Wikipedia dataset

The Wikipedia dataset is available with flexible refresh schedules: one-time, bi-annual, quarterly, monthly, weekly, or daily - with deeper discounts for higher-frequency subscriptions (up to 80% off on monthly plans). You can also choose between pre-collected data (instantly available, collected within the last days to months) or freshly collected data gathered on-demand at the time of your order. The freshness window can be defined before checkout.

Yes. You can filter the Wikipedia dataset to include only the records and data fields you need - by geography, timeframe, category, or any supported field - using Bright Data's AI-powered filter tool or the Filter Dataset API. You only pay for the records in your filtered snapshot, which can substantially reduce cost. Filters support operators like equals, includes, greater than, is null, and more, with up to 3 levels of nested logic.

The Wikipedia dataset is delivered in your choice of JSON, NDJSON, JSON Lines, CSV, XLSX, or Parquet, with optional .gz compression. Delivery destinations include: Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Snowflake, Google PubSub, SFTP, Webhook, or Email. You can also download directly via the Control Panel (up to 5 GB) or retrieve programmatically via the Snapshot Download API. For snapshots larger than 5 GB, download links are sent by email.

Yes. Every dataset in the Bright Data Marketplace - including the Wikipedia dataset - offers a downloadable data sample at no cost. You can preview the data schema, field definitions, and a representative set of records to evaluate quality, coverage, and relevance before committing to a purchase. Samples are available directly from the dataset page in the Bright Data Marketplace.

Yes. You can customize the Wikipedia dataset to include only the specific fields you need, hiding irrelevant columns to reduce cost and simplify integration. For more advanced needs - such as a proprietary data schema, enriched fields, or a source not currently in the marketplace - Bright Data's team can build a custom dataset tailored to your requirements. Contact the sales team to discuss your use case.

The Wikipedia dataset is built for seamless integration. You can pull data programmatically using the Marketplace Dataset API (with SDKs available for Python and JavaScript), push results directly to your data warehouse or cloud storage, or connect via native integrations with tools like Snowflake, AWS, Google Cloud, Databricks, and automation platforms like Zapier, Make.com, and n8n. The async workflow (trigger - poll - download) makes it easy to embed into any pipeline.

Yes. All Bright Data datasets - including Wikipedia - are collected exclusively from publicly available online sources in compliance with applicable laws and regulations, including GDPR, CCPA, and Bright Data's own Code of Ethics. Bright Data holds ISO 27001 certification and is SOC 2 compliant. Data undergoes rigorous quality assurance before delivery. You can review Bright Data's full compliance posture at the Trust Center.

Pricing for the Wikipedia dataset starts at $250 for 100K records (approximately $0.0025 per record) for a one-time purchase. Subscription plans unlock significant savings: up to 25% off bi-annual, 50% off quarterly, and 80% off monthly refresh plans. Volume tiers are available for 100K, 500K, 1M, 5M, and 20M+ records. Dataset bundles (purchasing two or more datasets together) and smart update options (paying only for new or changed records) provide additional savings. For enterprise-scale pricing, contact the Bright Data sales team.

When you order the Wikipedia dataset - or filter it via API - Bright Data generates a snapshot: a point-in-time export of your selected records. The async workflow runs as follows: (1) your order or API call triggers the collection job; (2) you can poll the job status using the snapshot ID; (3) once complete, you download the snapshot via the API or your configured delivery destination. Snapshot metadata (including error codes and initiation type) is accessible via the Snapshot Metadata API.

Yes. The Wikipedia dataset is structured and validated for immediate use in AI and ML workflows, including LLM fine-tuning, model training, RAG pipelines, and agent knowledge bases. Data is delivered in standard ML-ready formats (JSON, Parquet, NDJSON) with consistent schemas and documented field definitions accessible via the Dataset Metadata API. Bright Data also offers specialized AI data packages and a web archive with 50+ PB of historical data for large-scale pre-training.