DatriseAI-first ETL

Microsoft Azure to Amazon S3 Data Lake Integration

AI-first ETL from Microsoft Azure into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.

How Datrise loads Microsoft Azure into Amazon S3 Data Lake

Datrise syncs Microsoft Azure's subscriptions, resource groups, resources, usage records, costs, and activity logs into Amazon S3 Data Lake as columnar Parquet objects partitioned 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 and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.

Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.

Endpoints

Microsoft Azure: Azure subscription resources, usage, and billing.

Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.

How Microsoft Azure entities map to Amazon S3 Data Lake

Microsoft Azure entityAmazon S3 Data Lake objectNotes
subscriptionsmicrosoft_azure_subscriptionsid PK · custom fields → nested struct/map fields in Parquet
resource groupsmicrosoft_azure_resource_groupsid PK · linked to microsoft_azure_subscriptions
resourcesmicrosoft_azure_resourcesid PK · linked to microsoft_azure_subscriptions
usage recordsmicrosoft_azure_usage_recordsid PK · linked to microsoft_azure_subscriptions

FAQ

How does Datrise handle Microsoft Azure's custom fields in Amazon S3 Data Lake?

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 Amazon S3 Data Lake types.

How does the Microsoft Azure to Amazon S3 Data Lake sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.

Related pipelines

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