Google Maps Datasets Comparison: Bright Data vs Actowiz

updated on Apr 23, 2026

We compared the top Google Maps dataset providers Bright Data and Actowiz using a field-level benchmark. Rather than ranking the providers, we documented differences in schema breadth, field completeness, null value handling, and data integration readiness.

Both include place-level context, review-level content, and reviewer-level metadata. Bright Data appears stronger in missing-value representation, structured review metadata, and direct integration readiness. Actowiz appears stronger in visible schema breadth and reviewer-oriented field exposure.

To explore how Google Maps data can be gathered using a scraper, see our Google Maps scraper benchmark.

Shared field coverage

Category	Bright Data	Actowiz
Place fields	✅	✅
Review fields	✅	✅
Reviewer fields	✅	✅
Media fields	✅	✅
Response fields	✅	✅

Place fields: URL, ID, name, country, address, category, aggregate rating, review count, location identifiers
Review core fields: review ID, text, rating, date, likes, review details
Reviewer fields: reviewer name, URL, review count, photo count, local guide flag, profile picture URL
Review media fields: review photos
Review response fields: owner response and owner response date

Observed differences in sample usability

Notes for observed differences:

Missing values: In the Actowiz sample, missing entries are shown as N/A, while the Bright Data sample uses null. Placeholder values like N/A usually need to be changed before inserting data into databases, whereas null values work with most analysis tools as is.
Review text availability: In the visible Actowiz rows, the review_text field is often empty or marked N/A, limiting quick qualitative review of user feedback. In the Bright Data sample, visible rows contain more actual review text, making the sample easier to inspect immediately.
Photo count metadata: Actowiz explicitly exposes fields such as review_photos_count, which is useful for directly measuring media presence per review. In the Bright Data sample, photo-related information is present but not as visibly separated into dedicated count fields.

This comparison is based on sample analysis, see the methodology section for details.

Best Google Maps dataset services

Bright Data Google Maps review dataset covers a broad set of place, review, and reviewer fields, similar in scope to Actowiz. However, Bright Data’s sample appears cleaner and more consistent in value presentation.

Missing values are marked as null, which simplifies management in databases, data frames, and workflows. The sample also includes more review text. Additionally, the review_details field contains structured title-value pairs in a JSON-like format, such as food, service, or meal type.

This structure supports aspect-level analysis of review data, beyond raw text. The Bright Data sample also provides reviewer-level fields such as review counts, photo counts, profile URL, local guide flag, and profile picture URL.

Overall, Bright Data offers better sample cleanliness and usability for structured data than providing more visible columns.

The Actowiz Google Maps review dataset features a broad schema that includes standard place and review fields, as well as additional fields for the reviewer and media. These include reviewer profile URL, review and photo counts, local guide flag, profile picture URL, review photo URLs, and review photo count.

This additional reviewer information is valuable for later analysis. Datasets with review text and ratings support sentiment analysis but do not enable studies of reviewer activity or media usage.

A key issue with the Actowiz data is that many columns are empty, and missing values are shown as ‘N/A’. This format is readable in CSV files, but analysis pipelines require these placeholders to be converted to null values before processing.

In summary, Actowiz offers a broader schema, but the data requires additional cleaning before use.

Google Maps review dataset methodology

We used provider samples that represent review-level Google Maps data. Each row in both samples corresponds to a single review, with place-level details such as business name, address, category, and overall rating repeated for each entry.

This benchmark is based on samples and does not assess full production coverage, large-scale extraction reliability, duplicate rates, or field consistency across all provider data. Samples were evaluated using the following criteria: