DatriseAI-first ETL

1С:CRM Azure Data Lake Storage

AI-first ETL from 1С:CRM into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.

How Datrise loads 1С:CRM into Azure Data Lake Storage

Datrise syncs 1С: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

1С:CRM: CRM with strong adoption in CIS markets for sales and operations.

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

How 1С:CRM entities map to Azure Data Lake Storage

1С:CRM entityAzure Data Lake Storage objectNotes
contacts1c_crm_contactsid PK · custom fields → nested struct/map fields in Parquet
accounts1c_crm_accountsid PK · linked to 1c_crm_contacts
deals1c_crm_dealsid PK · linked to 1c_crm_contacts
activities1c_crm_activitiesISO-8601 timestamp columns events

FAQ

How does Datrise handle 1С: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 1С: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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