1С:CRM → Amazon S3 Data Lake
AI-first ETL from 1С:CRM into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.
How Datrise loads 1С:CRM into Amazon S3 Data Lake
Datrise syncs 1С:CRM's contacts, accounts, deals, activities, and lifecycle events into Amazon S3 Data Lake as columnar Parquet objects partitioned 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 and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.
Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.
Endpoints
1С:CRM: CRM with strong adoption in CIS markets for sales and operations.
Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.
How 1С:CRM entities map to Amazon S3 Data Lake
| 1С:CRM entity | Amazon S3 Data Lake object | Notes |
|---|---|---|
| contacts | 1c_crm_contacts | id PK · custom fields → nested struct/map fields in Parquet |
| accounts | 1c_crm_accounts | id PK · linked to 1c_crm_contacts |
| deals | 1c_crm_deals | id PK · linked to 1c_crm_contacts |
| activities | 1c_crm_activities | ISO-8601 timestamp columns events |
FAQ
How does Datrise handle 1С:CRM's custom fields in Amazon S3 Data Lake?
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 Amazon S3 Data Lake types.
How does the 1С:CRM to Amazon S3 Data Lake sync stay up to date?
It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.
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Early access
Connect 1С:CRM to Amazon S3 Data Lake the easy way
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