DatriseAI-first ETL

Bitrix24 Azure Data Lake Storage

AI-first ETL from Bitrix24 into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.

How Datrise loads Bitrix24 into Azure Data Lake Storage

Datrise syncs Bitrix24's leads, deals, contacts, companies, activities, and contact-center 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

Bitrix24: CRM, contact center, and task automation suite.

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

How Bitrix24 entities map to Azure Data Lake Storage

Bitrix24 entityAzure Data Lake Storage objectNotes
leadsbitrix24_leadsid PK · custom fields → nested struct/map fields in Parquet
dealsbitrix24_dealsid PK · linked to bitrix24_leads
contactsbitrix24_contactsid PK · linked to bitrix24_leads
companiesbitrix24_companiesid PK · linked to bitrix24_leads

FAQ

How does Datrise handle Bitrix24'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 Bitrix24 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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