Aqarmap Scraper

Slug: fatihtahta/aqarmap-scraper

Overview

Aqarmap Scraper collects structured public property listing records from Aqarmap, including listing identity, location, pricing, property attributes, media, contact details when available, and related party information. Aqarmap is a major real-estate marketplace for Egypt, making its public listings useful for market discovery, inventory monitoring, competitive analysis, and operational reporting. The actor helps teams turn public listing pages into repeatable JSON records that are easier to review, compare, export, and sync into downstream systems. Users can scope runs by location, keyword, listing mode, property category, price, area, amenities, compound status, enrichment depth, and output limit. Results are delivered as structured dataset records suitable for review, export, analytics, ETL pipelines, AI-agent workflows, and downstream APIs. The actor is designed for dependable recurring data acquisition without requiring users to manually copy listing details from the website.

Who Should Use This Actor

• Market research and analytics teams: compare public supply, pricing, property categories, locations, amenities, and listing availability across repeatable market segments.
• Real-estate operators and investment teams: build structured monitoring workflows for selected geographies, property types, price bands, and inventory segments.
• Product, marketplace, and catalog teams: normalize public property listings into a consistent dataset for search, matching, quality review, and internal catalog enrichment.
• Developers and data engineering teams: feed predictable nested JSON records into warehouses, APIs, search indexes, vector stores, and enrichment pipelines.
• AI agents and workflow automations: collect a scoped property dataset, summarize changes, identify records for review, and route outputs to the next workflow step.
• Sales, lead generation, and CRM teams: review public listing contacts, agencies, developers, and property attributes when those fields are available in the output.
• Monitoring and operations teams: schedule recurring runs for reporting, alerts, QA review, and downstream sync checks.

Common Use Cases

• Market intelligence: monitor public property supply, pricing, property types, amenities, locations, and listing details for selected Aqarmap segments.
• Competitive monitoring: track public listing activity across categories, price bands, geographies, compounds, or broker-visible records.
• Lead review and enrichment: identify public listing records with contact details, agent names, agency names, developer names, media, and property attributes.
• Catalog and directory building: populate internal property datasets with structured public listing records and stable source identifiers.
• Data enrichment: add current public listing attributes to CRM, BI, research, or valuation datasets.
• Recurring reporting: schedule repeat runs for dashboards, alerts, inventory snapshots, and market movement analysis.
• Agentic research workflows: let an internal agent collect a scoped dataset, compare it with previous records, summarize notable changes, and queue records for human review.
• Map-based inspection: use saved listing coordinates when available to review geographic coverage and listing clusters.

Real-World Questions This Data Can Answer

• Which public Aqarmap listings match a specific location, property category, deal type, keyword, price band, area range, or amenity profile?
• Which property listings appear in a selected market segment today, and which fields are available for review or export?
• Which listings include useful public signals for outreach, enrichment, or operational review?
• Which properties are new, removed, or changed when repeated runs are compared by stable listing identifiers?
• How do prices, floor areas, property types, and visible amenities vary across selected locations or categories?
• Which records include media, contact details, coordinates, or related party information that can support downstream workflows?
• Which optional fields are populated often enough to power dashboards, alerts, or human review queues?

Quick Start

1. Choose a market scope. Use location for a targeted search, or leave it empty for broader discovery.
2. Select the listing mode, property category, language, and any filters that define the segment you want to collect.
3. Set a small limit for the first validation run, such as 10 or 25 records.
4. Run the actor in Apify Console.
5. Inspect the first dataset records to confirm the output shape and field coverage match your use case.
6. Increase the limit, adjust filters, export the dataset, or schedule the actor once the output is verified.

Input Parameters

Configure Aqarmap property listing collection by selecting a market scope, listing mode, property category, optional filters, enrichment depth, and result limit.

Parameter	Type	Description	Default
location	string	Optional location phrase for targeted market discovery, such as great cairo, new cairo, or sheikh zayed. Leave empty for a broad category search.	-
keyword	string	Optional free-text term used to narrow discovery to listings matching a phrase, feature, project, or local search intent.	-
language	string	Requested Aqarmap language. Allowed values: english, arabic.	english
deal_type	string	Listing mode. Allowed values: buy, rent. Use buy for sale listings and rent for rental listings.	buy
property_type	string	Property category. Allowed values: property-type, apartment, furnished-apartment, chalet, villa, land-or-farm, building, commercial, administrative, medical, land-or-commercial.	property-type
min_price	integer	Optional minimum listing price in EGP. Use it to remove listings below a target budget band.	-
max_price	integer	Optional maximum listing price in EGP. Use it to keep exports within a target affordability or investment range.	-
min_area	integer	Optional minimum floor area in square meters. Minimum accepted value is 10.	-
max_area	integer	Optional maximum floor area in square meters. Minimum accepted value is 10 and maximum accepted value is 5000.	-
amenities	array of strings	Optional amenities that listings should include. Allowed values: balcony, security, elevator, maid_room, water_meter, pets_allowed, landline_phone, swimming_pool, private_garden, kids_play_area, air_conditioning, covered_parking, electricity_meter, natural_gas_meter, kitchen_appliances.	[]
inside_compound	boolean	When enabled, focuses collection on listings inside compounds for compound-level monitoring or premium inventory review.	false
enrich_data	boolean	When enabled, collects richer listing details when available, such as descriptions, photos, features, contact values, and related parties. Disable for faster standard listing records.	true
limit	integer	Maximum number of records to save. Use a smaller value for validation and a larger value for scheduled exports or downstream ingestion.	-

Choosing Inputs

Use location when you need a targeted market dataset, such as a city, district, neighborhood, or compound-adjacent search area. Leave location empty when the goal is broad discovery across a property category or listing mode.

Use deal_type to separate sale and rental workflows. Keeping sale and rental runs separate usually makes pricing analysis, monitoring, and downstream comparison cleaner.

Use property_type to control the real-estate segment. The broad property-type value is useful for discovery, while specific categories such as apartment, villa, chalet, or commercial are better for operational reporting and category-level monitoring.

Use keyword for project names, features, neighborhood phrases, or other text-based intent. Keywords can make datasets more relevant, but they can also reduce coverage if the source has few matching records.

Use price, area, amenities, and compound filters when you already know the segment you want. Narrower filters produce more targeted datasets; broader filters improve discovery and help reveal the available market.

Use enrich_data when richer fields matter for review, CRM enrichment, map artifacts, media inspection, or AI summarization. Use a lower limit first when enrichment is enabled, then scale after validating the field coverage.

Use limit as a workflow control. Start small for QA, then increase after confirming that the selected scope returns useful records.

Input Recipes

• Validation run: choose one listing mode, one property category, optional location, and a low limit. Use this to inspect field coverage before scaling.
• Targeted market collection: provide a location, select deal_type, choose a specific property_type, and add price or area filters for a focused operational dataset.
• Broad discovery: leave location, keyword, and most filters empty, choose a property category, and set a conservative limit to review the available market.
• Compound-focused monitoring: enable inside_compound, choose the relevant property category, and repeat the same input on a schedule for comparable snapshots.
• Amenities-based lead review: select one or more amenities, enable enrich_data, and review records with contact details or related party fields when available.
• Segmented analysis: run separate jobs by geography, property type, deal type, or price band so each dataset maps cleanly to a downstream report or warehouse partition.

Example Inputs

Example: Broad villa discovery

{
  "language": "english",
  "deal_type": "buy",
  "property_type": "villa",
  "enrich_data": true,
  "limit": 10
}

Example: Targeted apartment search in Greater Cairo

{
  "location": "great cairo",
  "language": "english",
  "deal_type": "buy",
  "property_type": "apartment",
  "min_price": 200000,
  "max_price": 30000000,
  "limit": 25
}

Example: Rental monitoring with amenities

{
  "location": "new cairo",
  "language": "arabic",
  "deal_type": "rent",
  "property_type": "furnished-apartment",
  "amenities": ["balcony", "security"],
  "inside_compound": true,
  "limit": 50
}

Output

Output Destination

The actor writes results to an Apify dataset as JSON records. The dataset is designed for direct consumption by analytics tools, ETL pipelines, AI agents, and downstream APIs with minimal post-processing.

The current output shape is a property_listing record. If additional entity types are added in the future, each shape should be documented separately.

The actor also exposes an output link for the default dataset and, when available, an interactive listing map artifact built from saved records with valid coordinates.

Record Envelope And Stable Identifiers

Each saved record uses record_type to identify the normalized output family and record_id for the source listing identifier when available. The recommended idempotency key is record_id when present, with entity.url or source_context.canonical_url as a fallback.

For conservative warehouse or CRM upserts, use a composite key such as source_context.source_domain + record_id, or fall back to source_context.source_domain + entity.url when a listing ID is unavailable. Stable identifiers make records easier to merge, deduplicate, sync, and compare across repeated runs.

The source_context object carries provenance fields such as source name, domain, source URL, canonical URL, result position, country, and requested language. Use these fields for audit links, QA, and source-aware downstream routing.

Example: Listing Record

{
  "record_type": "property_listing",
  "record_id": "7045172",
  "source_context": {
    "source_id": "aqarmap_property_scraper",
    "source": "aqarmap",
    "source_domain": "aqarmap.com.eg",
    "source_url": "https://aqarmap.com.eg/en/listing/7045172-for-sale-cairo-new-cairo-example/",
    "canonical_url": "https://aqarmap.com.eg/en/listing/7045172-for-sale-cairo-new-cairo-example/",
    "position": 1,
    "country": "Egypt",
    "language": "english"
  },
  "entity": {
    "title": "Apartment 1 in New Cairo with balcony and security",
    "description": "Enriched listing description with payment plan, delivery timing, amenities, and neighborhood context.",
    "url": "https://aqarmap.com.eg/en/listing/7045172-for-sale-cairo-new-cairo-example/",
    "external_ids": {
      "listing_id": "7045172"
    },
    "status": "active"
  },
  "location": {
    "address": "Greater Cairo / New Cairo - 5th Settlement / New Narges",
    "city": "New Cairo",
    "region": "Greater Cairo",
    "neighborhood": "New Narges",
    "country": "Egypt",
    "latitude": 30.02,
    "longitude": 31.21
  },
  "pricing": {
    "price": 4750000,
    "price_text": "4750000",
    "currency": "EGP",
    "original_price": 4875000,
    "price_per_area": 43181.82,
    "fees": {
      "maintenance_text": "Included when available"
    }
  },
  "property": {
    "property_id": "7045172",
    "property_type": "apartment",
    "bedrooms": 2,
    "bathrooms": 1,
    "floor_area": 110,
    "area_unit": "sqm",
    "features": [
      "Balcony",
      "Security",
      "Elevator",
      "Private Garden"
    ],
    "floor": 1,
    "finishing": "Super Lux"
  },
  "listing": {
    "listing_id": "7045172",
    "deal_type": "buy",
    "enrichment_status": "enriched",
    "published_text": "Recently listed"
  },
  "media": {
    "main_image_url": "https://img-2.aqarmap.com.eg/new-aqarmap-media/search-thumb/7045172-main.jpg",
    "image_urls": [
      "https://img-2.aqarmap.com.eg/new-aqarmap-media/search-thumb/7045172-main.jpg",
      "https://img-2.aqarmap.com.eg/new-aqarmap-media/search-thumb/7045172-living.jpg",
      "https://img-2.aqarmap.com.eg/new-aqarmap-media/search-thumb/7045172-balcony.jpg"
    ]
  },
  "contact_details": {
    "phones": [
      "01000000000"
    ]
  },
  "relationships": {
    "agent": {
      "name": "Owner contact"
    },
    "agency": {
      "name": "Example Brokerage"
    },
    "developer": {
      "name": "Example Developer"
    }
  },
  "attributes": {
    "detail_attributes": {
      "Delivery": "Ready to move",
      "Payment Method": "Cash or installments",
      "View": "Garden view",
      "Compound": "Inside compound"
    },
    "raw_attributes": {
      "source_badge": "Featured"
    }
  }
}

Field Reference

Record Envelope

• record_type (string, required): Normalized record family. Aqarmap listing rows use property_listing.
• record_id (string, optional): Stable listing identifier when available. Use it for matching, deduplication, and upserts.

source_context

• source_context.source_id (string, optional): Source identifier that produced the record.
• source_context.source (string, optional): Short source name, usually aqarmap.
• source_context.source_domain (string, optional): Public source domain associated with the listing.
• source_context.source_url (string, optional): Source URL used for listing review or audit workflows.
• source_context.canonical_url (string, optional): Canonical listing URL when available.
• source_context.position (integer, optional): Result position assigned within the parsed listing set.
• source_context.country (string, optional): Country associated with the listing.
• source_context.language (string, optional): Requested or inferred page language, such as english or arabic.

entity

• entity.title (string, optional): Listing title or display name.
• entity.description (string, optional): Longer listing description when available.
• entity.url (string, optional): Public Aqarmap listing URL.
• entity.external_ids.listing_id (string, optional): Source listing ID.
• entity.status (string, optional): Listing status label when available.

location

• location.address (string, optional): Source-provided location text or area path.
• location.city (string, optional): City-level location when available.
• location.region (string, optional): Region or market area when available.
• location.neighborhood (string, optional): Neighborhood or local area when available.
• location.country (string, optional): Listing country.
• location.latitude (number, optional): Latitude when valid coordinates are available.
• location.longitude (number, optional): Longitude when valid coordinates are available.

pricing

• pricing.price (number, optional): Numeric listing price when parseable.
• pricing.price_text (string, optional): Source price text after cleanup.
• pricing.currency (string, optional): Currency code, commonly EGP.
• pricing.original_price (number, optional): Original price when the source exposes it.
• pricing.price_per_area (number, optional): Price divided by area when available.
• pricing.fees.maintenance_text (string, optional): Maintenance or fee text when available.

property

• property.property_id (string, optional): Property-level identifier when available.
• property.property_type (string, optional): Normalized property category.
• property.bedrooms (number, optional): Bedroom count.
• property.bathrooms (number, optional): Bathroom count.
• property.floor_area (number, optional): Property area value.
• property.area_unit (string, optional): Area unit, usually sqm.
• property.features (array of strings, optional): Feature or amenity labels.
• property.floor (number, optional): Floor number when available.
• property.finishing (string, optional): Finishing quality label when available.

listing

• listing.listing_id (string, optional): Aqarmap listing identifier.
• listing.deal_type (string, optional): Listing mode, such as buy or rent.
• listing.enrichment_status (string, optional): Indicates whether richer listing details were added.
• listing.published_text (string, optional): Source publication timing text when available.

media

• media.main_image_url (string, optional): Primary listing image URL.
• media.image_urls (array of strings, optional): Image URLs associated with the listing.

contact_details

• contact_details.phones (array of strings, optional): Public phone strings associated with the listing contact when available.

relationships

• relationships.agent.name (string, optional): Visible agent or contact name.
• relationships.agency.name (string, optional): Agency or brokerage name.
• relationships.developer.name (string, optional): Developer name when available.

attributes

• attributes.detail_attributes (object, optional): Source-specific key-value property facts such as delivery, payment method, view, or compound labels.
• attributes.raw_attributes (object, optional): Additional meaningful source attributes preserved for review or source-specific downstream use.

Data Model Notes

• Identity fields: use record_id as the primary matching key when present. Use entity.url or source_context.canonical_url as fallback identifiers.
• Source and provenance fields: use source_context.source_domain, source_context.source_url, and source_context.canonical_url to trace a record back to its public source.
• Business attributes: pricing, property, location, listing, relationships, and contact_details carry the main operational value for real-estate workflows.
• Nested objects: related values are grouped to reduce top-level field sprawl and make JSON-first ETL, review, and AI usage easier.
• Optional fields: detail-rich values such as coordinates, contact details, media, floor, finishing, related parties, and raw attributes depend on what each listing publicly provides.
• Repeated runs: compare records across runs with stable identifiers plus Apify run metadata, saved input configuration, and run date stored alongside your exports.

Data Quality, Guarantees, And Handling

• Structured records: results are normalized into predictable JSON objects for downstream use.
• Best-effort extraction: fields may vary by region, availability, account visibility, UI experiments, or source-side changes.
• Optional fields: null-check optional values in downstream code, dashboards, and agent workflows.
• Deduplication: use record_id when present, with entity.url or source_context.canonical_url as a fallback.
• Freshness: results reflect the publicly available data at run time.
• Repeated runs: use the recommended idempotency key when syncing data into warehouses, CRMs, search indexes, vector stores, or monitoring systems.
• Schema awareness: downstream systems should rely on documented fields and handle newly missing optional fields gracefully.

Tips For Best Results

• Start with a small limit to validate the output shape before scaling up.
• Use one location, property category, deal type, or price band per run when you need cleaner comparison.
• Leave optional filters empty when the goal is broad discovery.
• Add filters gradually to understand how each field changes coverage.
• Enable enrich_data when descriptions, media, contact values, and related party fields matter.
• Schedule recurring runs for monitoring workflows instead of relying on manual one-off exports.
• Use stable identifiers for deduplication when storing results over time.
• Keep a saved copy of the input configuration used for each recurring workflow.

How to Run on Apify

1. Open the Actor in Apify Console.
2. Configure the available input fields for the target scope.
3. Set limit to control the maximum number of output records.
4. Click Start and wait for the run to finish.
5. Open the dataset and inspect the first records.
6. Download results in JSON, CSV, Excel, or another supported format.

Agentic And API-First Usage

Aqarmap Scraper can be used as a structured data acquisition step inside larger automated workflows. Agents and workflow builders can select a scoped input, run the actor, read the dataset, validate records against the field reference, and route listings into enrichment, alerting, review, or storage systems.

Agent workflow pattern:

1. Generate or select a scoped input from the supported schema.
2. Run the actor manually, on a schedule, or through Apify platform automation.
3. Wait for completion and read the dataset records.
4. Validate records against the field reference.
5. Upsert records into the downstream system using the recommended idempotency key.
6. Trigger analysis, enrichment, alerts, or human review.

Practical notes for agentic use:

• Keep prompts and automations grounded in the documented input parameters.
• Start with small validation runs before allowing broad automated collection.
• Feed the Field Reference and a small output sample to downstream AI steps.
• Treat optional fields as nullable instead of asking agents to infer missing values.
• Store run ID, input configuration, and dataset export metadata outside the record when building audit trails.

Scheduling & Automation

Scheduling

Automated Data Collection

Schedule runs to keep market snapshots current for dashboards, alerts, review queues, and recurring downstream syncs. Use the same input configuration for comparable monitoring, or create separate schedules for different locations, property types, and price bands.

• Navigate to Schedules in Apify Console.
• Create a new schedule, such as daily, weekly, or a custom cron interval.
• Configure input parameters.
• Enable notifications for run completion.
• Add webhooks for automated processing.

Integration Options

• CRM enrichment: sync public listing identifiers, contact values, related parties, property attributes, and source links into lead or account records.
• BI dashboards: monitor pricing, property types, locations, availability snapshots, and optional field coverage over time.
• Warehouses and lakehouses: store normalized property listing records for historical analysis, segmentation, and change detection.
• Google Sheets or Airtable: review smaller validation runs, QA samples, and market snapshots with non-technical stakeholders.
• Webhooks: trigger ingestion, notifications, validation checks, or enrichment workflows after each completed run.
• Alerts and scheduled reporting: notify teams when new listings, changed prices, populated contact fields, or selected property segments appear.
• Search or vector indexes: index titles, descriptions, locations, attributes, and source links for discovery, retrieval, and AI-agent context.

Export Formats And Downstream Use

• JSON: for APIs, applications, AI agents, and data pipelines that preserve nested objects.
• CSV or Excel: for spreadsheet workflows, stakeholder review, lightweight analysis, and manual QA.
• API access: for automated ingestion into internal systems and workflow platforms.
• BI and warehouses: for reporting, dashboards, historical analysis, market segmentation, and monitoring.
• Search or vector indexes: for listing discovery, semantic search, retrieval workflows, and agent context.

Downstream Pipeline Guide

• Idempotency: use record_id for upserts when available, with entity.url or source_context.canonical_url as a fallback.
• Null handling: treat optional fields such as location.latitude, location.longitude, contact_details.phones, relationships.agency.name, and media.image_urls as nullable.
• Type handling: preserve numbers, booleans, arrays, and nested objects when exporting to JSON-first systems.
• Flattening: if exporting to CSV or Excel, flatten nested objects deliberately and keep the original JSON export for full fidelity.
• Partitioning: store run date, input segment, location, property category, deal type, or workflow name outside or alongside records for easier analysis.
• Change detection: compare repeated runs by stable key and selected business fields such as pricing.price, entity.status, listing.published_text, property.features, or media.image_urls.
• Quality checks: monitor record count, duplicate rate, required identifiers, source URL availability, coordinate fill rate, contact fill rate, and media fill rate.
• Human review: route records with missing critical fields, unusual price values, changed status, or newly populated contact details into a review queue when needed.
• Retention: decide how long to keep raw exports versus normalized warehouse tables based on your reporting, audit, and enrichment needs.

Performance

Estimated run times:

• Small runs (< 1,000 outputs): ~3-5 minutes.
• Medium runs (1,000-5,000 outputs): ~5-15 minutes.
• Large runs (5,000+ outputs): ~15-30 minutes.

Execution time varies based on filters, result volume, target availability, and how much information is returned per record. Highly filtered runs can finish faster, while broad discovery or detail-rich records may take longer.

Limitations

• Availability depends on what https://aqarmap.com.eg publicly exposes at run time.
• Some optional fields may be missing on sparse listings or in faster non-enriched runs.
• Very broad searches may take longer or require higher limits.
• Target-side changes can affect field availability, labels, or naming.
• Regional, account, visibility, or availability differences may change visible results.
• The actor provides structured public data, not business advice, legal advice, valuation advice, or guaranteed completeness.
• Contact details are included only when they are publicly available in the collected listing context.

Troubleshooting

• No results returned: check filters, location spelling, property category, listing mode, and whether Aqarmap has matching public records.
• Fewer results than expected: broaden filters, raise limit, or verify that the target segment contains enough matching public listings.
• Some fields are empty: optional fields depend on what each listing publicly provides and whether enrichment is enabled.
• Duplicate-looking records: compare record_id, entity.url, and source_context.canonical_url to decide whether records represent the same listing or related variants.
• Run takes longer than expected: reduce scope, lower limit for validation, or split broad collection into smaller segments.
• Output changed: compare the current output with the Field Reference and include a small sample if support is needed.
• Downstream import failed: check JSON validity, nullable fields, nested objects, and whether your destination expects flattened columns.

FAQ

What data does this actor collect?

It collects public Aqarmap property listing records, including listing identity, source links, title, description, location, pricing, property attributes, media, contact details when available, related parties, and additional source-specific attributes.

Can I filter by location, category, price, keyword, amenities, or listing mode?

Yes. Supported inputs include location, keyword, deal_type, property_type, min_price, max_price, min_area, max_area, amenities, and inside_compound.

Is location required?

No. Provide location for a targeted market search, or leave it empty for a broader category search.

Why did I receive fewer results than my limit?

The selected filters may have fewer matching public listings than the requested limit, or some records may not expose enough usable information to save.

How should I choose a limit for my first run?

Start with a small limit, such as 10 or 25, inspect the dataset, then increase the value once the output shape and field coverage match your workflow.

Can I schedule recurring runs?

Yes. Use Apify schedules to run the same input daily, weekly, or on a custom cadence for monitoring and reporting.

How do I avoid duplicates across runs?

Use record_id when present. If it is unavailable, use entity.url or source_context.canonical_url as the fallback key.

What is the best field to use as a unique key?

The strongest key is record_id for listing records. For extra safety, combine it with source_context.source_domain.

Can I use the output with AI agents or automated workflows?

Yes. The dataset is structured JSON with stable field groups, making it suitable for agentic research, summarization, enrichment, alerting, and downstream routing.

Can I export the data to CSV, Excel, or JSON?

Yes. Apify datasets can be exported in JSON, CSV, Excel, and other supported formats.

Does this actor collect private data?

The actor is intended for publicly available Aqarmap listing information. Users are responsible for using collected data in accordance with applicable laws, regulations, and platform terms.

What should I include when reporting an issue?

Include the input used, run ID, expected behavior, actual behavior, a small output sample if helpful, and the downstream destination or export format if the issue is pipeline-related.

Compliance & Ethics

Responsible Data Collection

This actor collects publicly available property listing information from https://aqarmap.com.eg for legitimate business purposes, including:

• Real-estate research and market analysis.
• Inventory monitoring and operational reporting.
• Lead review, enrichment, and competitive intelligence workflows.

This section is informational and not legal advice.

Best Practices

• Use collected data in accordance with applicable laws, regulations, and the target site’s terms.
• Respect individual privacy and personal information.
• Use data responsibly and avoid disruptive or excessive collection.
• Do not use this actor for spamming, harassment, discrimination, or other harmful purposes.
• Follow relevant data protection requirements where applicable, such as GDPR, CCPA, or sector-specific rules.
• Review your own retention, access control, and data-sharing policies before operationalizing the dataset.

Support

For help, use the Issues tab or the actor page support options. Include the redacted input used, run ID, expected versus actual behavior, a small output sample if relevant, and the downstream destination or export format if the issue is pipeline-related.