Supply Chain & Retail Solutions release notes

A new Integration walkthrough page in the API Guide walks customer IT teams, solution engineers, and support engineers through the four stages of integrating with the Data Ingestion API: extract, transform, align field names, and batch + send. Each stage has the relevant API calls embedded inline — GET /api/v2/schema/solutions to confirm what's rolled out, POST /api/v2/objects/{objectName} for ingesting batches (with a dryRun variant), and POST /api/v2/schema/{objectName}/add-attribute for adding tenant-specific columns — alongside Snowflake COPY INTO and Redshift UNLOAD recipes for the extract step. Designed as a single one-stop guide rather than scattered references.

The Historical data ingestion guide's "File or folder?" section now spells out the folder-load sort-order rules. Lexicographic sort gives the right order when filenames encode order as zero-padded numbers (part-0001.csv) or ISO-style date prefixes (2024-06-01.csv) — both forms warehouse exports produce by default. It gives the wrong order for non-padded part numbers, DD-MM-YYYY or MM-DD-YYYY date names, or month-name files like January_report.csv. The doc now lists each failure case in a comparison table and recommends the safe naming pattern.

The Markdown & Promotions pages in the API Guide have been brought fully in line with the canonical schema. The visible changes:

Pipelines preparing data against the previous documentation may need to add the columns that were missing from location, update their primary-key generation for tax, and add data flows for sale_extra / stock_extra / currency_conversion if those are needed for the use case.

A new guide covers loading large historical datasets (millions to billions of rows) into the Data Ingestion API. It explains the design considerations for at-scale loads — streaming, dynamic batching, rate-limiting, foreign-key ordering, checkpointing, and failed-row handling — and provides a self-contained Python reference script you can configure and run. See Historical data ingestion.

The Python reference script in the historical-data-ingestion guide:

The guide also includes warehouse-specific UNLOAD / COPY INTO recipes for Snowflake and Redshift, and a section on re-running the script (resume behaviour, where checkpoint and failed-row files live, how to start fresh).

The API guide now frames the choice between APPEND and UPSERT around what the downstream solution needs from the data — current state vs full history — rather than around data type. The solution-specific guides state the recommended operation per table where it matters.

Every per-table page in the API Guide now includes a Nullable column next to Required. The two flags are independent:

This makes it possible to distinguish "I must send the key but the value can be null" from "I must send a non-null value" at a glance per column. See Schema definition structure for the underlying validation rules.

The Add a new column section now has a Controlling required-ness and nullability on the new column subsection. It documents that required and nonNull are independent validators in the add-column path and lays out the four valid combinations as a small table. It also calls out the API-enforced mutual exclusion of defaultValue and nonNull — sending both returns 400 Bad Request with the exact error message the API produces.