DatriseAI-first ETL

The Guardian API to Azure Data Lake Storage Integration

AI-first ETL from The Guardian API into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.

How Datrise loads The Guardian API into Azure Data Lake Storage

Datrise syncs The Guardian API's articles, sections, tags, contributors, publication dates, and headlines into Azure Data Lake Storage as partitioned Parquet in ADLS Gen2 per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes new date partitions to the container and compacts on a schedule, so re-runs update only what changed. Hive-style partitioning by load date, readable by Synapse and Databricks. ADLS hierarchical namespace makes folder layout matter, so Datrise keeps a predictable entity/date path your Azure engines mount directly.

Ideal for Azure lakehouse storage shared across Synapse and Databricks.

Endpoints

The Guardian API: Guardian article archive and section metadata.

Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.

How The Guardian API entities map to Azure Data Lake Storage

The Guardian API entityAzure Data Lake Storage objectNotes
articlesthe_guardian_api_articlesid PK · custom fields → nested struct/map fields in Parquet
sectionsthe_guardian_api_sectionsid PK · linked to the_guardian_api_articles
tagsthe_guardian_api_tagsid PK · linked to the_guardian_api_articles
contributorsthe_guardian_api_contributorsid PK · linked to the_guardian_api_articles

FAQ

How does Datrise handle The Guardian API's custom fields in Azure Data Lake Storage?

Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Azure Data Lake Storage types.

How does the The Guardian API to Azure Data Lake Storage sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions to the container and compacts on a schedule.

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