Method:CRM → Azure Data Lake Storage
AI-first ETL from Method:CRM into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.
How Datrise loads Method:CRM into Azure Data Lake Storage
Datrise syncs Method:CRM's contacts, accounts, deals, activities, and lifecycle events 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
Method:CRM: CRM for SMB teams managing pipeline, contacts, and customer activity.
Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.
How Method:CRM entities map to Azure Data Lake Storage
| Method:CRM entity | Azure Data Lake Storage object | Notes |
|---|---|---|
| contacts | method_crm_contacts | id PK · custom fields → nested struct/map fields in Parquet |
| accounts | method_crm_accounts | id PK · linked to method_crm_contacts |
| deals | method_crm_deals | id PK · linked to method_crm_contacts |
| activities | method_crm_activities | ISO-8601 timestamp columns events |
FAQ
How does Datrise handle Method:CRM'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 Method:CRM 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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More sources for Azure Data Lake Storage
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- Holded → Azure Data Lake Storage
- ForceManager → Azure Data Lake Storage
Early access
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