Zendesk Talk to Azure Data Lake Storage Integration
AI-first ETL from Zendesk Talk into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.
How Datrise loads Zendesk Talk into Azure Data Lake Storage
Datrise syncs Zendesk Talk's calls, agents, queues, call legs, recordings, and call metrics 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
Zendesk Talk: Voice support calls and queue performance.
Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.
How Zendesk Talk entities map to Azure Data Lake Storage
| Zendesk Talk entity | Azure Data Lake Storage object | Notes |
|---|---|---|
| calls | zendesk_talk_calls | id PK · custom fields → nested struct/map fields in Parquet |
| agents | zendesk_talk_agents | id PK · linked to zendesk_talk_calls |
| queues | zendesk_talk_queues | id PK · linked to zendesk_talk_calls |
| call legs | zendesk_talk_call_legs | id PK · linked to zendesk_talk_calls |
FAQ
How does Datrise handle Zendesk Talk'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 Zendesk Talk 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
More destinations for Zendesk Talk
- Zendesk Talk → Azure Synapse
- Zendesk Talk → Spreadsheets
- Zendesk Talk → Airtable
- Zendesk Talk → CSV Files
- Zendesk Talk → MongoDB
- Zendesk Talk → Supabase
- Zendesk Talk → Neon
- Zendesk Talk → PlanetScale
- Zendesk Talk → Amazon DynamoDB
- Zendesk Talk → Looker
- Zendesk Talk → Looker Studio
- Zendesk Talk → Microsoft Power BI
More sources for Azure Data Lake Storage
- Zenefits → Azure Data Lake Storage
- Zenloop → Azure Data Lake Storage
- Zuora → Azure Data Lake Storage
- Salesforce Pardot → Azure Data Lake Storage
- Salesforce Commerce Cloud → Azure Data Lake Storage
- Salesforce Service Cloud → Azure Data Lake Storage
- Less Annoying CRM → Azure Data Lake Storage
- Streak → Azure Data Lake Storage
- Apptivo → Azure Data Lake Storage
- folk → Azure Data Lake Storage
- Clay → Azure Data Lake Storage
- Day.ai → Azure Data Lake Storage
Early access
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