI Agents — Like Dynamic Stored Procedures with Decision-Making

A stored procedure can:

• Take input
• Run queries
• Return results

But an AI agent can do more:

• Read a requirement (in plain English)
• Write the SQL or Python code
• Execute the query
• Analyze the result
• Decide what to do next (alert a team, generate a report, update a dashboard)

Think of AI Agents as: A stored procedure that not only runs, but also thinks about what to do next — based on goals you set.

Why This Matters

Because once you see that AI is just structured math and data, it stops feeling like a mystery. And you realize:  

1. You already understand its building blocks.
2. You don’t need a PhD to start using it.
3. You can build AI-powered workflows using the same tools you already know (SQL, Python, Power BI, etc.).

Final Takeaway

• AI isn’t a magical black box.
  It’s probabilities, rules, and data — supercharged by compute power.
• And as someone who already understands data, you’re halfway there.

Coming up next…

We’ll explore the evolution of AI and how it became what it is today.

Let’s keep building “AI The One — With Uday.”

#Foundation #learn #ai #sql #dba #llm #dataengineer #dataanalyst #genai #agenticai #openai #llama

The post AI Isn’t Magic — It’s Just Math and Logic with Data appeared first on Data Meets AI.

You’ve likely come across this question in team discussions, forums, or even casual lunch breaks:
“Will AI replace my job as a DBA? A SQL Developer? A Data Engineer? Or a Data Analyst?”

It’s a valid concern. The common response is, “AI won’t replace you — but someone who knows how to use AI might.” And that’s absolutely true.”

And in this blog, we’ll break that down clearly — role by role — and show how AI can actually make you more valuable, not obsolete.

The Truth: AI Isn’t Here to Replace — It’s Here to Amplify

Let’s clear something up early:

• AI doesn’t replace people. It replaces tasks — especially those that are repetitive, time-consuming, or rule-based.

So, what does that mean for your day-to-day work?

• Let’s look at real-world examples.

 What AI Can Do (And Does Well)

• Write boilerplate (standard) SQL & Python scripts
• Generate documentation for stored procedures or pipelines
• Summarize log files or execution plans
• Create sample and synthetic data
• Convert natural language to SQL or Python
• Identify anomalies in your ETL pipelines
• Assist in optimizing indexes or partition strategies
• Create test cases or unit test coverage for data workflows

 What AI Can’t (And Shouldn’t) Replace

• Understanding business context behind queries
• Designing data models and system architecture
• Making judgment calls on cost, security, compliance, performance
• Managing incident response or disaster recovery
• Collaborating across teams and translating data into decisions
• Thinking strategically about data quality, governance, and trust

Let’s Go Role by Role

SQL DBA: Will AI Replace You?

Not at all. But it will change what you spend time on.

 What AI can help with:

• Auto-tuning indexes
• Identifying slow queries and suggesting plans
• Reviewing SQL Agent job logs with summarization
• Analyzing access patterns and potential anomalies
• Advanced monitoring and alert management

 What still needs YOU:

• Leveraging AI services and tools effectively in your job.
• Disaster recovery and high availability planning
• Security, encryption, user access controls
• Capacity planning, backup strategies, and compliance
• Understanding the why behind performance issues, not just the what

Think of AI as your junior assistant — scanning logs, suggesting changes, running tests — but you make the final call.

SQL Developer: Will AI Replace You?

No — but it will redefine your workflow.

 What AI can help with:

• Writing basic to advanced queries from natural language
• Refactoring legacy SQL code
• Writing code for database objects like stored procedures, functions, or views etc.
• Generating documentation or comments
• Creating mock data for testing

 What still needs YOU:

• Leveraging AI services and tools effectively in your job.
• Writing business-specific logic
• Designing efficient joins and nested logic
• Understanding schema relationships
• Handling exceptions, edge cases, and data anomalies
• Collaborating with stakeholders on requirements

AI can help you write faster, but only you know which query actually meets the business need.

Data Engineer: Will AI Replace You?

Not at all. But it’ll give you superpowers.

 What AI can help with:

• Automating data cleaning or feature engineering
• Generating PySpark or Pandas code
• Writing Airflow DAGs or pipeline YAMLs
• Parsing log files and summarizing ETL errors
• Validating schema drift and pipeline failures

 What still needs YOU:

• Building scalable ingestion pipelines
• Managing orchestration across cloud and hybrid environments
• Handling edge cases in semi-structured or streaming data
• Ensuring data quality, lineage, and security
• Designing for cost-efficiency, latency, and reliability

AI will help you build and test faster — but you’re still the architect.

Data Analyst: Will AI Replace You?

No — but it will supercharge your insights and speed.

 What AI can help with:

• Interpreting dashboards and summarizing trends
• Automating report generation (Power BI, Excel, Tableau)
• Translating natural language questions into SQL or DAX
• Cleaning and preparing data (suggesting joins, imputing missing values)
• Identifying anomalies or outliers in datasets
• Generating narrative insights from charts
• Forecasting based on past trends (with Gen AI suggestions)

 What still needs YOU:

• Asking the right business questions
• Defining KPIs and metrics that align with strategy
• Validating and interpreting AI-generated insights
• Ensuring data is accurate, relevant, and actionable
• Presenting insights to stakeholders with proper context
• Judging whether trends are meaningful or just noise
• Bridging the gap between raw data and real-world decisions

AI can answer questions — but only you know which questions matter to the business.

The Real Danger Is Ignoring AI

It’s not about losing your job to AI — it’s about falling behind if you don’t learn how to use it.

People who learn how to use AI tools:

• Write SQL, Python 5x faster
• Debug and deploy pipelines faster
• Have more time for strategic work
• Deliver more business value in less time

AI Can Write Great Code — But Can You Trust It Blindly?

Let’s take a step back and compare this to something we all understand:

• Imagine you have a health issue.
• You feed your symptoms into an AI diagnosis tool, and it suggests a medicine.
  Now, would you use that pill immediately — without consulting a real doctor?
• Of course not.
• Because even though the AI might be right, the risk is too high if it’s wrong.

Now let’s apply that to our world of data:

You receive a new business requirement. You ask Gen AI (like ChatGPT, Copilot, Grok, Gemini, or Claude) to write the SQL or Python code for it. It gives you a well-structured, confident-looking script.

Now ask yourself:

• Do you commit it directly to source control?
• Do you trigger a deployment pipeline and push it to UAT or Production as-is?

No. And you shouldn’t.

Because AI doesn’t:

• Know your business logic
• Understand your data anomalies
• Enforce your compliance constraints
• Handle edge cases, null scenarios, or data corruption
• Consider your performance SLAs, index strategies, or audit triggers

Note:

• AI can be a brilliant code assistant, but it’s not a production-level decision-maker.
• Just like the AI doctor can assist diagnosis, but the real doctor makes the call.
  you are still the owner of the logic, quality, and consequences of the code.
• Use AI to write, but use your brain to validate, test, and decide.

This is where your experience and expertise as a SQL Developer, DBA, Data Engineer, or Data Analyst still reign supreme. The best professionals will learn how to work with AI, not trust it blindly.

Why Your Role is Future-Proof

AI excels at scale and speed but lacks the human touch—context, judgment, and innovation. You’re the bridge between raw data and business value, adapting AI tools to solve unique challenges. Moreover, companies need professionals to oversee AI systems, interpret results, and ensure ethical use—roles that demand your skills. The U.S. Bureau of Labor Statistics projects a 15% growth in data engineering jobs by 2032, with AI proficiency as a key driver.

Adopt to the Change

Instead of fearing replacement, see AI as your career co-pilot.

• Start small — experiment with tools like AI2SQL, Cursor AI, Claude Desktop, GPT Codex, GitHub Copilot, Azure SQL Copilot, or Amazon CodeWhisperer to generate or refactor SQL queries or Python code.
• Use GaussMaster or Redgate SQL Monitor for intelligent database monitoring.
• Explore Windsurf, DataStax Astra Assist, or Microsoft Fabric Copilot to prototype data pipelines and transformations.
• For Data Analysts, try ThoughtSpot Sage, Power BI Copilot, or Qlik AutoML to analyze data with conversational AI. Dive into dbt Cloud’s AI Assist for transforming data in the modern stack, or use Hex Magic Cells for guided data storytelling.

The Bottom Line

AI won’t replace you – it will redefine your impact.

Let’s shift the mindset. Instead of thinking: “Will AI replace me?”, start asking “What can AI take off my plate, so I can focus on higher-value work?”

“The future isn’t about replacement; it’s about augmentation. Are you ready to steer this journey?”

Coming up next…

“AI Isn’t Magic — It’s Just Math and Logic with Data”

#learn #ai #sql #dba #llm #dataengineer #dataanalyst #genai #agenticai#openai #llama

The post “Will AI Replace You?” appeared first on Data Meets AI.

From SQL to AI — Your Journey Starts Here

You’ve written thousands of lines of SQL, Python. You’ve managed terabytes of data, thousands of databases, optimized queries, built pipelines, and protected mission-critical databases.

But now, there’s a new buzz in the air — AI.

It’s not just for data scientists or machine learning PhDs anymore.

AI is entering your world — and it’s not knocking gently.

If you’re a SQL Developer, DBA, Data Engineer, BI Developer, or Data Analyst, this blog series is your invitation (and roadmap) to enter the world of AI — simply, clearly, and practically.

Let’s begin by answering one important question:

Why Should You, a Data Professional, Care About AI?

• Because AI is no longer optional — it’s becoming part of the platforms, tools, and systems you already work with every day.
• You don’t need to switch careers to become a Machine Learning Engineer.
• But to stay relevant, evolve your role, and enhance your impact, you need to understand how AI fits into your world.

Here’s why:

1. AI Is Already Embedded in the Tools You Use

AI is no longer isolated in research labs — it’s baked into your IDEs, SQL engines, notebooks, and reporting tools.

• Copilot in SSMS, Azure Data Studio, Power BI, VS Code, GitHub: Helps you write code, generate documentation, fix performance issues — just by typing a prompt.
• SQL Server 2022 & Azure SQL: AI-driven query optimization, memory grant feedback, and intelligent plan corrections.
• Power BI & Microsoft Fabric: Natural language to report, Smart Narratives, and Copilot-driven dashboards.
• Microsoft Purview: Uses AI for data classification, lineage inference, and policy recommendation.
• Conversational and Code Assistants: ChatGPT (Open AI), Llama (Meta), Claude (Anthropic), Gemini (Google), Grok (xAI), Microsoft Copilot, GitHub Copilot, Perplexity AI, Cursor AI, Confluence AI (Atlassian) etc. 

2. AI Can Automate the Repetitive Stuff You Hate

Imagine:

• Writing complex joins with a prompt like “Get all active policies with claims over $10,000 in the last year.”
• Summarizing a 200-line query result in a few bullet points
• Detecting anomalies in ETL logs before they break production

AI tools like AI2SQL, Cursor AI, and GPT, Claud are making this possible today — not in some far-off future.

3. AI Is Becoming Mandatory — Even in Job Interviews

 “Tell me how you’ve used AI in your recent projects?” This is no longer a data scientist–only question.

In today’s interviews:

• AI is expected as a foundational knowledge area — like SQL, Python, Git, or Cloud.
• Employers want data professionals who can enhance workflows, automate tasks, and understand LLM-driven features in tools.
• Even non-technical roles are being asked about Copilot usage, prompt engineering, LLM Integrations, AI-assisted automation, or working with AI Agents.

Whether you’re applying for a SQL Developer, Data Engineer, BI Developer, Data analyst, or SQL DBA role, AI skills can set you apart — or leave you behind.

4. AI Bridges the Gap Between Data and Business Value

As a data pro, you’re already great at working with data. But AI helps you translate that data into business insights and decisions.

• Build agents that answer business questions directly from your database
• Use AI to detect patterns and exceptions faster than any dashboard
• Convert raw reports into executive summaries
• Enable non-technical users to ask questions in natural language and get smart answers

In short: AI helps you move from “Data Delivery” to “Business Impact.”

5. Your Role Is Evolving — Accept and Adopt to the Change

• You’re not just a code writer or database gatekeeper anymore.
• You’re a data strategist, an automation enabler, a knowledge engineer. AI makes your role more impactful, not less important.
• In fact, AI needs people like you — People who know how data works, how to structure it, and how to ask the right questions.

What This Blog Series Will Do for You

This series — “AI The One” for Data Professionals — is built just for you.

Coming up next…

The question on everyone’s mind — Will AI replace you?

#learn, #ai, #sql, #dba, #llm, #dataengineer, #dataanalyst, #genai, #agenticai, #openai, #llama, #AgenticAI, #AI, #AIForDataProfessionals, #AIFreeCourse, #CloudData, #DataAnalyst, #DataEngineer, #DataScience, #DBA, #GenerativeAI, #Llama, #LLM, #OpenAI, #SQL

The post Why Every Data Professional Should Care About AI Now? appeared first on Data Meets AI.

You’ve mastered databases. You speak fluent SQL. You can deal with Bigdata and build data pipelines in your sleep. But what if the next edge isn’t about writing better queries — it’s about making your data… intelligent?

Why This Blog Series? Why Now?

The world of data is changing — fast.

AI is no longer just a buzzword. It’s baked into the tools you’re already using:

• Azure AI & Copilot Integrations (SQL Server Management Studio, VS Code, Power BI, Microsoft Fabric Notebooks, etc.)
• Intelligent Query Recommendations
• Automated data preparation pipelines
• AI based Data Governance
• Powerful AI tools are transforming the way SQL Developers, DBAs, Python Developers, and Data Engineers write code — helping them code faster, smarter, and with fewer errors. These include intelligent code assistants like GitHub Copilot, Copilot with Azure SQL, OpenAI Codex, Anthropic Claude Code, Cursor AI, Kite, and AI2SQL etc.

Soon, understanding AI won’t be optional — it will be expected.

But here’s the problem:

Most AI courses are built for data scientists, ML engineers, or Python-first developers.
They’re full of math, jargon, and abstract theory.

What about us?

The SQL Developers, DBAs, Data Engineers, and Data analysts who already understand data better than most?

That’s exactly why I created “AI The One” — an AI learning path made for people like us.

What Is “AI The One”?

A practical, blog-style course that teaches AI and Agentic AI from your perspective as a data professional.

• No fluff.
• No heavy math.
• Just clean, structured, step-by-step learning — grounded in the world you already know: Data, SQL, data modelling, indexes, pipelines, storage, and logic.

What You’ll Learn — Without Getting Overwhelmed

• The real-world meaning of AI, ML, LLMs, Gen AI, and Agentic AI
• How AI systems relate to what you already know in SQL & Data Management
• Simple hands-on examples with your data
• Python for SQL minds — just what’s needed, nothing more
• Concepts like vectors, embeddings, and prompt engineering
• How tools like LangChain, LangGraph, AutoGen, and Copilots will reshape your daily work
• And how you can go from data expert → AI-empowered professional

Who Should Follow This?

• SQL Developers who want to future-proof their skills
• SQL DBAs who want to understand what AI is doing to their data landscape
• Data Engineers who want to extend their pipelines into intelligent automation
• Data Analysts who want to elevate their insights using AI-powered data exploration
• Anyone from the data world who has zero AI experience but big curiosity

Why You’ll Love It

• It speaks your language (SQL, tables, indexes, functions, pipelines, execution plans)
• Every concept connects to your existing knowledge
• No need to be a Python ninja or statistics wizard
• You’ll learn not just how, but why and where AI fits in
• It’s fun, practical, and focused on real impact in your daily work

Coming Up Next…

Stay tuned for the first blog in the series, where we’ll explore:

“Why Every DBA, SQL Developer and Data Engineer Should Care About AI Now”

#learn #ai #sql #dba #llm #dataengineer #dataanalyst #genai #agenticai#openai #llama

The post Introducing: “AI The One” for Data Professionals appeared first on Data Meets AI.

.

Links to other parts

A discussion between a CxO and a senior Data Architect Part 1

A discussion between a CxO and a senior Data Architect Part 2

A discussion between a CxO and a senior Data Architect Part 3

A discussion between a CxO and a senior Data Architect Part 4

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Background: We have been going through a discussion that took place between senior leadership and a data architect. Here the final part of the series continues.

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Discussion Follows:

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Alison: We are currently holding stakeholder financial portfolios, customer personal identities, and other sensitive information which is classified as confidential and restrictive. When I say moving to the cloud, the first thing that comes to my mind was data security. We are going to store our corporate data in a public cloud data center like Azure or AWS. Since you are the data owner, you need to convince me about the cloud migration by explaining the public cloud security capabilities. Considering I have zero knowledge about cloud security, can you list out all possible security risks and how Cloud providers can handle them?

Vasumat: Security is the top concern for any business. Security is a shared responsibility between the cloud provider (Azure, AWS, Google Cloud, etc.) and the customer in a public cloud platform. Three fundamental objectives of data security are A) Confidentiality – Ensuring data privacy; B) Integrity – Protect data from accidental or intentional alteration or deletion without proper authorization; C) Availability / Data Resiliency – Despite the incidents data continues to be available at a required level of performance.

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Possible security risks, reasons, solutions / preventive measures:

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Alison: I am a little curious about security because I started my career as a security engineer. Though you gave Great insights, you answered more generically about the common security concerns and their workaround. Now I would appreciate it if you could describe any public cloud provider’s security features. Just top 5 to 6 features are fine.

Vasumat: Sure! Almost all top cloud providers are providing air-tight security controls. It’s all about how customers efficiently utilize those features. I’ll talk about Azure security features.

Application Security:

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Storage Security:

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Network Security:

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Database security:

Other Security:

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Security monitoring and alerts:

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Alison: Consider this as the last question, have you ever experienced or seen any failures or disasters in Cloud? Maybe while migrating or post-migration?

Vasumat: Before answering this, I want to define the word “failure”. Failures are two types A) Not able to reach the expectations as per the estimated plan. Ex: Cost is increased, failed to migrate within the timeline, performance is decreased in the cloud. B) Migration plan itself failed hence rollback. Since we spend adequate and quality time on the analysis and planning phase, fortunately, I never fall into option “B”. However, when we are dealing with a huge enterprise, whatever the care we take, there are still unknown things that essentially impact our estimations.

But consultants reach me for help and suggestions (both from inside and outside the organization), and there I have seen the ground-level mistakes done. Let me quickly explain a few case studies:

Alison: Sorry, for stopping you in-between. Did it happen in your organization? I mean as a vendor.

Vasumat: No mam, I write blogs so have some contacts and one of my followers called me for help.

Alison: I am excited to know your suggestion there.

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Alison: I reckon you suggested the right thing there. Damage control is the first step in any incident. Do you have any other use cases?

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Vasumat: Want to hear more?

Alison: I think that’s more than enough. What I observe is most of them are due to human error, isn’t it?

Vasumat: Certainly! 80% of the failures are due to human mistakes. You might have heard about the recent data leak that exposed 38 million records, including employee databases, COVID-19 vaccination statuses, etc. They hosted 1000 plus applications on Microsoft PowerApps and due to a simple misconfiguration data was exposed to the public.

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Alison: Yes! I do. But, in my opinion, the problem is from both sides. Azure cloud must make it private by default instead they are making it public. Developers forgot to make it private.

Vasumat: That’s why we always say that SECURITY is a shared responsibility. Azure automated monitoring system identified it and sent a security notification saying that their sensitive data was exposed to the public. The customer immediately reacted and made access private.

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Alison: How can we prevent this?

Vasumat: Three things, A) Continues testing and improving test scenarios. We need to have customizable security testing tools and services that fit our business. B) Automation. First, ensure that our environment is stable and automate one by one process. C) Monitor and improve the automation procedures.

Alison: Make sense to me.

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Alison: Vasumat, have you ever worked in pre-sales?

Vasumat: Well, I can’t claim that I am experienced in the end-to-end pre-sales life cycle but it’s a part of any architect’s role. The sales team involves architects in customer discussions. I am experienced in helping pre-sales teams in preparing the technical documentation, creating mockups, giving presentations, and most importantly holding sales teams in customer discussions when they are giving unrealistic promises, buying time from the customer for performing POC, etc.

Alison: In that case, you already know a lot about pre-sales.

Vasumat: I have a query here, can I?

Alison: Sure!

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Vasumat: As part of sales, before getting the PO approved, we need to understand the customer requirement to prepare the best possible solution, impress and win the deal from the customer. But your requirement says that your infrastructure needs to be migrated. This means there is no customer concept here. Then I would like to know why you are specific about pre-sales.

Alison: I understand your concern. Let me put it this way. We are not going to have the entire IT team with us, instead, we would give the migration project to multiple vendors. But we need to make sure of holding the KEY people with us, people like architects who can understand the core business, design the architecture, and essentially drives the entire cloud journey. Though we are ready to migrate our workloads to the cloud, it doesn’t mean that we got the green signal. Business vertical wise we need to pass through approvals from App owners, Audit teams, Cyber security, stakeholders, etc. It is not pre-sales, but almost needs to have a similar preparation…

Vasumat: To convince them for the cloud migration.

Alison: Exactly. Hope it answered your question.

Vasumat: 100%. Thanks for that.

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Alison: Well, that’s all from my side. It’s already been a long day for you, in fact, I took 80 min extra, my apology for that. I would appreciate your time, patience, and commitment. I am open to questions if you have any.

Vasumat: You don’t need to apologize. For me, it’s one of the best technical discussions I have ever had. I hope someday I’ll sit and have a coffee from the mountain view place. Any idea when I can hear about the next steps of the hiring process?

Alison: Hopefully very soon!

Vasumat: Thank you a lot!

Alison: Viswa will escort you to the security check; From there you can take the shuttle and reach Exit-1. Have a good evening.

Vasumat: Good evening, Alison. Bye!

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This discussion went on for 7:30 hrs. and the person who attended this interview had to go through a discussion with the head of the Data Engineering vertical and a final discussion with HR followed by the offer. It took 5 more weeks to get the final offer in hand.

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***Note:

I will take some time and prepare the blog on the discussion that happened between a Senior Data Architect and a Head of Data Engineering. That would be fascinating as they talk about building modern data platforms using various architectures including Lambda, Kappa, Data Mesh, Data Fabric, Data Lake, Data Warehouse, etc. using Databricks, Synapse, Kafka, Snowflake, IoT, Stream Analytics, Cosmos DB, Red Shift, Azure SQL Database, RDS, S3, Blob, Spark Streaming, etc.

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The post A discussion between a CxO and a senior Data Architect Part 5 appeared first on Data Meets AI.

Links to other parts

A discussion between a CxO and a senior Data Architect Part 1

A discussion between a CxO and a senior Data Architect Part 2

A discussion between a CxO and a senior Data Architect Part 3

A discussion between a CxO and a senior Data Architect Part 5

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Background: We have been going through a discussion that took place between senior leadership and a data architect. Part – 4 continues.

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Discussion Follows:

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Alison: Can you tell me the most complex migration that you have done till today?

Vasumat: Sure! We have migrated workloads for one of the European-based engineering and construction firms. They operate from 85 offices across the globe with a staff of 13000 (IT Staff 4300) and revenues over $6.5 billion with a customer count of 0.4 million. We have created the business case by considering two major challenges. A) Data management on expensive storage – They are generating enormous data and struggling to maintain, and manage data and in handling disaster recovery solutions at their data centers. B) Scalability – Systems are not scalable to handle heavy workloads. We proposed the cloud solution by addressing these two challenges. Projected Azure as the suitable target by considering the TCO, ROI, and compatibility (majority of their workloads are using Microsoft products .Net, Windows OS, SQL Server, Office suite including Email, SharePoint, etc.).

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Alison: Great! Can you summarize the migration?

Vasumat: Sure!

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Alison: That’s amazing, I think we are looking for similar background. Out of curiosity, how did you handle the legacy systems?

Vasumat: Legacy systems are always a big deal. I’ll tell you an example, our client has been using a simple application based on SQL Server 2000 running on Windows Server 2003. They are neither retiring that application nor ready to invest in refactoring. Hence, we are left with only the option of moving it as is to the Cloud VM. We spent some time preparing a detailed analysis report with the possible problems and issues and projected Infront of the business team. Ex: SQL Server 2000 is a 32-bit application hence it has compatibility issues with the OS, limitations utilizing the memory, also most importantly product support has been expired years ago. Hence it can’t fit the modern data world. To make it simpler, we suggested re-platforming SQL Server 2000 to Azure SQL Database. So, we do not need to invest more money, it’s just a few extra efforts to migrate schema and data to Azure SQL Database. After checking the possibilities, cost ratio, performance gain, and risk involved, we got the go-ahead for re-platforming and we successfully did it too. Likewise, for migrating Windows Server 2003 VMs to Azure:

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Alison: Do you remember the TCO differences and improvements after the cloud migration? Approximate figures would suffice.

Vasumat: Yes, I do! After successful cloud migration, we monitored for some time (a few weeks/months depending on criticality) and captured the crucial parameters, predicted the future values, and compared it with the on-premises numbers. A detailed KPI report was given to the business. The major advantage of the cloud is less or no Capital Expenditure (money we spend to purchase and maintain the IT infrastructure). KPI values comparing with on-premises:

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Alison: Let’s suppose, I need a machine with 32 core, 64GB RAM, and 16 TB HDD configurations. From the cost point which one is the best option, purchasing a physical server or an Azure VM / AWS EC2?

Vasumat: For the physical server, we may need to spend $9000 approx. as a one-time investment. To get an Azure VM with a similar configuration we may need to pay $900 per month. By the second month, we invest $1800 and by the 10^th month, we pay $9000 in total for an Azure VM.

Alison: Which means owning a physical server is the winner from the cost point. Isn’t it?

Vasumat: Certainly! But before concluding, we should consider the facts:

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Alison: Got you. What was the time taken to complete this migration project?

Vasumat: It took 24 months out of which 8 months for analysis and planning and 16 months for execution and optimization.

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Alison: You might have worked with many clients till today. What is the highest business impact that you have seen due to unplanned downtime?

Vasumat: One of the manufacturing clients projected their business impact as $2.3 million for unplanned downtime for 4 hours (It was down due to hardware failure).

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Alison: Vasumat, you must have handled VLDB (Very Large Database) migration, right?

Vasumat: Yes! Till now the largest single database I migrated is 8.5 TB

Alison: That’s a real VLDB. Can you describe a few tips for handling VLDB migration?

Vasumat: For VLDB, there are three major things that we need to worry about A) Backup B) Consistency check C) Index maintenance. These operations may run from hours to days depending on the database size and available resources. For us, VM with configuration 32 Core, 448 GB RAM, 20000 MBPS network bandwidth, it took 12.3 hrs. to complete the full backup.

Best practices in dealing with VLDB are:

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Alison: So, you migrated that 8.5 TB database to Azure VM. Is it?

Vasumat: Yes! We migrated it to an Azure VM, but issues were remaining the same. It’s been taking a half day for a full backup, and differential backup (4 days after full backup) was also taking 8 hours. Also, the maximum Blob size is limited to 195 GB, hence we had to take the split backup to ensure we do not hit the maximum size of 195 GB. When I approached an expert engineer from the Azure team, he suggested me the file snapshot approach, we performed a POC with success and then implemented the same for our production database.

Alison: Can you explain how did you do that?

Vasumat: We have a feature using which we can place SQL Server Database files as Page Blobs in Azure Blob Storage. With this, we get the benefit of Azure Storage which is not available in the traditional storage system. For example, consider Instant File Initialization (IFI), in traditional storage if we do not enable IFI when new space is requested, the system performs zeroing process (put zeros on the disk to avoid security risk). But it is only applicable for the Data file and not for the Log file. When it comes to Azure Blob Storage, without enabling IFI, it automatically takes care of the zeroing process and most importantly IFI is applicable for Log files as well. When we call Put Page API, it simply clears the range and guarantees that reading from a cleared range will always return zeroes. We tried allocating the logfile from 50 MiB to 1 TiB and it took 1 second.

Alison: But how it resolved your backup issue?

Vasumat: I am coming to that; We have a feature called File Snapshot Backup. Unlike traditional streaming backups, a File snapshot Backup creates a storage snapshot for each SQL Server Database file. Can you believe, for the 8.5 TiB production database while it was operating where queries were running with 72% CPU utilization, full backup was completed in 2 minutes 30 seconds?

Alison: That’s amazing. Even with 10 GBPS speed, it can hardly take 1 TB backup in 2 min. How come it’s been taking 8.5 TB?

Vasumat: Because no data movement has occurred, instead it simply stores pointers to these snapshots and other backup metadata. Hence it is faster and maintains backup consistency. And coming to running data consistency checks (DBCC), we can quickly restore (taking 5 seconds) the latest file snapshot backup into other SQL instances and run DBCC commands. Two benefits, A) We are fully checking the consistency (No physical only or partial checks) B) Checking on the latest copy without distributing the production workload.

Alison: Okay! That sounds fascinating. So, do you prefer the same approach for all SQL workloads?

Vasumat: Certainly not! Every feature is designed for a specific cause hence it has its limitations, and we use it only when our database is fit into this feature. For example, it doesn’t support FileStream data, geo-replication for a storage account, In-Memory OLTP, and, Always On failover cluster instances. Also, standard storage (500 TB limit) can’t be used due to performance issues. But the premium storage account is limited to 35 TB.

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Alison: I understand. I specifically asked you about VLDB as we have SAP systems running on-premises. Do you see any SAP offerings from Azure?

Vasumat: I didn’t have experience in migrating SAP workloads to the cloud. Nevertheless, I participated in a case study discussion in a customer place, where they explained how SAP systems with VLDB were migrated to Azure. Ideally, I can say that I know the theory.

Alison: Then I would like to know about your theory.

Vasumat: Sure! SAP systems mainly require (at least from the data point) A) Larger storage B) Elastic Compute Capacity. By addressing these parameters, Azure is offering two optimized solutions A) SAP on Azure Virtual Machine; B) SAP HANA on Azure Large Instances. Both options are certified to host SAP workloads and it is the Best of Both Worlds (SAP and Azure Cloud). Because, along with the SAP, we can utilize the latest features of Azure Ex: Automation, High Availability with guaranteed SLA, security, scalability, cost & operational efficiency, etc. Azure supports SAP deployment including SAP NetWeaver, SAP S/4HANA, BW/4HANA, BI, and HANA in scale-up and scale-out scenarios.

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Alison: That’s interesting. In the case study that you are talking about, from the customer business point, what was the top benefit that they gained by migrating their SAP to Azure? Only if you remember.

Vasumat: I may forget the process and approach but always try to remember the KPI because that tells us the success rate of our migration strategy. There were two major factors A) Projected 5-year cost savings as between 60 to 65% B) Time for provisioning and building new components for Very Large Workloads drastically improved from 1 month (on-prem) to 1 week (Azure).

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Alison: Do you know how they migrated large SAP databases to the cloud?

Vasumat: Again, it’s a theory as I didn’t experiment on it.

Alison: Fine, your theory is from a real use case. So, for me, it’s more than a theory. Please go ahead.

Vasumat: If it is a homogenous system (Oracle to Oracle, SQL Server to SQL Server) then we can migrate them with relatively low or near zero downtime using the native HA & DR features. But when it comes to heterogenous systems, we need downtime and duration depends on our planning strategy.

For larger databases, they used AzCopy, R3Load (import/export data in a platform-independent format), and Migmon (Migration Monitor) tools. At on-premises provisioned 4 physical servers for running R3Load with accelerated networking. Server -1 for loading the top 5 tables, servers 2 & 3 for loading tables with table splits, and server 4 for migrating remaining all tables. Since R3Load exports in parallel, controlling the sequence of Import happens via the Signal File (SGN) that is automatically generated when all Export packages are completed. Here I am speaking in a very generic and simple language, but implementation is highly challenging and needs to consider a lot of facts:

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Alison: That’s very informative, thanks for taking me into the SAP world. I liked that cloning thought.

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Alison: I think it’s already late. Do you have 30 more min? We’ll quickly wrap it up.

Vasumat: Absolutely no issue.

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Alison: Wonderful! We are currently holding stakeholder financial portfolios, customer personal identities, and other sensitive information which is classified as confidential and restrictive. When I say moving to the cloud, the first thing that comes to my mind was data security. We are going to store our corporate data in a public cloud data center like Azure or AWS. Since you are the data owner, you need to convince me about the cloud migration by explaining the public cloud security capabilities. Considering I have zero knowledge about cloud security, can you list out all possible security risks and how Cloud providers can handle them?

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In the final part, we will see how Vasumat deals with Cloud Data Security.

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The post A discussion between a CxO and a senior Data Architect Part 4 appeared first on Data Meets AI.

Links to other parts

A discussion between a CxO and a senior Data Architect Part 1

A discussion between a CxO and a senior Data Architect Part 2

A discussion between a CxO and a senior Data Architect Part 4

A discussion between a CxO and a senior Data Architect Part 5

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Background: We have been going through a discussion that took place between senior leadership and a data architect. They took 45 min of lunch break and here Part – 3 continues.

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Discussion Follows:

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Alison: Hope you enjoyed your food.

Vasumat: Yup, more than that, I liked that elegant garden, especially the spectacular mountain view from the corner.

Alison: Hmm, yes, that’s a favorite coffee spot for everyone on the floor.

Alison: Coming to the business, now I would like to discuss your area of expertise. I believe you have extensive experience in migrating data platforms. Isn’t it?

Vasumat: True!

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Alison: Can you describe the steps involved in migrating database systems from on-premises to the cloud? Well, let me simplify my question, let’s suppose we have some heterogeneous database systems like Microsoft, Oracle, etc. I would like to know how you handle the database migrations. Since we already discussed phases, you can explain the series of steps involved. Appreciate it if you can explain it with whiteboarding. Take your time and explain as much detail as possible.

Vasumat: Sure! Assuming we already have an approved business case and talent is assigned.

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Migration Planning:

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Pre-Migration Activity:

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Execute Migration Project:

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Post-Migration Activity:

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This is the overall migration process that I experienced and successfully migrated 4 major enterprise database workloads to Azure and 2 workloads to AWS.

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Alison: I understand your proficiency. But does “successfully migrated” mean the complete process went on without any issues?

Vasumat: Well, not exactly. It is almost impossible to execute a migration without dealing with errors or issues. What I meant to say was, we made the data migration activity completed on time by maintaining the data quality. While migrating we faced issues, applied fixes, learned from them, noted the resolutions, predicted, and prevented the issues for the next set of workloads.

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Alison: Great. Can you describe the most common challenges we may face during the migration? Perhaps talk about the top 2 or 3 issues.

Vasumat: We can prevent 90-95% of problems with a proper migration plan and execution. However, the most common challenges are:

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Alison: That’s a huge list. Have you ever experienced dealing with all those failure cases?

Vasumat: Not exactly. As I mentioned before, I prefer spending quality time in the analysis and planning phase so that we can prevent the majority of the issues. Fixing compatibility issues and the issues that occurred during POC and non-prod workload migrations will help us to anticipate and mitigate the risk in production workloads.

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Alison: So, Vasumat, it sounds like data migration requires a lot of attention and effort. Do we have any shortcuts? or can we make it simple?

Vasumat: Let me put it this way “Data migration is an extremely high-risk and most critical activity in any migration”. I’ll add some points to prove my statement is correct. A famous global survey says that 83% of data migrations exceed budgets and timelines or fail altogether. And most of the failures are because of bad planning. My experience has taught me the same.

Alison: What was the survey that you were talking about?

Vasumat: Gartner survey. It’s a global research and advisory firm

Alison: Yes, I know, it’s a leader in global research.

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Alison: Can you tell me the most complex migration that you have done till today?

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In the next part, we will see how Vasumat explains about a business case where he faced a lot of challenges in migrating the workloads to cloud.

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The post A discussion between a CxO and a senior Data Architect Part 3 appeared first on Data Meets AI.