DatriseAI-first ETL

Front to Azure Data Lake Storage Integration

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

How Datrise loads Front into Azure Data Lake Storage

Datrise syncs Front's conversations, messages, contacts, inboxes, tags, and teammates 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

Front: Shared inbox for team email and messaging.

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

How Front entities map to Azure Data Lake Storage

Front entityAzure Data Lake Storage objectNotes
conversationsfrontapp_conversationsid PK · custom fields → nested struct/map fields in Parquet
messagesfrontapp_messagesid PK · linked to frontapp_conversations
contactsfrontapp_contactsid PK · linked to frontapp_conversations
inboxesfrontapp_inboxesid PK · linked to frontapp_conversations

FAQ

How does Datrise handle Front'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 Front 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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