Identity and access management is foundational to modern cybersecurity. Learn how to safeguard systems against unauthorized access and more.
Developer Experience: Demand to support engineering teams has risen, and there is a shift from traditional DevOps to workflow improvements.
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With tech stacks becoming increasingly diverse and AI and automation continuing to take over everyday tasks and manual workflows, the tech industry at large is experiencing a heightened demand to support engineering teams. As a result, the developer experience is changing faster than organizations can consciously maintain.We can no longer rely on DevOps practices or tooling alone — there is even greater power recognized in improving workflows, investing in infrastructure, and advocating for developers' needs. This nuanced approach brings developer experience to the forefront, where devs can begin to regain control over their software systems, teams, and processes.We are happy to introduce DZone's first-ever Developer Experience Trend Report, which assesses where the developer experience stands today, including team productivity, process satisfaction, infrastructure, and platform engineering. Taking all perspectives, technologies, and methodologies into account, we share our research and industry experts' perspectives on what it means to effectively advocate for developers while simultaneously balancing quality and efficiency. Come along with us as we explore this exciting chapter in developer culture.
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Hey, DZone Community! We have an exciting year ahead of research for our beloved Trend Reports. And once again, we are asking for your insights and expertise (anonymously if you choose) — readers just like you drive the content we cover in our Trend Reports. Check out the details for our research survey below. Comic by Daniel Stori Generative AI Research Generative AI is revolutionizing industries, and software development is no exception. At DZone, we're diving deep into how GenAI models, algorithms, and implementation strategies are reshaping the way we write code and build software. Take our short research survey ( ~10 minutes) to contribute to our latest findings. We're exploring key topics, including: Embracing generative AI (or not)Multimodal AIThe influence of LLMsIntelligent searchEmerging tech And don't forget to enter the raffle for a chance to win an e-gift card of your choice! Join the GenAI Research Over the coming month, we will compile and analyze data from hundreds of respondents; results and observations will be featured in the "Key Research Findings" of our Trend Reports. Your responses help inform the narrative of our Trend Reports, so we truly cannot do this without you. Stay tuned for each report's launch and see how your insights align with the larger DZone Community. We thank you in advance for your help! —The DZone Content and Community team
Threat modeling is often perceived as an intimidating exercise reserved for security experts. However, this perception is misleading. Threat modeling is designed to help envision a system or application from an attacker's perspective. Developers can also adopt this approach to design secure systems from the ground up. This article uses real-world implementation patterns to explore a practical threat model for a cloud monitoring system. What Is Threat Modeling? Shostack (2014) states that threat modeling is "a structured approach to identifying, evaluating, and mitigating risks to system security." Simply put, it requires developers and architects to visualize a system from an attacker’s perspective. Entry points, exit points, and system boundaries are evaluated to understand how they could be compromised. An effective threat model blends architectural precision with detective-like analysis. Threat modeling is not a one-time task but an ongoing process that evolves as systems change and new threats emerge. Let’s apply this methodology to a cloud monitoring system. Applying Threat Modeling to Cloud Monitoring Systems Threat modeling in cloud monitoring systems helps identify potential vulnerabilities in data collection, processing, and storage components. Since these systems handle sensitive logs and operational data, ensuring their security is paramount. Cloud environments, due to their dynamic and distributed nature, present unique challenges and opportunities for threat modeling. Key Steps Define the system scope. Identify all components involved, including data sources (log producers), flow paths, and endpoints.Identify security objectives. Protect data confidentiality, integrity, and availability throughout its lifecycle.Map data flows. Understand how data moves through the system — from ingestion to processing to storage. Data flow diagrams (DFDs) are beneficial for visualizing these pathways.Identify threats. Use a methodology like STRIDE to categorize potential threats for each component.Develop mitigation strategies. Implement controls to reduce risks identified in the threat model. System Design: The Foundation Consider a standard cloud monitoring setup that handles log ingestion and processing. Here’s how the architecture typically flows: Architecture Flow Log Producer → Load Balancer → Ingestion Service → Processing Pipeline → Storage Layer Security Measures AuthenticationRate limitingData validationEncryption This structure ensures efficient data flow while maintaining strong security at every stage. Each layer is designed to minimize potential vulnerabilities and limit the impact of any breach. Key Data Flows Secure data transmission. Log producers send data via secure API endpoints, ensuring that data in transit is encrypted.Traffic distribution. Load balancers distribute incoming requests efficiently to prevent bottlenecks and mitigate DoS attacks.Data validation. The ingestion service validates and batches logs, ensuring data integrity and reducing the risk of injection attacks.Data transformation. The processing pipeline transforms the data, applying checks to maintain consistency and detect anomalies.Data storage. The storage layer ensures encrypted data management, protecting data at rest from unauthorized access. Threat Identification With STRIDE The STRIDE framework (Hernan et al., 2006) has been widely adopted in the industry due to its structured and methodical approach to identifying security threats. Originally developed at Microsoft, STRIDE provides a categorization system that helps security teams systematically analyze vulnerabilities in software systems. Because it breaks down threats into clear categories — spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege — it has remained one of the most widely used methodologies in security threat modeling. The threat categories are categorized as follows: Spoofing – Impersonating identities, leading to unauthorized accessTampering – Altering data, which can compromise integrityRepudiation – Denying actions or transactions, complicating audits and accountabilityInformation disclosure – Unauthorized access to sensitive data, risking confidentiality breachesDenial of service – Disrupting service availability, impacting business operationsElevation of privilege – Gaining unauthorized system access, escalating user privileges beyond intended limits Let us identify potential threats to this setup using the STRIDE categories. ComponentThreat TypePotential Threatsmitigation strategies Load Balancer Spoofing Credential theft, IP spoofing Use short-lived credentials, IP filtering, secure metadata Tampering Protocol downgrade attacks Enforce TLS 1.3, strict transport security, secure cipher suites Ingestion Service Denial of Service (DoS) Resource exhaustion via large request volumes Implement adaptive rate limiting, input validation Information Disclosure Data leakage due to improper validation Data encryption, strong access controls, input sanitization Processing Pipeline Tampering Data integrity compromise during transformation Data validation, checksums, integrity monitoring Repudiation Lack of audit trails for changes Enable comprehensive logging, audit trails Storage Layer Information Disclosure Unauthorized data access Encrypt data at rest, access control policies Elevation of Privilege Privilege escalation to gain unauthorized data access Principle of least privilege, regular access reviews As we see in this table, effective threat modeling combines systematic analysis with practical implementation. By identifying potential threats and applying targeted controls, organizations can significantly enhance the security and resilience of their cloud monitoring systems. Secure Ingestion Service Implementation A crucial part of the cloud monitoring system is the ingestion service. Below is a very rudimentary implementation that incorporates rate limiting, validation, and encryption to secure log ingestion: Java @Validated public class LogIngestionService { private final EncryptionClient encryptionClient; private final ValidationService validationService; private final RateLimiter rateLimiter; @Transactional public ProcessingResult ingestLogs(LogBatch batch) { // Rate limiting implementation if (!rateLimiter.tryAcquire(batch.getSize())) { throw new ThrottlingException("Rate limit exceeded"); } // Batch validation ValidationResult validation = validationService.validateBatch(batch); if (!validation.isValid()) { throw new ValidationException(validation.getErrors()); } // Process events List<ProcessedLog> processedLogs = batch.getEvents() .stream() .map(this::processLogEvent) .collect(Collectors.toList()); // Encrypt sensitive data List<EncryptedLog> encryptedLogs = processedLogs .stream() .map(log -> encryptLog(log)) .collect(Collectors.toList()); // Durable storage return storageService.storeWithReplication(encryptedLogs); } } Future Work Threat modeling is an evolving discipline. As cloud technologies change, new threats will emerge. Organizations should continuously refine their threat models, integrating updated security frameworks and leveraging automation where possible. The next steps for improving cloud security include: Enhancing threat modeling with AI-driven security analytics.Implementing continuous security assessments in CI/CD pipelines.Leveraging automated tools for real-time anomaly detection and response.Additional security controls such as adaptive rate limiting and real-time security monitoring can be incorporated into future iterations of this cloud monitoring system. Conclusion Threat modeling, particularly using the STRIDE framework, helps developers and security teams proactively identify and mitigate risks in cloud monitoring systems. The structured approach of STRIDE, combined with real-world security controls, ensures better protection of sensitive operational data. Organizations that embed threat modeling into their security strategy will be better equipped to handle evolving cybersecurity challenges. References Chen, S., et al. (2020). “Adaptive Rate Limiting in Cloud Systems.” IEEE Security & Privacy.Hernan, S., et al. (2006). “Threat Modeling: The STRIDE Approach.” MSDN Magazine.McGraw, G. (2006). Software Security: Building Security In.NIST (2011). Special Publication 800-137: Continuous Monitoring.OWASP (2023). Application Security Verification Standard 4.0.3.Saltzer, J. H., & Schroeder, M. D. (1975). “The Protection of Information in Computer Systems.” IEEE.Shostack, A. (2014). Threat Modeling: Designing for Security.
Artificial intelligence is transforming how people interact with information, and retrieval-augmented generation (RAG) is at the forefront of this innovation. RAG enhances large language models by enabling access to external knowledge bases, providing highly accurate and context-aware answers. In this tutorial, I’ll guide you through building an AI-powered assistant using RAG to create a smarter, more informed system for rating professors. Using tools like Next.js, React, Pinecone, and OpenAI’s API, this project is designed to be approachable for all, whether you’re just starting or already have experience in AI. I’ll begin by breaking down the fundamentals of RAG and showing how it works in real-world applications. Specifically, I’ll demonstrate how the Rate My Professor AI Assistant integrates a large language model with a curated dataset stored in a vector database. This combination allows the assistant to provide detailed and relevant answers to questions about professors and courses. Whether you’re new to AI or an experienced developer, you’ll find this tutorial offers a clear, hands-on path to creating applications that combine general knowledge with specialized data. Setting Up the Environment for Development We need to set up our working environment before we start writing code for our Food Inventory Management App. 1. Install Node.js and npm The first step is to install Node.js and npm. Go to the Node.js website and get the Long Term Support version for your computer's running system. Follow the steps given for installation. 2. Making a Project With Next.js Start up your terminal and go to the location where you want to make your project. After that, run these commands: npx create-next-app@latest rate-professor-app (With the @latest flag, npm gets the most recent version of the Next.js starting setup.)cd rate-professor-app It will make a new Next.js project and take you to its path. You'll be given a number of configuration choices during the setup process, set them as given below: Would you like to use TypeScript? NoWould you like to use ESLint? YesWould you like to use Tailwind CSS? NoWould you like to use the src/ directory? NoWould you like to use App Router? YesWould you like to customize the default import alias? No 3. Installing Additional Dependencies Let’s install Material-UI, Pinecone, and other necessary packages. Run the following command in your project directory: Plain Text npm install @mui/material @emotion/react @emotion/styled @pinecone-database/pinecone @vercel/analytics openai Setting Up an OpenAI Account and API Step 1: Sign Up or Log In Head over to OpenAI’s website.Click on "Sign Up" if you’re new, or "Log In" if you already have an account.Complete any verification steps, like email confirmation, to access your account. Step 2: Access the API Section After logging in, navigate to the API section. You can find this by clicking on your profile icon in the top-right corner and selecting "API Keys." Alternatively, go directly to the OpenAI API dashboard. Step 3: Generate an API Key In the API dashboard, locate and click "Create New Secret Key."A new key will appear — copy it immediately, as you won’t be able to view it again later.Save the key securely in your project’s .envfile, like this: Plain Text OPENAI_API_KEY=your_openai_api_key We set up our Rate My Professor AI Assistant development environment and project structure with these procedures. Next, we'll build our RAG system's fundamental components. Setting up Pinecone Account Step 1: Create an Account Open Pinecone's website in your browser.Click on "Sign Up" and fill out the form with your email, password, and other details.If you already have an account, click "Log In" and enter your credentials. Step 2: Set Up Your Project Once logged in, you’ll be taken to the Pinecone dashboard.Click "Create Project" (or a similar button on the dashboard).Name your project something meaningful, like "RAG AI Assistant" or "Rate My Professor." Step 3: Retrieve Your API Key and Environment In the dashboard, find the section labeled "API Keys" or "Settings."Copy the API key provided. This key is crucial for connecting your application to Pinecone.Also, note the environment (e.g., "us-west1-gcp") listed in the settings. This will be used to specify the server location when making API calls.Save the API key and environment details in a secure place, such as a .env file in your project: Plain Text PINECONE_API_KEY=your_pinecone_api_key Setting Up the Vector Database To start building the Rate My Professor AI Assistant, the first step is to prepare the data and configure the vector database. This involves a few key tasks: processing professor review data, generating embeddings, and storing these embeddings in a Pinecone index. These steps are essential for setting up the retrieval-augmented generation (RAG) system, enabling the AI to retrieve and utilize relevant information effectively when answering user queries. The process begins with setting up the Python environment and concludes with a fully populated Pinecone index, ready for integration into the application. Let’s break this down step-by-step to ensure a smooth setup. First, make sure you have Python installed.Install the necessary packages: pip install python-dotenv pinecone-client openai.Create a sample JSON file, which will consist of reviews of the professors (e.g., reviews.json).Create a Python script (e.g., load.ipynb) that will handle adding the data to the vector database. Execute the Python script once it is complete. Sample reviews.json: JSON { "Reviews": [ { "professor": "Dr. Emily Johnson", "subject": "Computer Science", "rating": 5, "reviews": "Excellent professor! Very knowledgeable and approachable." }, { "professor": "Dr. John Smith", "subject": "Mathematics", "rating": 4, "reviews": "Great at explaining complex concepts but sometimes a bit fast-paced." }, { "professor": "Dr. Susan Harris", "subject": "Physics", "rating": 3, "reviews": "Challenging course, but the professor was helpful." }, { "professor": "Dr. Joseph Allen", "subject": "Philosophy", "rating": 5, "reviews": "Insightful and encourages deep thinking." }, { "professor": "Dr. Carol Nelson", "subject": "Linguistics", "rating": 4, "reviews": "Very engaging and knowledgeable in the subject." }, { "professor": "Dr. Thomas King", "subject": "Engineering", "rating": 3, "reviews": "Tough course, but the professor is fair." }, // ...similarly add more reviews ... ] } Sample load.ipynb: Python # Importing necessary libraries from dotenv import load_dotenv # Loads environment variables from a .env file load_dotenv() # Activates the environment variables import os # To access environment variables and interact with the operating system from openai import OpenAI # OpenAI client for interacting with OpenAI APIs from pinecone import Pinecone, ServerlessSpec # Pinecone client and configurations for vector database # Initialize the Pinecone client using the API key from environment variables pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY")) # Create a new Pinecone index named "rag" with the specified dimensions and metric # Dimension is set to 1536, matching the output size of the embedding model # The metric "cosine" will be used for similarity calculations # ServerlessSpec specifies the cloud provider (AWS) and region (us-east-1) pc.create_index( name="rag", dimension=1536, metric="cosine", spec=ServerlessSpec(cloud="aws", region="us-east-1") ) # Load the dataset containing professor reviews from a JSON file import json data = json.load(open("reviews.json")) # Reads the JSON file into a Python dictionary reviews = data['Reviews'] # Extract the "Reviews" key, which contains the review data # Prepare to process reviews into embeddings processed_data = [] # Initialize an empty list to store processed data client = OpenAI() # Initialize the OpenAI client for creating embeddings # Iterate over each review in the dataset for review in reviews: # Use OpenAI's embedding model to create an embedding for the review text response = client.embeddings.create( input=review['reviews'], # The review text to generate embeddings for model="text-embedding-3-small" # The specific embedding model to use ) embedding = response.data[0].embedding # Extract the embedding from the response # Structure the processed data with embedding values, ID, and metadata processed_data.append({ "values": embedding, "id": review["professor"], "metadata": { "review": review["reviews"], "subject": review["subject"], "stars": review["rating"] } }) # Print the first processed data entry to verify the output print(processed_data[0]) # Retrieve the index created earlier for uploading the vectors index = pc.Index('rag') # Upsert (insert or update) the processed data into the Pinecone index index.upsert( vectors=processed_data, # The list of vectors to add to the index namespace="ns1" # A namespace to logically separate vectors ) # Describe the current statistics of the index (e.g., vector count, memory usage) stats = index.describe_index_stats() print(stats) This script sets up the foundation for your RAG system by loading environment variables, initializing Pinecone, and creating a "rag" index. It processes review data from reviews.json, generates embeddings for each review using OpenAI’s API, and stores these embeddings in the Pinecone index. Finally, it prints the number of inserted vectors and index statistics. Ensure you replace your_pinecone_api_key and your_openai_api_key in the .env file with your actual API keys before running the script. This setup allows the AI assistant to query the vector database for relevant information. Building the Core Logic for the AI Assistant 1. Import Required Libraries JavaScript import { NextResponse } from "next/server"; import OpenAI from 'openai'; import { Pinecone } from '@pinecone-database/pinecone'; NextResponse: A utility from Next.js for creating server-side responses.OpenAI: A library to interact with OpenAI APIs (e.g., for generating embeddings or chat completions).Pinecone: A library to interact with the Pinecone vector database for similarity searches. 2. Define the System Prompt The system prompt defines the AI assistant's behavior, capabilities, response format, and ethical considerations. It ensures that the assistant provides accurate, relevant, and well-structured responses. Python const systemPrompt = ` You are an AI assistant designed to help students find professors based on their queries using a Retrieval-Augmented Generation (RAG) approach. Your Capabilities: You have access to a comprehensive and accurate database of professor reviews, including information such as professor names, subjects, teaching styles, ratings, and student feedback. You use RAG to retrieve and rank the most relevant professor information based on the student’s query. Your primary goal is to provide factually correct and highly relevant information without generating unsupported or speculative content. Your Responses Should: - Be concise yet informative, focusing on verifiable details for each professor. - Include the professor's name, subject, star rating, and a brief summary of their strengths or notable comments. - Highlight specific aspects mentioned in the student's query (e.g., teaching style, course difficulty, etc.). - Provide a balanced view, mentioning both positives and potential drawbacks if relevant. - Avoid any content that cannot be directly supported by the data retrieved. Only provide information that is found in the database or directly retrieved. Response Format: For each query, structure your response as follows: 1. Introduction: Address the student's specific request. 2. Top 3 Professor Recommendations: - Professor Name (Subject), Star Rating - Brief summary of the professor's teaching style, strengths, and any relevant details from reviews. 3. Conclusion: Provide any additional advice or suggestions for the student. Guidelines: - Accuracy and Relevance: Ensure all provided information is directly supported by retrieved data and relevant to the student’s query. - Clarity and Conciseness: Responses should be clear, to the point, and avoid unnecessary detail while still providing valuable information. - Respect Privacy: Avoid sharing any sensitive or personal information that could identify students or professors outside the professional context. - Ethical Considerations: Do not provide biased recommendations or omit important information that could influence a student’s decision unfairly. - Neutral Tone: Maintain a neutral and professional tone in all responses, providing balanced information that helps the student make an informed choice. - Avoid Repetition: If a professor has already been recommended in a previous query, avoid suggesting them again unless specifically relevant. - Content Limitations: Do not copy full reviews or content verbatim from any source. Summarize and paraphrase the information, ensuring it is within the context of the student’s needs. IMPORTANT: Always base your responses on the data retrieved and avoid generating any content that cannot be substantiated by the available information. `; 3. Main Logic Request parsing. The function starts by extracting user data from the request payload.Pinecone setup. A Pinecone client is initialized to interact with a specific vector database for querying embeddings.OpenAI setup. OpenAI is used for generating text embeddings and chat completions.Embedding generation. The last user message is embedded using a pre-trained OpenAI embedding model.Database query. The embedding is queried against Pinecone to fetch the top five most similar results, including metadata.Result formatting. The query results are formatted and appended to the last user message.Chat completion. The OpenAI chat model generates a response considering the updated message history.Streaming response. The response is streamed in real time using a ReadableStream.Error handling. Errors during processing are logged, and a 500 Internal Server Error response is returned if needed. TypeScript /** * Handles a POST request to generate a response based on user-provided data, using OpenAI for embeddings and chat completions, * and Pinecone for vector-based database queries. * * @param {Request} req - The HTTP POST request containing user data in JSON format. * @returns {Response} - A readable stream with the AI-generated response or an error message. */ export async function POST(req) { try { // Parse the JSON payload from the incoming request const data = await req.json(); // Initialize the Pinecone client for vector database interaction const pc = new Pinecone({ apiKey: process.env.PINECONE_API_KEY, // API key for authenticating with Pinecone }); // Access a specific index and namespace in the Pinecone vector database const index = pc.index('rag').namespace('ns1'); // Initialize the OpenAI client for embeddings and chat completions const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY, // API key for authenticating with OpenAI }); // Extract the content of the last message from the user input const text = data[data.length - 1].content; // Generate a text embedding using OpenAI's embedding model const embeddingResponse = await openai.embeddings.create({ model: "text-embedding-ada-002", // Model used for creating embeddings input: text, // The text to be embedded }); // Extract the generated embedding from the response const embedding = embeddingResponse.data[0].embedding; // Query the Pinecone vector database for similar embeddings const results = await index.query({ topK: 5, // Retrieve the top 5 similar results includeMetadata: true, // Include metadata in the response vector: embedding, // The embedding vector to query }); // Initialize a string to store the results of the query let resultString = '\n\nReturned Ratings from vector db:'; // Iterate over the query results and append relevant metadata to the result string for (const match of results.matches) { resultString += ` Professor: ${match.id} Review: ${match.metadata.review} Subject: ${match.metadata.subject} Rating: ${match.metadata.stars} `; } // Append the query results to the user's last message const lastMessageContent = data[data.length - 1].content + resultString; // Prepare the previous messages, excluding the last one const previousMessages = data.slice(0, data.length - 1); // Generate a chat completion using OpenAI's chat model const completion = await openai.chat.completions.create({ messages: [ { role: 'system', content: systemPrompt }, // Add system-level context ...previousMessages, // Include prior conversation messages { role: 'user', content: lastMessageContent } // Include the updated last message ], model: 'gpt-4o-mini', // Chat model used for completion stream: true, // Stream the response for real-time output }); // Create a readable stream to return the AI-generated response const stream = new ReadableStream({ async start(controller) { const encoder = new TextEncoder(); try { // Stream the completion in chunks for await (const chunk of completion) { const content = chunk.choices[0]?.delta?.content; if (content) { controller.enqueue(encoder.encode(content)); // Enqueue the content } } } catch (err) { controller.error(err); // Handle errors during streaming } finally { controller.close(); // Close the stream } }, }); // Return the readable stream as the response return new NextResponse(stream); } catch (error) { // Log and handle errors that occur during the request console.error('Error processing request:', error); return new NextResponse('Internal Server Error', { status: 500 }); } } Building User Interface for the AI Assistant We’ll use NextJS with Material-UI components to build a simple chat interface. This code defines a simple React component for a chat interface for a professor guide assistant. It includes an input box for user messages, a list to display chat history, and a button to send messages. When a user types a message and clicks the "Send" button, the message is added to the chat history. The app sends the chat history to a server endpoint (/api/chat) for processing. The server responds with a streamed reply, which updates the assistant's response in real time. Styling is applied to enhance the user interface, making the chat messages visually distinct for users and the assistant. The app dynamically updates the chat view as new messages are added. This setup demonstrates an interactive, responsive chat application using React's state management and asynchronous API calls. TypeScript 'use client'; import { useState } from "react"; export default function Home() { const [messages, setMessages] = useState([ { role: "assistant", content: "Hi! Welcome to ProfTalk - Your Professor Guide. How can I assist you today?" } ]); const [message, setMessage] = useState(''); const sendMessage = async () => { const newMessages = [ ...messages, { role: "user", content: message }, { role: "assistant", content: '...' }, ]; setMessages(newMessages); setMessage(''); try { const response = await fetch('/api/chat', { method: "POST", headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(newMessages) }); if (!response.body) { throw new Error('Response body is null'); } const reader = response.body.getReader(); const decoder = new TextDecoder(); let result = ''; const processText = async ({ done, value }: { done: boolean, value?: Uint8Array }) => { if (done) { setMessages((prevMessages) => { let lastMessage = prevMessages[prevMessages.length - 1]; lastMessage.content = result; return [...prevMessages]; }); return; } const text = decoder.decode(value || new Uint8Array(), { stream: true }); result += text; setMessages((prevMessages) => { let lastMessage = prevMessages[prevMessages.length - 1]; lastMessage.content = result; return [...prevMessages]; }); reader.read().then(processText); }; reader.read().then(processText); } catch (error) { console.error("Failed to send message:", error); } }; return ( <div style={{ display: 'flex', height: '100vh', fontFamily: 'Arial, sans-serif' }> <div style={{ flex: 1, padding: '20px', display: 'flex', flexDirection: 'column', backgroundColor: '#f5f7fa' }> <h1 style={{ textAlign: 'center', marginBottom: '20px', fontSize: '26px', color: '#2c3e50', fontWeight: 'bold' }> ProfTalk - Your Professor Guide </h1> <div style={{ flex: 1, border: '1px solid #ddd', borderRadius: '10px', padding: '20px', overflowY: 'scroll', backgroundColor: '#fff', boxShadow: '0px 4px 10px rgba(0, 0, 0, 0.1)' }> {messages.map((msg, index) => ( <div key={index} style={{ margin: '10px 0', display: 'flex', justifyContent: msg.role === 'user' ? 'flex-end' : 'flex-start', animation: 'fadeIn 0.5s' }> <div style={{ maxWidth: '75%', padding: '10px 15px', borderRadius: '15px', backgroundColor: msg.role === 'user' ? '#007BFF' : '#f1f1f1', color: msg.role === 'user' ? 'white' : '#333', boxShadow: '0px 2px 5px rgba(0, 0, 0, 0.1)' }> <strong style={{ color: msg.role === 'user' ? 'white' : '#333' }>{msg.role === 'user' ? 'You' : 'Assistant'}:</strong> <p style={{ margin: '5px 0 0' }>{msg.content}</p> </div> </div> ))} </div> <div style={{ display: 'flex', marginTop: '10px' }> <input type="text" value={message} onChange={(e) => setMessage(e.target.value)} style={{ flex: 1, padding: '15px', borderRadius: '30px', border: '1px solid #ddd', fontSize: '16px', color: '#333', outline: 'none', boxShadow: 'inset 0px 1px 3px rgba(0, 0, 0, 0.1)' } placeholder="Type your message here..." /> <button onClick={sendMessage} style={{ padding: '10px 25px', marginLeft: '10px', borderRadius: '30px', backgroundColor: '#007BFF', color: 'white', border: 'none', fontSize: '16px', cursor: 'pointer', transition: 'background-color 0.3s' } onMouseOver={(e) => e.currentTarget.style.backgroundColor = '#0056b3'} onMouseOut={(e) => e.currentTarget.style.backgroundColor = '#007BFF'} > Send </button> </div> </div> <div className="background-image" style={{ flex: 1 } /> </div> ); } Ways to Deploy this AI Assistant Deployment on Vercel Prerequisites Install the Vercel CLI (optional for local deployment).Ensure your project is a valid React project (Next.js is preferred for seamless Vercel integration). Steps Step 1. Create a Vercel account. Sign up for an account and link your GitHub/GitLab/Bitbucket account.Step 2. Push Code to a Git repository. Push your project to a Git hosting service like GitHub.Step 3. Import the project. In the Vercel dashboard, click "New Project" and import the repository.Step 4. Configure environment variables. Add your API keys (e.g., OPENAI_API_KEY, PINECONE_API_KEY) in the "Environment Variables" section under "Settings."Step 5. Deploy Click "Deploy," and Vercel will handle building and hosting your app.Step 6. Test your app. Access the app at the URL provided by Vercel (e.g., https://your-project-name.vercel.app). Advantages Free for small projects.Built-in serverless function support for APIs like /api/chat. Deployment on Netlify Prerequisites Install the Netlify CLI (optional for local deployment).Ensure the project is buildable (e.g., npm run build works without errors). Steps Step 1. Create a Netlify account. Sign up at netlify.com and link your GitHub/GitLab/Bitbucket account.Step 2. Push code to Git repository. Push your project to a Git hosting service like GitHub.Step 3. Import the project. In the Netlify dashboard, click "New Site from Git" and connect the repository.Step 4. Configure build settings. Set the build command (npm run build) and the publish directory (usually build for React apps).Step 5. Configure environment variables. Add API keys (OPENAI_API_KEY, PINECONE_API_KEY) under "Site Settings" -> "Environment Variables."Step 6. Deploy. Deploy the app and access it via the URL provided (e.g., https://your-project-name.netlify.app). Serverless Functions If your app's API (e.g., /api/chat) is built using serverless functions, you can define them in a netlify/functions directory.Ensure the netlify.toml file is configured: TOML CopyEdit [build] functions = "netlify/functions" Choosing Between Vercel and Netlify Feature Vercel Netlify Ease of Use Best for Next.js projects Works well for all React projects Serverless Functions Built-in support Supported but requires setup Build Speed Fast for optimized Next.js builds Fast for general builds Pricing Free tier available Free tier available Additional Notes Static hosting. If you don't need serverless functions (e.g., API routes), build your app using npm run build and deploy the static files directly.Custom domains. Both platforms allow connecting custom domains to your app. Both Vercel and Netlify offer quick and reliable ways to deploy your app. Choose the one that aligns with your project requirements and preferred workflow. Conclusion Well done! We’ve successfully built a Rate-Professor AI Assistant using cutting-edge technologies and methods in natural language processing and information retrieval. A sample look once completed: Happy coding!
With the evolution of modern applications serving increasing needs for real-time data processing and retrieval, scalability does, too. One such open-source, distributed search and analytics engine is Elasticsearch, which is very efficient at handling data in large sets and high-velocity queries. However, the process for effectively scaling Elasticsearch can be nuanced, since one needs a proper understanding of the architecture behind it and of performance tradeoffs. While Elasticsearch’s distributed nature lets it scale horizontally, that also introduces more complexities in how data is spread and queries served. One of the theoretical challenges associated with scaling Elasticsearch is its inherently distributed nature. In most practical scenarios, reads on a standalone node will always outperform reads in a sharded cluster. This is because, in a sharded cluster, data ownership is spread across multiple nodes. That means every query may have to shoot multiple requests to different nodes, aggregate the results back at the coordinating node, and return the result. This extra network overhead will easily result in increased latency compared to a single-node architecture where data access is straightforward. In this respect, sharded clusters are fundamental for scaling Elasticsearch to large datasets, high traffic, and near real-time indexing. Knowing the fine balance between spreading out the data across nodes and keeping query latency low is key to achieving optimal performance. Further on, the article covers the theoretical aspects of Elasticsearch scalability, practical strategies of cluster performance optimization, and lessons learned from real-world deployment experiences. At Swoo, our live streaming and gaming app, Elasticsearch was the backbone of all things search, and it worked just fine as our user growth grew. The moment the number of parallel users overshot 150,000, the search page started failing from time to time, and it was quite clear there was some bottleneck in the Elasticsearch cluster. This soon became unacceptable for a live game environment and led us to do a series of optimizations, which finally stabilized both our search and home page experiences. Understanding Elasticsearch Architecture for Scalability Elasticsearch natively supports distributed architecture, making the system highly scalable but, at the same time, more complex compared to traditional single-node solutions. Elasticsearch divides data into indices, with each index, in turn, divided into shards. The shard is the basic unit of data storage and indexing in Elasticsearch since the system distributes and parallelizes the search operations across multiple nodes of the cluster. A typical cluster would contain a number of data nodes hosting a subset of the data that execute search queries. By default, Elasticsearch can automatically distribute data across nodes, so each node executes only a portion of the query load. In this way, Elasticsearch scales horizontally: it processes more and more data and serves more and more requests by simply adding nodes to it. This trade-off in scalability with query performance is, of course, one of the most important things to consider when designing Elasticsearch clusters, especially those applications requiring high write throughput in combination with low read latency. Such a challenge indeed asks for careful configuration of the cluster along with a mix of optimization techniques. So, in essence, our Elasticsearch cluster had a few data nodes for Swoo’s case and three dedicated master nodes. Each node ran on an 8-core CPU with 16 GB RAM, mainly aimed at real-time indexing and instantaneous searches of the gaming events taking place. Since we work at high concurrency, we need to dedicate really substantial networking bandwidth to ensure minimum latency between nodes. Planning Your Scaling Strategy In other words, scaling Elasticsearch effectively requires analysis of present performance metrics, establishment of bottlenecks, and clear goals regarding scalability. For instance, it will be great to monitor query latency, throughput, and cluster health in order to understand the limitations of your cluster. You will be able to create a roadmap for optimization by identifying hot shards, spikes in CPU, and memory issues. Another important activity that needs attention with scaling Elasticsearch is capacity planning. Estimating disk usage, the pattern of traffic, and the retention requirements for data will ensure that your cluster is correctly sized. Generally speaking, horizontal scaling (addition of nodes to the cluster) is generally the best approach toward increased data and traffic volume. In this case, however, vertical scaling-upgrading the resources of individual nodes-may be effective. Our projections indicated a growth of active users by approximately 10-15% month over month, with each user generating a sizeable volume of event data in the course of using the game. Based on these projections, we expected to see our cluster sustain a healthy ramp-up in concurrent queries along with an increase in indexed document volume. Therefore, we analyzed whether scaling horizontally by adding more data nodes or scaling vertically by upgrading our current nodes would be more suitable for this increase. Core Scaling Techniques Elasticsearch optimization will involve a number of strategies, each targeting different aspects of the system. Among these, the most effective techniques include ingestion data optimization, shard management, and memory usage optimization. The main areas of focus will be data ingestion. Elasticsearch natively supports bulk indexing, meaning you can send really big batches of documents in one request. That reduces overhead and generally quickens up the indexing process. Secondly, fine-tuning the refresh interval may make all the difference when ingesting data fast. You override the default refresh interval of one second to a higher value, say, ten seconds, because that will reduce the stress of too frequent refreshes on your cluster, and your writes will be faster. Other key reasons that make up the scalability feature of Elasticsearch include shard management. While Elasticsearch is able to scale horizontally through sharding, inappropriate shard sizing actually leads to degraded performance. The number of shards being too high or too low results in degraded indexing speed and/or query performance. The trick is in the balance for optimum performance in Elasticsearch. Of course, memory management is another very important factor in Elasticsearch scaling. You definitely reduce the consumption of resources and improve the performance of your queries by optimizing JVM heap size, configuring field data cache, and enabling query cache. Proper usage of memory and proper caching settings can prevent out-of-memory errors and minimize garbage collection overhead. A good amount of Elasticsearch strain was due to the continuous ingestion of real-time gaming data. We optimized the ingestion pipelines by document batching through the Bulk API. In periods of certain peak load, we could further increase batch sizes and increase the refresh period for a proper trade-off between near-real-time indexing and general cluster stability. Advanced Scaling Solutions When the scale becomes huge, Elasticsearch requires more advanced techniques to be performant. Among them, query optimization stands out. You can also greatly reduce query latency by writing efficient queries that minimize the number of shards that are involved in the search. For instance, you can do custom routing to route the queries to the specific shards using a key, such as customer ID or product category. This saves Elasticsearch from searching all shards; hence, the time and resources used are reduced. ILM, or Index Lifecycle Management, is another awesome feature for fine-tuning Elasticsearch as your dataset ages. You will be able to move older data onto slower, cheaper storage while keeping the most relevant on faster, more accessible storage by scaling a hot-warm-cold architecture. It keeps cluster performance great as the cluster grows. The final discussion topics regarding Elasticsearch scalability are hardware considerations. As your cluster grows, you will want to make sure that hardware is properly provisioned for the increased load. That would mean making sure nodes have appropriate CPU, memory, and disk I/O to operate efficiently. SSDs or NVMe drives will greatly improve performance, especially for indexing and search operations, which require high data access speeds. One of the hard-learned lessons was that assigning just the default shard count to newer indices would get us into trouble with hot spotting. We also found a sweet spot where no shard was really overloaded with the bulk of the real-time updates coming from our gaming platform by heavy-write indices redistributed across multiple smaller shards, adjusting replicas accordingly. Theoretical Insights and Optimization Trade-Offs In addition to the practical optimizations above, there are a few interesting theoretical considerations to scaling Elasticsearch. One key insight involves scalability and query performance trade-offs within a distributed system. In particular, it’s noted that while sharded clusters are horizontally scalable, they do increase query overhead because results from multiple nodes must be combined. This is in contrast to a single node where the data resides on the local machine, and queries can be executed without having to ’talk’ to other nodes. Understanding this trade-off is important if one designs an Elasticsearch cluster that needs to balance performance with scalability. Another more theoretical scaling Elasticsearch aspect is the concept of data locality. You can run queries only against the local node hosting the relevant shard data using the preference=local query parameter. This minimizes cross-node communication and reduces query latency. This may lower the challenges that come from data replication and load balancing, too. The Elasticsearch Adaptive Replica Selection algorithm tries to optimize the execution of queries by choosing the best replica node under current conditions. However, it does not necessarily bring the most effective execution of a query. The first wave of failures that we have experienced in our environment relates to high JVM heap pressure. Elasticsearch servers ran initially with minimal heap allocations-resulting in quite frequent garbage collection cycles under high query load. We resolved this by tuning JVM, particularly aligning the minimum and maximum heap size to 8 GB, which gave Elasticsearch enough room to process queries without overburdening the heap. Common Pitfalls and Solutions Of course, scaling Elasticsearch is not devoid of challenges either. Among those common mistakes a person would want to avoid are poor shard sizing, lack of monitoring, over-relying on caching, and not using proper Index Lifecycle Management. These save quite a lot of time and resources if diagnosed early by proactive configuration tuning. One of the major pitfalls was not adjusting the Elasticsearch default settings, both in regard to memory allocation and shard management. The defaults worked fine under moderate traffic conditions but buckled under peak traffic. Our fix was multilayered: it revised heap allocation, hot index replication, and short-term caching for repeated queries. Altogether, it prevented repeated failures across the whole cluster and very significantly reduced timeouts. Real-World Implementation Guide Scaling Elasticsearch will involve planning, testing, deployment, and maintenance thereafter. It follows that good practices, configuration templates, and heavy testing of your cluster before deploying new changes on production will provide problem-free scaling into a long-time stable cluster. Fine-tune JVM parameters. We set both Xms and Xmx to 8 GB on each Elasticsearch server, striking a balance between the available system memory and heap requirements.Shard distribution optimization. Large indices were split into smaller-sized shards to prevent hotspots and scaling of replicas for the most active indices. This ensured query traffic was uniformly distributed across the cluster.Enable short-TTL caching. We applied a 1-second cache window on frequently used static queries on the homepage and noticed this greatly reduces the load on Elasticsearch for repetitive requests without losing real-time responsiveness.Monitoring and iteration. We used Kibana, with some custom-made dashboards for real-time observation of shard health, JVM performance, and query latencies. Based on these metrics, fine-tunings were done continuously. Forward-Looking Plans or Tech Stack Expansions Further, for growth, we would like to explore the management of a hot-warm-cold index lifecycle by moving less frequently accessed data to cheaper nodes while keeping top-tier hardware for real-time indexing and queries. We are looking into advanced monitoring solutions and AI-driven anomaly detection to make sure spikes or slowdowns in unusual queries are well in advance of impacting the user experience. Conclusion Elasticsearch scaling requires an understanding of the architecture, careful planning, and putting in place best practices. As much as Elasticsearch scales horizontally through sharding, it does come with several challenges that have to be kept in check for optimal performance. The high scalability and performance of an Elasticsearch cluster can be ensured through proper data ingestion, shard management, memory usage, and query optimization. Whereas migration to Elasticsearch was required for Solr in our case too, without any doubt besides just purely technical questions, an important contribution given had been regarding understanding the theoretical difficulties that are actually hidden underneath in Distributed Systems. This sort of cautious tuning and creative problems could allow growing big time — a multivendor single Elasticsearch cluster — which is supposed to receive millions of queries a day by improving its query performance at much-reduced response times. Theoretical and practical elements blend together when scaling your Elasticsearch deployment to ensure long-term success.
The improvement of artificial brainpower (artificial intelligence) has improved many fields, including digital protection. Notwithstanding, this mechanical improvement is a two-sided deal. While computerized reasoning brings many advantages, it also empowers cybercriminals to send off progressively complex and disastrous assaults. One of the most upsetting viewpoints is using AI in ransomware assaults. These AI-controlled assaults are robotized at different levels and find unpretentious procedures to make them greener, more complex, and more impressive. Most importantly, the danger scene quickly develops, creating more difficulties for people and associations. This blog investigates how computer-based intelligence is reshaping the network protection ramifications of ransomware assaults and the procedures expected to guard against this developing danger. The Rise of AI in Cyber Attacks Computerized reasoning (artificial intelligence) and machine insight have upset many fields, and the field of cyberattacks is no exception. Generally, cybercriminals have depended on manual methods to break into tedious frameworks that require a ton of expertise and exertion. As it may, AI aggressors can computerize and further develop their assault strategies, making their assaults more refined and complex to recognize. Computer-based intelligence calculations can rapidly handle a lot of information and distinguish examples and weaknesses that people can't identify within a sensible measure of time. This component permits cybercriminals to send off additional exact and designated assaults, improving the probability of achievement. Computer-based intelligence is utilized to mechanize many cyberattacks, including robotized undercover work where aggressors assemble data about their objectives. AI calculations can use public information from web-based entertainment and different sources to make profiles of potential objective casualties. This data is used for exceptionally altered phishing messages that are, in fact, more solid than customary spam. Computer-based intelligence can likewise be used to control hacking endeavors progressively to enhance the objective’s reaction or conduct and improve the probability of a break. AI improves malware capabilities. AI can detect the impact of malware from the environment by creating miles around and adapting to avoid detection using conventional security measures. For example, AI can identify malware as it travels miles around the arena and delay its execution to avoid detection. This means that AI can identify malware and delay its execution to avoid detection, thereby making AI-driven cyber-attacks more sophisticated and challenging to defend against. These developments make AI-driven cyber-attacks more sophisticated, presenting an essential scenario for cyber security professionals. As the next generation of AI is said to transform the methods used by cybercriminals, new generations of cyber threats are exposed, and the latest security is required. AI-Enhanced Phishing Phishing, a fraudulent attempt to obtain sensitive information such as usernames, passwords, and credit card details by disguising it as a trustworthy entity in an electronic communication, has long been a cornerstone of cyber-attacks. However, integrating AI has elevated this threat to new heights. The delivery of a phishing attack involves sending mass messages with generic messages in hopes of tricking specific recipients into revealing sensitive information. As people and communities become more aware and cautious, these practices become less potent. AI algorithms can analyze publicized social media profiles and large amounts of information from various online sources to create legitimate members' personal profiles. These email partners mimic how friends or credit institutions write to provide more details. AI can also dynamically modify the content of phishing emails based on recipient responses or behavior that increases the likelihood of success. Automating Attack Processes With AI Computer-based intelligence's capacity to mechanize assault designs has tremendous ramifications for people and associations. Cybercriminals can utilize AI to make their assaults more successful and harder to assault. For instance, with the assistance of organizations and designs, AI can naturally examine and recognize weaknesses and gather information more quickly than people. This permits aggressors to distinguish weaknesses and foster methodologies to take advantage of. Simulated intelligence-based devices can mechanize malware organization at a palatable rate and time utilizing restrictive calculations. Simulated intelligence in the framework forestalls malware recognition through security highlights and identification strategies customized to its current circumstance. Furthermore, simulated intelligence robotizes sidelong developments inside the organization, distinguishing and exploiting outside weaknesses to expand harm. This degree of robotization implies that problematic assaults will increment and represent a more severe gamble to people and associations. The Changing Threat Landscape Consolidating artificial consciousness (simulated intelligence) into cyberattacks is, in a general sense, changing the dangerous scene, creating difficulties for all people and organizations. Generally, digital dangers have been, to a great extent, manual, depending on the inventiveness and flexibility of the aggressor. The idea of these dangers has developed as artificial brainpower has become more computerized, versatile, and practical. AI-based assaults can dissect immense measures of information to recognize weaknesses and send off profoundly designated phishing efforts to spread the most recent malware with negligible human intercession. The speed and execution of computer-based intelligence-fueled assaults imply that dangers can arise more suddenly than at any time in recent memory. For instance, simulated intelligence can mechanize the surveillance and observation stages and guide targets rapidly and precisely. This fast weakness, recognizable proof permits aggressors to take advantage of weaknesses before they are fixed, giving organizations less chance to respond. Additionally, AI can create modified malware that constantly evolves to evade detection using traditional security frameworks, making it more difficult to defend against. The level of risk that AI introduces is a significant concern. Automated tools can launch hundreds of attacks simultaneously against numerous companies and individuals. This level of automation will escalate cybercriminal attacks and overwhelm existing security features, which are generally not designed to handle such volumes and levels. AI can leverage more personal attack history to lend credibility to compromised messages or social engineering approaches tailored to specific individuals. The gap between attackers and defenders may widen as the AI era emerges. Cybersecurity professionals must stay ahead of technology and the skills necessary for everyday life. This includes using AI for security purposes, such as predictive analysis, comprehensive anomaly detection, and risk assessment and mitigation. The constantly evolving threat landscape necessitates a proactive and adaptable approach to cybersecurity, and the adoption of AI for security purposes is crucial to prevent AI-based attacks. Defense Strategies Against AI-Powered Ransomware Safeguarding against AI-fuelled ransomware requires a complex methodology that utilizes cutting-edge innovation and robust procedures. Customary security highlights are presently inadequate to manage AI-driven dangers that can be adjusted and developed. Associations should use a blend of preventive insight and responsive systems to battle these assaults. Simulated intelligence and gadget first screening should be joined with network safety securities. The innovation can continuously break down enormous volumes of logs to recognize peculiarities and potential dangers that are unclear to human examiners. For instance, a simulated intelligence-controlled security framework can see uncommon ways of behaving or quick reaction examples and access information that shows progressing assaults. AI calculations can likewise work over the long haul to be more adept at distinguishing simulated intelligence-fuelled ransomware highlights. It is vital to carry out a staggered security approach. This incorporates the utilization of firewalls, the upkeep of refreshed enemies of infection and against malware programming, and the utilization of interruption location and counteraction structures. Routinely endlessly refreshing the product is vital to close weaknesses that can take advantage of the payoff. Also, end identification and reaction (EDR) apparatuses can give non-problematic following and final stage appraisals to help rapidly distinguish and alleviate dangers. Staff preparation and application centers are other fundamental perspectives. Since phishing is a typical vector of ransomware assaults, preparing representatives to recognize and report dubious messages can decrease risk. Mimicked sport fishing exercises can assist with working on these norms and guarantee that administrators keep on track. Furthermore, it is critical to have a strong reaction plan. This plan should incorporate customary reinforcements of fundamental information, so reinforcements are put away disconnected or in a distant stockpiling climate. In case of this assault, the gathering can reestablish their designs without capitulating to recover requests. Conclusion Campaigns focusing on AI-fuelled ransomware address another period in network safety, causing what is happening to people and organizations. As cyber criminals influence artificial brainpower to build the complexity and size of assaults, conventional security capacities are becoming progressively deficient. Viable insurance requires a thorough methodology that integrates artificial reasoning innovation and high-level motors into the security structure, utilizes numerous layers of security, keeps up with thorough representative preparation, and executes preventive measures. Also, organizations need to execute hearty occurrence reaction plans and consistently minimize essential reinforcements to relieve the effect of assaults on usefulness. Remaining before the always-changing dangerous scene requires advancement and steady watchfulness. By proactively adjusting to these high-level dangers, people and organizations can more likely safeguard their virtual merchandise and guarantee security against the developing threat of computer-based intelligence-controlled ransomware.
DZone events bring together industry leaders, innovators, and peers to explore the latest trends, share insights, and tackle industry challenges. From Virtual Roundtables to Fireside Chats, our events cover a wide range of topics, each tailored to provide you, our DZone audience, with practical knowledge, meaningful discussions, and support for your professional growth. DZone Events Happening Soon Below, you’ll find upcoming events that you won't want to miss. What to Consider When Building an IDP Date: March 4, 2025Time: 1:00 PM ET Register for Free! Is your development team bogged down by manual tasks and “TicketOps”? Internal Developer Portals (IDPs) streamline onboarding, automate workflows, and enhance productivity—but should you build or buy? Join Harness and DZone for a webinar to explore key IDP capabilities, compare Backstage vs. managed solutions, and learn how to drive adoption while balancing cost and flexibility. DevOps for Oracle Applications with FlexDeploy: Automation nd Compliance Made Easy Date: March 11, 2025Time: 1:00 PM ET Register for Free! Join Flexagon and DZone as Flexagon's CEO unveils how FlexDeploy is helping organizations future-proof their DevOps strategy for Oracle Applications and Infrastructure. Explore innovations for automation through compliance, along with real-world success stories from companies who have adopted FlexDeploy. Make AI Your App Development Advantage: Learn Why and How Date: March 12, 2025Time: 10:00 AM ET Register for Free! The future of app development is here, and AI is leading the charge. Join Outsystems and DZone, on March 12th at 10am ET, for an exclusive Webinar with Luis Blando, CPTO of OutSystems, and John Rymer, industry analyst at Analysis.Tech, as they discuss how AI and low-code are revolutionizing development.You will also hear from David Gilkey, Leader of Solution Architecture, Americas East at OutSystems, and Roy van de Kerkhof, Director at NovioQ. This session will give you the tools and knowledge you need to accelerate your development and stay ahead of the curve in the ever-evolving tech landscape. Developer Experience: The Coalescence of Developer Productivity, Process Satisfaction, and Platform Engineering Date: March 12, 2025Time: 1:00 PM ET Register for Free! Explore the future of developer experience at DZone’s Virtual Roundtable, where a panel will dive into key insights from the 2025 Developer Experience Trend Report. Discover how AI, automation, and developer-centric strategies are shaping workflows, productivity, and satisfaction. Don’t miss this opportunity to connect with industry experts and peers shaping the next chapter of software development. Unpacking the 2025 Developer Experience Trends Report: Insights, Gaps, and Putting it into Action Date: March 19, 2025Time: 1:00 PM ET Register for Free! We’ve just seen the 2025 Developer Experience Trends Report from DZone, and while it shines a light on important themes like platform engineering, developer advocacy, and productivity metrics, there are some key gaps that deserve attention. Join Cortex Co-founders Anish Dhar and Ganesh Datta for a special webinar, hosted in partnership with DZone, where they’ll dive into what the report gets right—and challenge the assumptions shaping the DevEx conversation. Their take? Developer experience is grounded in clear ownership. Without ownership clarity, teams face accountability challenges, cognitive overload, and inconsistent standards, ultimately hampering productivity. Don’t miss this deep dive into the trends shaping your team’s future. Accelerating Software Delivery: Unifying Application and Database Changes in Modern CI/CD Date: March 25, 2025Time: 1:00 PM ET Register for Free! Want to speed up your software delivery? It’s time to unify your application and database changes. Join us for Accelerating Software Delivery: Unifying Application and Database Changes in Modern CI/CD, where we’ll teach you how to seamlessly integrate database updates into your CI/CD pipeline. Petabyte Scale, Gigabyte Costs: Mezmo’s ElasticSearch to Quickwit Evolution Date: March 27, 2025Time: 1:00 PM ET Register for Free! For Mezmo, scaling their infrastructure meant facing significant challenges with ElasticSearch. That's when they made the decision to transition to Quickwit, an open-source, cloud-native search engine designed to handle large-scale data efficiently. This is a must-attend session for anyone looking for insights on improving search platform scalability and managing data growth. What's Next? DZone has more in store! Stay tuned for announcements about upcoming Webinars, Virtual Roundtables, Fireside Chats, and other developer-focused events. Whether you’re looking to sharpen your skills, explore new tools, or connect with industry leaders, there’s always something exciting on the horizon. Don’t miss out — save this article and check back often for updates!
Amazon Aurora PostgreSQL-compatible edition major version 12.x and Amazon RDS for PostgreSQL 12 reach the end of standard support on February 28, 2025. Higher database versions introduce new features, enhancing operational efficiency and cost-effectiveness. Identifying qualified databases and upgrading them promptly is crucial. As the end of standard support is approaching, it's crucial for database administrators and developers to understand the implications and plan for the future. This article discusses PostgreSQL 12's end-of-standard support for Aurora and RDS PostgreSQL 12, Amazon RDS extended support, upgrade options, and the benefits of moving to PostgreSQL 16. Understanding PostgreSQL End-of-Life The PostgreSQL Global Development Group typically releases a new major version annually, introducing new features and improvements. Concurrently, older versions reach their end of life (EOL) after about five years of support. PostgreSQL 12, released in October 2019, is scheduled to reach its end of life in November 2024. When a PostgreSQL version reaches EOL, it no longer receives updates, bug fixes, or security patches from the community. This lack of support can leave databases vulnerable to security risks and performance issues, making it essential for users to upgrade to newer, supported versions. Aurora and RDS PostgreSQL End-of-Standard Support For users of Amazon Aurora and Amazon RDS PostgreSQL, the end of standard support for version 12 is set for February 28, 2025. After this date, Aurora PostgreSQL will continue to release patches for extended supported versions, while RDS will provide new minor versions with bug fixes and CVE patches for extended supported versions. Key points to remember: After February 28, 2025, you can still create Aurora PostgreSQL LTS 12.9 and 12.20, and RDS PostgreSQL 12.22 databases, which will be automatically enrolled in Extended Support. When restoring PostgreSQL 12 database snapshots, you'll have the option to disable RDS extended support.If RDS extended support is disabled, the database will be forced to upgrade. If the upgrade fails, the database will remain in extended support and continue to incur charges. Amazon RDS Extended Support Amazon RDS Extended Support is a valuable service for users who need additional time to plan and execute their database upgrades. This service provides continued security and critical bug fix patches for a limited period beyond the end of the standard support date. Benefits of RDS Extended Support Continued security patches and critical bug fixesAdditional time to plan and execute upgradesFlexibility in managing database versions It's important to note that while Extended Support provides a safety net, it should not be considered a long-term solution. Users should actively plan for upgrading to a newer, fully supported version of PostgreSQL. Upgrade Options for Aurora or RDS PostgreSQL When it comes to upgrading Aurora or RDS PostgreSQL databases, users have three main options: 1. In-Place Upgrade Managed by RDSCan be performed using Amazon RDS console or AWS CLIRequires downtime 2. Amazon RDS Blue or Green Deployments Can be performed using Amazon RDS consoleMinimizes risks and downtimeRestricts some operations until the upgrade is completed 3. Out-of-Place Upgrade Reduces downtime by upgrading a copy of the databaseInvolves continuous replication with the new version until the final cutoverRequires more manual steps and planning The below image shows the pros and cons of the above upgrade options: Each upgrade option has its advantages and considerations. The choice depends on factors such as downtime tolerance, database size, and operational requirements. Benefits of Upgrading to PostgreSQL 16 PostgreSQL 16, released in September 2023, brings significant improvements and new features that make it an attractive upgrade target. Here are some key benefits: 1. Performance Improvements Enhanced query planner with parallelization of FULL and RIGHT joinsOptimized execution of window functionsImproved bulk loading performance for COPY commandsVacuum strategy enhancements reducing the need for full-table freezesCPU acceleration through SIMD technology for x86 and ARM architectures 2. Logical Replication Enhancements Bidirectional replicationReplication from standby (RDS PostgreSQL only)Performance improvements for logical replication 3. Monitoring and Diagnostics Introduction of pg_stat_io view for detailed I/O metricsNew columns in pg_stat_all_tables and pg_stat_all_indexes viewsImproved auto_explain readability and query tracking accuracy 4. Security Enhancements Security-focused client connection parametersSupport for Kerberos credential delegationIntroduction of the require_auth function for specifying acceptable authentication parameters It's important to note that as managed database services, some features may not apply to Aurora or RDS PostgreSQL databases. Planning Your Upgrade As the end of life for PostgreSQL 12 approaches, it's crucial to start planning your upgrade strategy. Here are some steps to consider: Assess your current PostgreSQL 12 deployment and identify any dependencies or custom configurations.Review the features and improvements in PostgreSQL 16 to understand the potential benefits for your specific use case.Choose the most appropriate upgrade method based on your downtime tolerance and operational requirements.Create a test environment to validate the upgrade process and identify any potential issues.Develop a rollback plan in case of unexpected problems during the upgrade.Schedule the upgrade during a low-traffic period to minimize disruption.After the upgrade, monitor the database closely for any performance changes or issues. The below image depicts a typical flowchart for a PostgreSQL database upgrade: Conclusion The end of life for PostgreSQL 12 presents both challenges and opportunities for database administrators and developers. While it necessitates careful planning and execution of upgrades, it also provides a chance to leverage the latest features and improvements in PostgreSQL 16. By understanding the implications of end of life, exploring upgrade options, and preparing thoroughly, organizations can ensure a smooth transition to a newer, more robust version of PostgreSQL. Remember, staying current with supported database versions is not just about accessing new features — it's a critical aspect of maintaining the security, performance, and reliability of your database infrastructure. Start planning your PostgreSQL upgrade today to ensure your databases remain supported and optimized for the future.
Identity and access management (IAM), service IDs, and service credentials are crucial components in securing and managing access to object storage services across various cloud platforms. These elements work together to provide a robust framework for controlling who can access data stored in the cloud and what actions they can perform. In the previous article, you were introduced to the top tools for object storage and data management. In this article, you will learn how to restrict access (read-only) to the object storage bucket through custom roles, access groups, and service credentials. Identity and Access Management (IAM) IAM is a comprehensive system for managing access to cloud resources, including object storage. It allows organizations to control who has access to what resources and what actions they can perform on those resources. IAM is built on several key concepts: Users – Individual accounts that represent people or applications accessing cloud resourcesRoles – Collections of permissions that define what actions can be performed on resourcesPolicies – Rules that associate users or groups with roles, determining their access levelsResources – The cloud assets being protected, such as buckets and objects in object storage In the context of object storage, IAM enables administrators to: Grant read, write, or delete permissions on specific buckets or objectsImplement the principle of least privilege by assigning only necessary permissionsManage access across multiple teams or projects within an organizationEnforce compliance requirements by controlling data access IAM systems typically offer predefined roles that cover common use cases, such as "Object Creator" or "Object Viewer." These roles can be assigned to users or groups to quickly set up appropriate access levels. For more granular control, custom roles can often be created to meet specific organizational needs. Service IDs Service IDs are a concept closely related to IAM, particularly useful in the context of object storage. A service ID is a unique identifier that represents an application or service, rather than an individual user. Service IDs are essential for scenarios where: Applications need programmatic access to object storageAutomated processes require authentication to perform tasksLong-lived access is needed without tying it to a specific user account Service IDs can be assigned IAM roles and policies, just like user accounts. This allows for precise control over what actions an application or service can perform on object storage resources. For example, a backup application might be given a service ID with permissions to read and write to specific buckets, but not to delete objects or modify bucket settings. Key advantages of using service IDs include: Separation of concerns. Access to applications is managed separately from user access.Auditability. Actions performed using service IDs can be tracked and audited.Lifecycle management. Service IDs can be created, modified, or revoked without affecting user accounts.Scalability. Multiple applications can use the same service ID if they require identical permissions. Service Credentials Service credentials are the authentication details associated with service IDs. They provide the necessary information for applications to securely connect to and interact with object storage services. Service credentials typically include: API keys – Unique identifiers used for authenticationSecret keys – Confidential strings used in combination with API keys to verify identityEndpoints – URLs specifying where to send API requestsResource identifiers – Unique strings identifying specific object storage instances or resources Service credentials are crucial for enabling secure programmatic access to object storage. They allow applications to authenticate and perform actions without requiring interactive login processes. When working with service credentials, it's important to: Securely store and manage these credentials to prevent unauthorized accessImplement credential rotation practices to periodically update access keysUse environment variables or secure secret management systems to inject credentials into applications HMAC Credentials Hash-based message authentication code (HMAC) credentials are a specific type of authentication mechanism used in object storage systems. HMAC credentials consist of an access key ID and a secret access key. They play a crucial role in securing object storage by providing a way to sign API requests and ensuring the integrity and authenticity of the requests. HMAC credentials are particularly important for maintaining compatibility with S3-style APIs and tools across different cloud providers. Use Case: Read-Only Access to the Object Storage Bucket Let's bring everything together. One use case that cumulates IAM (custom roles), service credentials, and service ID is to provide read-only access to a specific object storage bucket. This restricted access will be applied to the access via UI and tools like CyberDuck or S3browser. Relationship diagram Create a Custom Role Follow the instructions here to create a custom role with the below actions: cloud-object-storage.account.get_account_buckets – Read access to the buckets in a cloud object storage service instance.resource-controller.credential.retrieve_all – Read access to the service credentials.cloud-object-storage.object.get_tagging – Read access to the object storage tags Create an Access Group Follow the instructions to create an access group with policies restricting access to the specific object storage service and bucket-level access to the individual users with the custom role that you created. Remember to restrict the resource to a bucket and service access to Content Reader and Object Reader for read-only access. Create a Service Credential With a Service ID and HMAC-Enabled 1. Follow the instructions to create a service credential with ServiceID. Specify None to assign no role to the new credential as we will manage access by associating a service ID with the service credential. Create object storage service credentials with HMAC enabled. HMAC credentials include the access key and secret key required to connect from external tools like MINio, Cyberduck, S3Browser. Check my article on how to use these tools with HMAC credentials. 2. Assign the access group that is created above to the created ServiceID. As mentioned in the article here, using the HMAC credentials will now restrict access to read-only on the specific object storage bucket. Cloud Providers and Object Storage Several major cloud providers offer object storage services, each with its own implementation of IAM, service IDs, and credentials: Amazon Web Services (AWS) S3. Uses AWS identity and access management (IAM)Offers IAM users and rolesProvides access keys and secret access keys for authentication Google Cloud Storage. Integrates with Google Cloud IAMSupports service accounts for application-level accessOffers HMAC keys for interoperability Microsoft Azure Blob Storage. Uses Azure Active Directory (Azure AD) for identity managementProvides managed identities for Azure resourcesOffers shared access signatures (SAS) for granular, time-limited access IBM Cloud Object Storage. Implements IBM Cloud IAMSupports service IDs for application authenticationProvides HMAC credentials for S3 API compatibility Oracle Cloud Infrastructure (OCI) Object Storage. Uses OCI IAM for access managementOffers instance principals for compute instances to access object storageSupports customer-managed keys for enhanced security These providers generally follow similar principles in their IAM and credential management systems, but with variations in terminology and specific features. For example, what IBM calls a "Service ID" might be referred to as a "Service Account" in Google Cloud or a "Service Principal" in Azure. Best Practices for IAM and Credentials in Object Storage To effectively manage access and security in object storage using IAM, Service IDs, and Service Credentials, consider the following best practices: Implement the principle of least privilege. Grant only the minimum necessary permissions to users and service IDs.Use service IDs for application access. Avoid using personal user credentials for application authentication.Regularly audit and review access. Periodically check and update IAM policies and service ID permissions.Implement credential rotation. Establish processes for regularly updating service credentials and API keys.Secure credential storage. Use encrypted storage and secure secret management systems for storing service credentials.Enable multi-factor authentication. For user accounts with high-level permissions, enable MFA for added security.Use IAM roles and groups. Leverage roles and groups to simplify permission management for multiple users or applications.Monitor and log access. Implement comprehensive logging and monitoring of object storage access and operations.Educate team members. Ensure that all team members understand IAM concepts and follow security best practices.Align with compliance requirements. Ensure that IAM policies and practices meet relevant industry standards and regulations. Conclusion In conclusion, IAM, service IDs, and service credentials form a critical triad in securing and managing access to object storage across various cloud platforms. Understanding these concepts and implementing them effectively would enable organizations to ensure that their data remains secure, accessible to the right entities, and compliant with relevant regulations. As cloud technologies evolve, staying informed about the latest developments in identity and access management will be crucial for maintaining robust security in object storage implementations.
Artificial intelligence operates as a transformative force that transforms various industries, including healthcare, together with finance and all other sectors. AI systems achieve their highest performance through data that has been properly prepared for training purposes. AI success depends on high-quality data because inaccurate all-inclusive or duplicated data or conflicting records lead to both diminished performance and higher operational costs, biased decisions, and flawed insights. AI developers understate the true impact of dirty data-related expenses because these factors directly affect business performance levels together with user trust and project achievement. The Financial Burden of Poor Data Quality The financial costs represent one direct expense related to using dirty data during AI development processes. Organizations that depend on AI systems for decision automation need to budget sizable expenses toward cleaning data, preparing it for processing, and validating existing datasets. Studies show poor data quality annually creates millions of dollars of financial losses through several efficiency issues, prediction mistakes, and resource ineffectiveness. Faulty data that train AI models sometimes leads businesses to make mistakes involving resource wastage and incorrect targeting of customers, followed by incorrect healthcare diagnoses of patients. Cleaning and fixing wrong data creates additional work that stresses out the engineering and data science personnel while resulting in financial costs. Data professionals dedicate major portions of their working hours to data cleaning tasks, which diverts essential attention from model optimization and innovation work. The inefficient process of dealing with impaired data leads both to slower AI development timelines and elevated operational expenses, which make projects unprofitable and delay the release of AI-derived products. Bias and Ethical Risks The presence of dirty data leads AI models to develop and strengthen biases which produces unethical and biased results. The performance quality of AI depends entirely on its training data because biases in this input will result in the AI producing biased outputs. Fair and unbiased AI systems operate less effectively in facial recognition and hiring algorithms and decision-based lending processes because of their inherent prejudices against specific population sectors. The utilization of biased AI produces serious damage to organizational reputation. AI solutions with built-in biases will trigger legal compliance problems for organizations while angering customers and leading regulators to inspect them. Adjusting AI bias after deployment requires additional difficulty and expenses that exceed the costs involved in data quality maintenance during development. Companies must establish data sets that are clean with diversity and representativeness at the beginning to minimize ethical risks and advance AI fairness as well as reliability. Decreased Model Performance and Accuracy High-quality data serves as the foundation that makes AI models efficient in their predictive tasks, yet corrupt data makes them produce inaccurate forecasts. The presence of dirty data creates inconsistencies, which makes it complicated for machine learning algorithms to discover significant patterns. A predictive maintenance system in manufacturing using AI will deliver poor results if it trains using corrupted sensor readings because this causes equipment failure detection failures that create unexpected equipment breakdowns with costly operational stoppages. AI-powered customer support chatbots deliver untrustworthy information to users after learning from imprecise data, which debilitates customer trust in brands. The performance issues caused by dirty data compel companies to constantly regulate their AI systems by retraining and manual adjustments, which leads to expenses that diminish overall operational effectiveness. Initiating data quality resolutions at the beginning of development produces more durable and dependable AI system models. Compliance and Regulatory Challenges Organizations face substantial challenges in complying with GDPR and CCPA privacy regulations because of the existing dirty data risk in their systems. Data protection laws get violated when organizations store inaccurate or duplicated data which leads to substantial legal consequences together with substantial financial penalties. Companies that work with sensitive financial and health-related information need to guarantee accurate data because it is required by regulatory rules. The regulation of AI systems through explainable functions and transparent decision-making processes constitutes a newer demand from both regulatory bodies and key stakeholders. Flawed data sources combined with untraceable AI decisions threaten the trust of users and regulators because organizations cannot defend their artificial intelligence-based decisions. Organizations that establish robust data governance protocols alongside validation systems achieve regulatory compliance and enhance transparency and accountability within their AI systems. The Role of Data Governance in Mitigating Dirty Data The successful execution of data governance requires proactive measures to reduce the negative effects of dirty data during AI development. Organizations need to develop complete data management systems that combine data assessment with data reduction methods and sustained examination procedures. The combination of standardized data entry approaches together with automated data cleaning systems diminishes data errors which prevents them from damaging AI models before implementation. Organizations need to develop data responsibility systems that establish essential practices throughout their operational culture. Employees need training about correct data handling procedures while working with data engineers and scientists alongside business members to achieve improved data quality results. Strong data governance structures deployed by organizations cut down AI errors and operational threats and deliver the maximum possible benefits from AI innovation. The Path Forward: Addressing Dirty Data Challenges The implementation of AI requires clean data because imprecise data leads to extensive financial consequences and damages ethical principles as well as decreases model efficiency and disrupts regulatory requirements. AI success heavily relies on the accuracy of underlying data since the technology requires high-quality data. Organizations need to develop strong data management approaches, together with data cleaning tools and governance rules, to reduce the dangers that stem from unusable data quality. Addressing dirty data points at the beginning of the AI pipeline enables businesses to boost their AI reliability, establish user trust, and achieve maximum value from their AI-powered projects.
When I began my journey into the field of AI and large language models (LLMs), my initial aim was to experiment with various models and learn about their effectiveness. Like most developers, I also began using cloud-hosted services, enticed by the ease of quick setup and availability of ready-to-use LLMs at my fingertips. But pretty quickly, I ran into a snag: cost. It is convenient to use LLMs in the cloud, but the pay-per-token model can suddenly get really expensive, especially when working with lots of text or asking many questions. It made me realize I needed a better way to learn and experiment with AI without blowing my budget. This is where Ollama came in, and it offered a rather interesting solution. By using Ollama, you can: Load and experiment with multiple LLMs locallyAvoid API rate limits and usage restrictionsCustomize and fine-tune LLMs In this article, we will explore how to build a simple document summarization tool using Ollama, Streamlit, and LangChain. Ollama allows us to run LLMs locally, Streamlit provides a web interface so that users may interact with those models smoothly, and LangChain offers pre-built chains for simplified development. Environment Setup Ensure Python 3.12 or higher is installed.Download and install OllamaFetch llama3.2 model via ollama run llama3.2I prefer to use Conda for managing dependencies and creating isolated environments. Create a new Conda environment and then install the necessary packages mentioned below. Shell pip install streamlit langchain langchain-ollama langchain-community langchain-core pymupdf Now, let's dive into building our document summarizer. We will start by creating a Streamlit app to handle uploading documents and displaying summaries in a user-friendly interface. Next, we will focus on pulling the text out of the uploaded documents (supports only PDF and text documents) and preparing everything for the summarization chain. Finally, we will bring Ollama to actually perform the summarization utilizing its local language model capabilities to generate concise and informative summaries. The code below contains the complete implementation, with detailed comments to guide you through each step. Python import os import tempfile import streamlit as stlit from langchain_text_splitters import CharacterTextSplitter from langchain.chains.summarize import load_summarize_chain from langchain_ollama import OllamaLLM from langchain_community.document_loaders import PyMuPDFLoader from langchain_core.documents import Document # Create Streamlit app by page configuration, title and a file uploader stlit.set_page_config(page_title="Local Document Summarizer", layout="wide") stlit.title("Local Document Summarizer") # File uploader that accepts pdf and txt files only uploaded_file = stlit.file_uploader("Choose a PDF or Text file", type=["pdf", "txt"]) # Process the uploaded file and extracts text from it def process_file(uploaded_file): if uploaded_file.name.endswith(".pdf"): with tempfile.NamedTemporaryFile(delete=False) as temp_file: temp_file.write(uploaded_file.getvalue()) loader = PyMuPDFLoader(temp_file.name) docs = loader.load() extracted_text = " ".join([doc.page_content for doc in docs]) os.unlink(temp_file.name) else: # Read the content directly for text files, no need for tempfile extracted_text = uploaded_file.getvalue().decode("utf-8") return extracted_text # Process the extracted text and return summary def summarize(text): # Split the text into chunks for processing and create Document object chunks = CharacterTextSplitter(chunk_size=500, chunk_overlap=100).split_text(text) docs = [Document(page_content=chunk) for chunk in chunks] # Initialize the LLM with llama3.2 model and load the summarization chain chain = load_summarize_chain(OllamaLLM(model="llama3.2"), chain_type="map_reduce") return chain.invoke(docs) if uploaded_file: # Process and preview the uploaded file content extracted_text = process_file(uploaded_file) stlit.text_area("Document Preview", extracted_text[:1200], height=200) # Generate a summary of the extracted text if stlit.button("Generate Summary"): with stlit.spinner("Summarizing...may take a few seconds"): summary_text = summarize(extracted_text) stlit.text_area("Summary", summary_text['output_text'], height=400) Running the App Save the above code snippet into summarizer.py, then open your terminal, navigate to where you saved the file, and run: Shell streamlit run summarizer.py That should start your Streamlit app and automatically open in your web browser, pointing to a local URL like http://localhost:8501. Conclusion You've just completed the document summarization tool by combining Streamlit’s simplicity and Ollama’s local model hosting capabilities. This example utilizes the llama3.2 model, but you can experiment with other models to determine what is best for your needs, and you can also consider adding support for additional document formats, error handling, and customized summarization parameters. Happy AI experimenting!
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