Over the last year, we have been migration our databases to a new data warehouse that delivers significant improvements in terms of performance and data quality. We are extremely excited to announce that we have finally finished the migration today with the launch of our new Property Data.
You can read our full announcement here.
Our documentation has been updated to provide Datafiniti users with a thorough understanding of how to work with the new Property Data. Here are some helpful links:
- You must now specify a
formatwith your API call.
formatcan be set to
CSV. If you don’t supply a format, the API response will default to
- Some field names have changed in order to abide by a consistent naming convention. See the last note under Data Quality below. You can view the updated product schema here.
- The presentation and ordering of data in API responses or downloads may be different due to removal of fields or changes in field names. Some fields have been "demoted" to our
All fields are searchable
- Users can now query all fields. Previously, certain fields could not be queried.
Query on nested fields
- You can now query on fields within multi-valued fields, except for the
reviews.textnested field. E.g.,
q=reviews.rating:5will return all products with at least one review that has a 5-star rating.
Easier querying on sourceURLs
- Previously, querying on the
sourceURLsfield required running a time-intensive wildcard search or knowing the exact http format of the sourceURL. Now, users can just do a query like
q=sourceURLs:shop.lego, and all relevant products will return.
Use comparison operators
- You can now do a query like
q=reviews.rating:>1to return all businesses with a review greater than 1. >, >=, <, and <= are supported. These should only be used on fields that only contain numeric values.
- Inserting raw web crawl data into the database now happens 50x faster than before.
- Downloading full data sets can typically be done in minutes. For context, our entire business database can now be downloaded in less than 3 hours.
- Overall improvement in stability and reliability of our back-end.
Implemented new merging algorithm to reduce duplicate data
- We have implemented a more consistent approach to merging, that should lead to less duplicates and over merged records (e.g., one record for an entire mall).
More rigorous validation and normalization before database insertion
- Raw data from web crawls will now pass through a comprehensive suite of validation checks before being accepted into the database.
- Validation checks will also normalize values where needed to produce more standardized values.
Cleanup of existing data
- While migrating data from our old database into ElasticSearch, we applied our validation checks and normalization methods to cleanup historical data. As a result, data is completely standardized throughout the database.
- Included in this cleanup is a full standardization of date strings that appear in the data.
Support for foreign characters
- Foreign characters will no longer be encoded.
Record counts are consistent
- Previously, quick successive API calls could result in dramatically different estimated_total values, which was confusing. Our new backend shows the same
estimated totalvalue each time for the same API call (barring any additions to the database).
Standardized naming convention for all fields
- All fields are now pascal-cased. E.g.,
managedBy'. All multi-valued fields now use a plural word. E.g.,reviews