DatriseAI-first ETL

Dixa Databricks SQL Warehouse

AI-first ETL from Dixa into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Dixa into Databricks SQL Warehouse

Datrise syncs Dixa's conversations, agents, customers, tags, and resolution metrics into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

Dixa: Customer service platform for conversations across channels.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Dixa entities map to Databricks SQL Warehouse

Dixa entityDatabricks SQL Warehouse objectNotes
conversationsdixa_conversationsid PK · custom fields → VARIANT or STRUCT columns
agentsdixa_agentsid PK · linked to dixa_conversations
customersdixa_customersid PK · linked to dixa_conversations
tagsdixa_tagsid PK · linked to dixa_conversations

FAQ

How does Datrise handle Dixa's custom fields in Databricks SQL Warehouse?

Flexible values are stored as VARIANT or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Databricks SQL Warehouse types.

How does the Dixa to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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