Forem: Sumit Grover

Amazon Q Developer: Your Complete Guide to AI-Powered Development

Sumit Grover — Tue, 15 Jul 2025 10:09:51 +0000

Picture this: it's 2 AM, your AWS Lambda function is throwing cryptic errors, Stack Overflow has no answers, and you're drowning in IAM policies. We've all been there, wishing we had a senior engineer who could explain these mysteries and help us write better code.

That wish isn't so far-fetched anymore. AI is rapidly transforming how we build software, automating repetitive tasks and letting us focus on what truly matters - solving meaningful problems and shipping faster. But here's the thing: most AI coding tools feel generic, struggling with AWS-specific contexts and cloud-native workflows.

That's where Amazon Q Developer comes in. Unlike other AI assistants that treat AWS as an afterthought, Q Developer was built from the ground up for developers working in the cloud ecosystem.

What Is Amazon Q Developer?

First introduced at re:Invent 2023, Amazon Q felt like a promising but unfinished product. Fast forward to re:Invent 2024, and everything changed. Amazon Q returned with major upgrades and production-ready polish that made it feel like a genuine game-changer.

Built on Amazon Bedrock and powered by 17 years of AWS expertise, Amazon Q comes in two flavors:

Amazon Q Developer: Your coding companion for software development
Amazon Q Business: Enterprise productivity tool for teams and business automation

For developers, Q Developer is where the magic happens. It's specifically designed to understand AWS contexts, CLI commands, and cloud-native workflows - something generic AI tools often struggle with.

What makes Q Developer different:

Code chat and intelligent assistance with AWS context
Inline code completions that understand your cloud architecture
Security vulnerability scanning with AWS best practices
Code upgrades and modernization suggestions
Real-time debugging and optimization help
AWS architecture and resource guidance
Available across IDEs, CLI, and GitHub workflows

It's not just another AI assistant - it's your cloud-savvy pair programmer that understands the intricacies of building on AWS.

Getting Started with Amazon Q in Your IDE

Ready to add an AI wingman to your development setup? The IDE integration is probably the most natural place to start since it plugs directly into your existing workflow.

Supported IDEs

Amazon Q works with the tools you're already using:

Visual Studio Code
JetBrains IDEs (IntelliJ IDEA, PyCharm, WebStorm, etc.)
Eclipse (Preview)
Visual Studio (Windows only)

Make sure your IDE is updated before installing the extension - you'll want the latest version for the best experience.

Installation Process

The setup is surprisingly straightforward:

Install the Plugin: Download the Amazon Q extension from your IDE's marketplace

Choose Your Authentication: Pick between Builder ID (free, no AWS account needed) or IAM Identity Center (for enterprise teams)

Complete Browser Authentication: A quick browser flow to verify your identity

Start Coding: Once authenticated, you'll see the Q icon in your sidebar

Authentication Options Explained

Builder ID (Recommended for Individual Developers): This is the fastest way to get started. You don't need an existing AWS account, and the setup takes less than two minutes. Perfect for personal projects or trying out Q Developer.

IAM Identity Center: Ideal for enterprise teams or if you're already using AWS organizational accounts. Sessions typically last 8 hours, but if you onboarded after April 2024, sessions can extend up to 90 days.

Important Note: Amazon Q doesn't support traditional IAM role-based authentication in IDEs. You'll need to use one of the two options above.

IDE-Specific Setup

Visual Studio Code: Look for the Q icon in the sidebar after installation. Click it, authenticate via browser, then start chatting from the side panel. The integration feels natural and doesn't interfere with your existing extensions.

JetBrains IDEs: Open the Amazon Q tab in the sidebar, complete browser authentication, then interact from the side or bottom panel. Works seamlessly across IntelliJ, PyCharm, and other JetBrains tools.

Eclipse: Click the Q icon at the top right, authenticate through your browser, then start chatting from the bottom panel. Since this is still in preview, expect some rough edges.

What to Expect After Setup

Once you're authenticated, Amazon Q becomes part of your development environment. You can:

Ask questions about your code in plain English
Get explanations for error messages
Request code improvements and refactoring suggestions
Get help with AWS service integration
Generate unit tests and documentation

Pricing and What You Get

Amazon Q Developer offers two tiers:

Free: 50 IDE interactions per day (perfect for getting started)
Pro: $19/month with unlimited access, priority features, and faster response times

The free tier is generous enough to give you a real feel for how Q Developer works. If you hit the daily limit, you can either upgrade or wait for the reset - no strings attached.

Know more about Q developer pricing here.

Your Code's Security

This is probably on your mind, and rightfully so. Here's what you need to know:

Pro Tier: Your code and queries are never used for model training or service improvement. Period.

Free Tier: Your data may be used to improve model quality, but you can opt out anytime.

Amazon Q stores your input and generated responses temporarily (up to 90 days) to support features like code transformations and context-aware suggestions. After that, everything is permanently deleted.

The security model is transparent and puts control in your hands, which is exactly what you'd expect from an enterprise-grade tool.

Working with Amazon Q in GitHub

Now here's where things get really interesting. While the IDE integration is great for day-to-day coding, Amazon Q Developer's GitHub integration (currently in preview) opens up entirely new possibilities for collaborative development.

I discovered this feature while working on a project that had some frustrating issues after publishing to GitHub. The converted markdown was missing headers, Medium-specific UI elements were cluttering the output, and Python comments weren't being extracted properly. Instead of going back to the CLI, I decided to try this new GitHub integration.

Setting Up GitHub Integration

Getting started is surprisingly simple and doesn't require any AWS credentials:

Install the Q Developer GitHub App: Navigate to the Amazon Q Developer GitHub application and add it to your repositories

Choose Your Scope: Select all repositories or pick specific ones

Start Working: That's it - no additional authentication needed

The setup is genuinely frictionless. You get limited monthly invocations for feature development and code reviews, with the option to increase limits by connecting your AWS account later if needed.

The Development Workflow That Changed My Mind

Here's how I used Amazon Q Developer to systematically fix my project issues:

Step 1: Document Problems as Issues I created detailed GitHub issues describing each problem. The key is being specific about what's broken and what you expect to happen.

Step 2: Apply the Magic Label I added the "Amazon Q development agent" label to trigger the AI. This is where the automation kicks in.

Step 3: Review AI-Generated Solutions The agent analyzed my issues and created pull requests with proposed fixes. I could review everything in the familiar "Files changed" tab.

Step 4: Iterate Using Standard GitHub Flow For each suggestion, I could comment, request modifications, or approve changes using GitHub's standard review process. It felt like collaborating with a skilled developer who understood my codebase.

Step 5: Merge and Close After a few rounds of feedback, I merged three pull requests that fixed all my original issues. The entire process felt natural and productive.

What I Loved About the GitHub Integration

Browser-Based Development: Everything happens in GitHub's interface. When I wasn't at my main development machine, I could still make meaningful progress using just a browser.

Context-Aware Intelligence: The AI understood my project structure and suggested changes that made sense for my specific use case, not generic solutions.

Familiar Workflow: Uses standard GitHub review processes, so there's zero learning curve. It feels like working with a skilled developer who happens to be an AI.

Lightning-Fast Iterations: When I requested changes, Amazon Q Developer responded quickly with updated solutions that addressed my feedback.

Zero Barriers to Entry: Works without AWS credentials, making it accessible to any developer regardless of their cloud provider.

Amazon Q Developer CLI?

Developers spend significant time in terminals, managing tools, running commands, and debugging issues. Remembering countless command parameters across different tools becomes challenging, especially as they evolve with new versions.

Amazon Q Developer CLI brings AI-powered capabilities directly to your terminal. This open-source tool (Apache 2.0 and MIT licensed) provides three core features:

Key Features

Smart Command Completion
IDE-style auto-completion for hundreds of popular CLI tools (git, docker, aws) directly in your terminal.

Natural Language to Command Translation
Convert your intentions into proper command syntax when you know what you want but can't recall specific parameters.

Conversational Terminal with Agentic Execution
Chat with your terminal using natural language, debug issues, explore codebases, and execute actions like generating code, editing files, and automating Git workflows with your permission.

The tool supports multiple languages beyond English, making it accessible globally.

Installation

Requirements

OS: macOS, Linux, or Windows (WSL)
Shell: zsh, bash, or fish
Terminal: Most standard terminals supported

Installation Types

Minimal: Core chat and autocomplete features
Full: Includes GUI configuration tools

Setup Process

macOS: Direct download or Homebrew installation (simplest)
Linux: Follow the Linux installation guide
Windows: Use the Windows/WSL installation guide

For macOS/Linux and Ubuntu - Follow this guide
For windows - Follow this guide

Authentication

After installation, authenticate using:

Builder ID (Free): Register at Builder ID page with your email - no AWS account needed
Identity Centre: For enterprise users

Getting Started

Command Completion

Enhanced suggestions appear as you type. Navigate with arrow keys or continue typing to refine options. Press ESC to dismiss.

Command Translation

Transform natural language into executable commands:

q translate "find all files containing 'amazon-q' excluding hidden directories"

Output:

Shell · find . -type f -name "*amazon-q*" -not -path "*/.*" -not -path "./.*"

❯ ⚡ Execute command
  📝 Edit command
  🔄 Regenerate answer
  ❓ Ask another question
  ❌ Cancel

Chat Mode

Start an interactive session:

q chat

Or with direct query:

q chat "What is the largest file in my directory?"

The system requests permission before executing commands:

Execute shell command
▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔
I will run the following shell command:
find . -type f -maxdepth 1 -exec du -h {} \; | sort -hr | head -n 10

Enter y to run this tool, otherwise continue chatting.

Context Management

Amazon Q CLI uses contextual information from your current directory for better responses.

View Current Context

> /context show

current profile: default

global:
    .amazonq/rules/**/*.md
    README.md
    AmazonQ.md

Improve Context

Create markdown files with coding preferences in a "spec" directory
Add project-specific context files
Use profiles for different working environments: /profile
Context files are stored in ~/.aws/amazonq/

Useful Commands

Chat Session Commands

/acceptall - Auto-approve all requests (use cautiously)
/profile - Manage profiles (Beta)
/context - Manage context files (Beta)
/help - Show available commands
/quit - Exit chat session

In-Session Execution

Execute commands without leaving chat using "!" prefix:

> ! echo "Q is awesome"

Permission System

Auto-requests permission for most operations
Read-only commands (ls, cat, echo, pwd, which, head, tail) don't require permission
Use /acceptall to auto-approve (supervise carefully)

Best Practices

Security

Always review commands before execution
Monitor outputs for unexpected behavior
Use /acceptall with supervision

Workflow Optimization

Combine completion with translation features
Use chat mode for complex debugging
Maintain organized context files
Create profiles for different project types

Conclusion

Amazon Q Developer CLI transforms command line interaction by integrating AI capabilities directly into your terminal. The combination of intelligent completion, natural language processing, and agentic execution creates a more intuitive and efficient development experience.

Key Takeaways

Amazon Q Developer isn't just following the AI trend - it's genuinely solving real problems that AWS developers face every day. The combination of AWS-specific knowledge, multiple integration points, and focus on practical developer workflows makes it feel like a natural extension of your development environment.

The tool works best when you approach it as a knowledgeable pair programmer rather than a magic code generator. Ask it questions, get explanations, iterate on solutions, and use it to learn better practices. The more you use it, the more it understands your coding patterns and preferences.

While it's still evolving (especially the GitHub integration), the foundation is solid and the direction is promising. If you're working with AWS regularly, Amazon Q Developer is worth adding to your toolkit.

Give it a try and see how it fits into your development workflow. You might find yourself wondering how you ever debugged those cryptic Lambda errors without it.

NL2DATA

Sumit Grover — Thu, 26 Jun 2025 17:10:01 +0000

Introduction

Have you ever wished you could just ask your data a question and instantly get the answer as a chart or a table?

That’s exactly what NL2DATA does.

NL2DATA is an AI-powered platform that turns plain English into SQL queries and live data visualizations. Whether you want to retrieve raw data or generate dashboards, you can just type a simple prompt and no coding needed. It connects directly to your PostgreSQL database, translates your request into optimized SQL, fetches the data, and presents it in the most relevant form, either as a table or a chart.

In this blog, I’ll walk you through what NL2DATA offers and how you can start using it right away.

What is NL2DATA?

NL2DATA - Natural Language to Data is built to make data analysis easier and faster. Instead of writing complex SQL queries or spending hours designing dashboards, you can now just type what you need in natural language.

Key features:-

Natural Language to SQL: Ask questions like “Show sales by month for 2023” and NL2DATA converts this to SQL automatically.
Claude AI Integration: Uses Claude to understand and interpret even complex user queries.
Secure Database Connection: Easily connect your PostgreSQL database using secure credentials.
Auto-Generated Charts: Automatically suggests the best chart type for your data such as bar, pie, line, number, and more to come later.
Interactive Dashboards: Drag, filter, and customize views in real-time.
Tabular Results View: See your query output in clean, sortable tables.

How to Access and Use NL2DATA?

1. Open the Platform

Click on the link to open the NL2DATA platform in your browser.

2. Sign Up or Log In

Use your email and password to create an account or log in.
Note: Third-party login like Google is not supported.

3. Explore the Dashboard

After login, you will be redirected to the main dashboard.
The left sidebar has four menu options:

Dashboard
Charts
Data Sources
Logout

4. Connect a Data Source

Go to the Data Sources page.
You will see a default test database that includes two sample tables:

netflix_shows
titanic_passengers

To add your own database:

Click on Add Data Source
A modal will appear — fill in required fields (host, port, databasename, username, password)
Click Test Connection
If the test is successful, you can save and connect the database

5. Select a Table

Go back to the Dashboard.
Click the Data Drawer icon on the top-right.
It will list all your connected databases and their tables.
Select a database, then choose any table from the list.

6. Query Your Data

You can retrieve data in two ways:

Prompt Mode: Write your request in plain English, Example: Show all Titanic passengers older than 60
SQL Mode: Write a valid SELECT query manually

Click Run Query to see the results in a table format.

7. Create a Chart

Go to the Charts section from the left sidebar.
You will see the Chart Dashboard, which displays all your saved charts.
To create a new chart, click on the Add Chart button.

8. Generate a New Chart

This will open the Chart Preview page.
Follow these steps to generate your chart:

Select the desired database from the dropdown
Choose the type of chart (Number, Line, Bar, Pie)
Write a prompt describing the chart you want to create
Click on Generate Chart

Once the chart is generated, preview it.
If you're satisfied with the result, click Save Chart.

The chart will now appear on your Chart Dashboard.

Final Thoughts

NL2DATA was built to simplify how we interact with data, no more writing long SQL queries or dealing with complex dashboard tools.

Whether you're an analyst, developer, or just someone curious about their data, NL2DATA helps you turn questions into insights instantly.

Start with the test database or connect your own and begin exploring your data like never before.

Try It Out Now

Live Demo: link

GitHub: github profile

Connect: Found a bug? Want to collaborate?
Message me on LinkedIn.

Step 1: GET-> SET-> AWS!

Sumit Grover — Wed, 15 Jan 2025 13:40:26 +0000

Introduction: My First Steps Into the Cloud

Hey curious reader! I wholeheartedly welcome you to my first-ever blog on this platform. This blog is about how I began my journey into AWS, starting from knowing nothing to gradually learning about it and surrounding myself with fellow AWS enthusiasts. It highlights how community learning, or learning in public, enhances our core knowledge of a subject.

Before the Cloud: What I Didn’t Know About AWS

During the initial days of my Bachelor's in Information Technology, my main goal was to explore as many technologies related to software development as I could.

One of them was AWS!

The only information I had about AWS at that point was that it stood for Amazon Web Services—nothing beyond that. But one event changed everything for me.

The Turning Point: Discovering the AWS Community

On a random day in April 2023, while scrolling through LinkedIn, I came across a post by AWS User Group Delhi NCR about their first-ever offline meetup post-COVID at Amazon's office in Gurugram.

I felt it was a great opportunity to learn about AWS and career opportunities in this field.

(Of course, one of my motives was to visit the Amazon office 😉)

So I quickly registered for the event along with my friend Vridhi Duggal and eagerly waited for the day to arrive.

The Community Event: A Game Changer

Finally, the day arrived—5 May 2023. My friend and I reached the AWS office, and the first thing that mesmerized us was the office itself. The modern structure, along with the tech-inspired surroundings and interiors, motivated me to learn AWS and sparked a dream of working in that same office someday.

The meetup began with a fun and engaging opening keynote, followed by interactive sessions on AWS services led by the hosts. The lineup of speakers included intellectual professionals such as AWS employees, AWS Heroes, AWS Community Builders, and leaders from the user group itself.

Honestly, everything discussed in those sessions was new to me, and I began to doubt whether I should continue exploring the cloud or move on to something else. Fast-forward to the lunch break, where I had the chance to interact with the hosts and speakers face-to-face.

One memorable interaction was with Mrs. Dipali Kulshreshta. I remember asking her, "Is Cloud Computing rocket science?" Her response gave me the motivation and spark to explore the cloud field in detail:

"Only rocket science is rocket science. The more you try and fail in this, the more you get to know what AWS Cloud really is." - Dipali Kulshreshta

After a couple of sessions post-lunch, the event concluded. At the end, I had the chance to interact with Mr. Rajat Arora. I asked him about resources to learn AWS and where I should start. He immediately provided me with links and the names of online resources to begin my journey.

Some moments from the inaugural meetup:

Starting the Journey: My First Steps in AWS

The very next day after the meetup, I signed up and successfully created my account on AWS. I went through all the resources provided by Mr. Rajat and also explored some YouTube videos.

My major focus was to learn the basics first and get a brief understanding of various AWS services, without diving too deep into the details.

After exploring, I concluded that one of the best ways to learn about AWS and its various services is by preparing for the AWS Certified Cloud Practitioner exam.

After a few months of preparation, I cracked the exam on my first attempt.

Closing Thoughts: Advice to Aspiring Cloud Learners

I hope you enjoyed reading this blog. If you are new to the tech world or want to transition into the cloud, I suggest you join nearby cloud communities and interact with fellow cloud enthusiasts and professionals. Remember, communication and networking are key. Always try to interact and stay curious during these interactions.

Follow their advice and learn from their experiences. Explore more about these technologies and create your own Cloud Story.

I would love to hear about your cloud journey in the comment section. I am also open to any feedback—your feedback will help me improve my blogging journey.

Don’t forget to show some love by liking and following if you enjoyed this post.

What’s Next:

In my next blog, I will share some resources and tips on how to prepare for and ace your AWS Certified Cloud Practitioner exam. Make sure to follow my blog page for future updates.

Happy learning!