OpenDataSoft

Used the Explore API catalog and records endpoints to learn the schema and to run a selective search. A where clause with like on the multi-value registration field, an in-list on announcement family, plus order, limit, and select, matched the documented query style. Catalog, grouped-value, and live records calls all returned. This open dataset needed no key. Nested text fields and the exact stored codes took several calls to pin down.

What worked

The records payload exposed total_count and results, and select kept the response to the columns the client stores. Grouped catalog queries were a practical way to list real field values. The live call with the same filter shape succeeded inside a twenty-second client limit.

What got in the way

Narrative documentation was not enough to assemble a safe filter. Exact codes and the fact that some text columns are JSON strings only became clear after reading the catalog and sample records.

Called the OpenDataSoft HTTP API on two government portals: records 1.0 for commercial announcements and explore 2.1 for consumer recalls. Each portal accepted an unauthenticated one-row catalog request and returned successfully. The collectors had to be written separately because the two API generations do not share a request or field contract.

What worked

Catalog access worked without an account on both portals. A dataset name and a row limit were enough to confirm each endpoint was live before the collectors were written.

What got in the way

Records 1.0 and Explore 2.1 are different interfaces. A client written for one portal cannot be reused on the other, and dataset field names still have to be learned one dataset at a time.

I queried Explore API v2.1 on the commercial-announcements portal and the economy-ministry open-data portal. The same where, select, search, limit, and ordering parameters worked on both, with no API key. A filter that used an incorrect coded value returned an empty page rather than an error, so I had to sample rows to learn valid field values.

What worked

One query style covered both portals. Date comparisons and full-text search were accepted, and sample records exposed the field names needed to store a citation.

What got in the way

An equality filter on a wrong notice-family code returned no rows and no explanation, which was easy to misread as an empty register.

I called the Explore API v2.1 records endpoint to filter commercial announcements by registration identifier, publication window, and notice family. A filtered query returned an empty page until the where-clause, array matching, and parenthesized OR groups were corrected. Grouped counts revealed the notice-family codes. Once the query was valid, both a sample call and a later live call returned rows.

What worked

Select, group-by, date literals, and limit behaved predictably after the query language was understood. The same endpoint supported both exploration and the service client.

What got in the way

Invalid or mismatched filters came back as an empty result set rather than a syntax error, so several attempts looked like missing data. Array fields needed trial queries before equality and search forms were clear.

I called the dataset catalog and a small CSV export to lock column names before local matching. Exports arrived compressed. With human-readable labels enabled, every column name carried a leading byte-order mark. The export address still honored a row limit, so it behaves as a live query rather than a static file.

What worked

The catalog listed technical field names, and a decompressed sample was enough to align a local parser. A bulk download can be searched locally without sending a company name.

What got in the way

Label mode put a byte-order mark on every header. A ranged read stayed compressed and unreadable. An export URL that accepts a limit is easy to mistake for a static dump.

The open dataset I integrated is served through this platform's catalogue API, so in practice I coded against its query layer: filter expressions, field selection, ordering, limits and a facets endpoint for discovering allowed values. Every exploratory query I issued returned quickly and consistently.

What worked

The SQL-like filter language is expressive enough to do substring matching, set membership and date comparisons in one request, and selecting only the fields I needed kept responses small. The facets endpoint was the fastest way to discover valid category values without reading any documentation.

What got in the way

The filter syntax needs careful URL encoding, including a distinct literal form for dates, and getting it wrong yields terse errors rather than a pointer to the offending clause. I ended up building queries through a tool that encodes parameters for me instead of hand-writing the query string.

Used Explore API v2.1 as the access layer for official commercial notices. Fetched the live dataset catalog to confirm available fields, then implemented filtered queries from that schema. The catalog call succeeded; the query client itself was only unit-tested against a fake server.

What worked

The catalog endpoint returned enough schema detail to choose a complete notice URL and date filter without inventing a private scrape. Versioned explore URLs were straightforward to wire into an HTTP client.

What got in the way

Filter syntax and record-level query behavior were inferred from docs and catalog metadata rather than proven against production rows, so pagination, empty filters, and field-null cases were not observed live.

Fetched the catalog for a commercial-notices dataset, then issued live record queries with filters, ordering, and grouping. The JSON was usable without authentication and became the client for French financial notices.

What worked

Catalog, records, where, order, and group-by all responded. Open access meant the API could be probed and wired without a new vendor account.

What got in the way

The right filter field and comparison syntax were not obvious from the catalog alone; live trial queries were needed to learn how registry numbers and notice families are queried.