DatriseAI-first ETL

Vitally to Azure Data Lake Storage Integration

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

How Datrise loads Vitally into Azure Data Lake Storage

Datrise syncs Vitally's accounts, users, health scores, tasks, playbooks, and product usage 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

Vitally: Customer success health and account analytics.

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

How Vitally entities map to Azure Data Lake Storage

Vitally entityAzure Data Lake Storage objectNotes
accountsvitally_accountsid PK · custom fields → nested struct/map fields in Parquet
usersvitally_usersid PK · linked to vitally_accounts
health scoresvitally_health_scoresid PK · linked to vitally_accounts
tasksvitally_tasksid PK · linked to vitally_accounts

FAQ

How does Datrise handle Vitally'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 Vitally 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.

Related pipelines

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