Bright Data Datasets

Bright Data Datasets is a marketplace of pre-collected, validated datasets sourced from over 100 popular websites. Teams that need structured data for AI training, market research, or business intelligence can purchase or subscribe to a dataset and receive clean, structured records without building or maintaining any scraping infrastructure.

General
DeveloperBright Data
TypeReady-made Data Marketplace
Sources100+ popular websites and platforms
Documentationdocs.brightdata.com/datasets
Product Pagebrightdata.com/products/datasets

Core Features

  • 100+ platform datasets: pre-collected data from Amazon, LinkedIn, Instagram, TikTok, YouTube, Reddit, Glassdoor, and dozens of other sources.
  • Clean and validated records: data is structured, deduplicated, and validated before delivery, reducing processing overhead.
  • Multiple delivery formats: JSON, CSV, and other formats available depending on the dataset.
  • Scheduled refresh: subscribe to datasets that update on a set schedule (daily, weekly, or custom) to keep data current.
  • Instant download: purchase a snapshot and download immediately, with no wait for scraping to complete.
  • Custom datasets: request a custom dataset from a specific source if it is not already in the marketplace.

Common Dataset Categories

  • E-commerce product listings and pricing (Amazon, eBay, Shopify stores)
  • Social media profiles and posts (LinkedIn, Instagram, TikTok, Reddit)
  • Review and rating data (Glassdoor, Yelp, Trustpilot, Google Maps)
  • Real estate listings (Zillow, Realtor.com)
  • Job postings (LinkedIn, Indeed, Glassdoor)
  • Video and content metadata (YouTube, podcasts)

Tools and Resources

Ecosystem and Integrations

  • Datasets integrate with AI training pipelines, vector databases, and data warehouses via standard formats.
  • Available alongside Bright Data's scraping APIs for teams that need both pre-built and custom data collection.
  • The Python SDK and JavaScript SDK expose dataset access programmatically for automated ingestion.

Browse available datasets and preview schemas at brightdata.com/products/datasets.

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Bright Data Datasets top builders

Explore the top contributors showcasing the highest number of Bright Data Datasets app submissions within our community.

Bright Data Bright Data Datasets AI technology Hackathon projects

Discover innovative solutions crafted with Bright Data Datasets, developed by our community members during our engaging hackathons.

Agent ai contents generation

Agent ai contents generation

Agent AI Contents Generation is a multi-step AI agent that turns a short brief into ready-to-publish content. The user provides a topic, target audience, tone of voice and desired channels (blog, LinkedIn, X, newsletter). The agent then plans the content, gathers relevant context through web search, and generates drafts for each channel. The system is built as a pipeline of specialised agents: a Planner that breaks the brief into tasks, a Researcher that collects up-to-date facts, a Writer that produces the draft, and an Editor that reviews it for clarity, factual consistency, style and length limits. If the Editor finds issues, the draft goes back for revision until the quality criteria are met. Our goal is to save marketers, founders and creators hours of manual work while keeping a consistent brand voice. Users can adjust tone, length and format, regenerate individual sections, and export the results. In the future we plan to add image generation, post scheduling and analytics-driven optimisation of content.

HandSpeak

HandSpeak

SignBridge is a real-time AI-powered sign language interpretation system designed to make communication more accessible between sign language users and people who do not understand sign language. Using a camera, the system detects hand and body movements, analyzes gesture sequences, and converts recognized signs into natural-language text and speech. The project will leverage AMD GPU acceleration to improve real-time computer vision and AI inference performance, allowing the system to process video with low latency. Beyond individual signs, SignBridge aims to recognize sequences of gestures and reconstruct meaningful phrases, making communication more natural. The project combines accessibility, computer vision, AI, and high-performance AMD computing to create a practical assistive technology solution.

EdgeWatch: AI Fleet Telemetry

EdgeWatch: AI Fleet Telemetry

EdgeWatch is an AI-powered fleet monitoring system designed to detect IoT device problems before they become failures. The project simulates six ESP32 edge devices using Wokwi, with each device periodically reporting telemetry such as temperature, voltage, vibration, signal strength, and uptime. Two devices intentionally develop abnormal behavior: one experiences a gradual voltage drop while another overheats. A Python-based ingestion layer collects the telemetry, maintains a rolling history of recent readings for every device, and stores the data for analysis. Instead of sending individual requests for every device, EdgeWatch batches the fleet's recent telemetry into a single AI analysis request. An LLM analyzes the combined data, identifies anomalies, classifies device health, explains potential problems, and predicts possible failures. The resulting analysis is saved as structured snapshots that can power a live monitoring dashboard. The goal is to demonstrate a practical AI infrastructure pipeline connecting simulated edge hardware, telemetry ingestion, rolling-window processing, centralized AI reasoning, and actionable predictive maintenance. EdgeWatch shows how intelligent monitoring can scale beyond individual devices to entire fleets.

Fresno LUX: A Living City

Fresno LUX: A Living City

Fresno LUX is a prototype for a living intelligence layer across an entire city. Fresno already contains enormous amounts of intelligence: residents know where problems exist, nonprofits know where help is needed, businesses know where opportunities exist, educators understand gaps in learning, researchers generate knowledge, and government maintains critical infrastructure and data. The problem is that much of this intelligence is fragmented across disconnected systems. Fresno LUX connects those pieces. The platform creates a shared city graph where people, organizations, resources, needs, projects, observations, experiments, and outcomes become connected nodes. Instead of simply collecting data, LUX continuously asks: What is happening? What do we know? What is missing? What can we test? What changed? A community problem can become an observable signal. That signal can become an experiment. The experiment produces evidence. Evidence changes the system's understanding. That updated understanding informs the next action. Observe → Connect → Understand → Act → Measure → Learn → Converge. The goal is to move Fresno from disconnected information toward collective intelligence—a system where knowledge can travel across boundaries and successful solutions can be discovered, tested, measured, and replicated. LUX is designed to make the invisible connections visible: the relationship between a neighborhood and its resources, between a student and educational opportunity, between a business and workforce capacity, between environmental conditions and community health, and between an idea and measurable real-world impact. This hackathon prototype demonstrates the beginning of a Living Fresno Graph: a city that can observe itself, learn from evidence, and coordinate toward better outcomes.

Rehearsal - Research. Practice. Get Hired.

Rehearsal - Research. Practice. Get Hired.

The problem Candidates rehearse “tell me about yourself” and “greatest weakness,” then walk into a room where the conversation is about this posting, this stack, and what the company shipped last month. The gap between generic prep and a real interview is where people lose. Those specifics are public — the posting, the company page, the news — but almost nobody works through them systematically the night before. What we built Rehearsal is a three-tab workspace: Research · Rehearse · Relive. 1. Research — Paste a job link. Three live scrapes land as cards (job, company, news) with source domain and timestamp. A prep brief and optional resume-based fit match sit inside the same dossier. 2. Rehearse — Choose Voice or Text. An interviewer (named, role-framed) asks questions grounded in that dossier — e.g. Deltek Playwright/LLM-as-judge QA, or Dodo Payments developer onboarding — not recycled bank questions. Source labels show where each question came from. 3. Relive — Answers are scored with missed points and model answers so practice produces a verdict, not just a feeling. Why it is different Every question is meant to be traceable to scraped evidence. A generic tool asks about test pyramids; Rehearsal asks how you would build an LLM-as-judge pipeline because that sentence is in the posting, and it can show you the line. Design Institutional dossier aesthetic — paper and ink, mono for facts, signal red only when the mic is live. Not a chat UI. Not a SaaS dashboard. A file being assembled on a candidate. Scope One job at a time, night-before practice: research → rehearse → review. Not job search, not mass apply, not a career LMS. Demo / status End-to-end research and interview paths run against live services. Demo film uses product stills + music-led cards (screenshot cut under time). Live product: https://rehearsal.nryn.dev Made by Narayan SS · nryn.dev

Revnuvo Investigator

Revnuvo Investigator

Revnuvo Investigator is an AI-powered business intelligence and trust analysis platform that turns a company name or URL into a structured investigation report. Instead of forcing users to search across multiple websites and technical tools, the Investigator combines company discovery, web intelligence, website and DNS analysis, evidence gathering, trust scoring, risk assessment, and recommendation generation in one live workflow. The investigation streams progress through discovery, research, gathering, technical analysis, risk assessment, reasoning, and report generation. The final report presents company information, evidence, technical findings, trust score, risk indicators, sources, and actionable recommendations. Bright Data powers the web intelligence layer, enriching company research from the public web. The system is also designed with graceful fallbacks using DuckDuckGo, Wikipedia, direct website retrieval, and DNS/technical analysis when premium web intelligence is unavailable. The application is built with React, TypeScript, Vite, and Supabase Edge Functions, with SSE streaming for real-time investigation progress. Revnuvo Investigator is designed as a modular foundation for broader business verification, counterparty assessment, digital trust, and AI-agent intelligence infrastructure.