DatriseAI-first ETL

Odoo CRM Amazon S3 Data Lake

AI-first ETL from Odoo CRM into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.

How Datrise loads Odoo CRM into Amazon S3 Data Lake

Datrise syncs Odoo CRM's modular CRM entities, opportunities, and revenue process data 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

Odoo CRM: Modular CRM in the Odoo suite for leads, opportunities, and revenue.

Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.

How Odoo CRM entities map to Amazon S3 Data Lake

Odoo CRM entityAmazon S3 Data Lake objectNotes
modular CRM entitiesodoo_crm_modular_crm_entitiesid PK · custom fields → nested struct/map fields in Parquet
opportunitiesodoo_crm_opportunitiesid PK · linked to odoo_crm_modular_crm_entities
revenue process dataodoo_crm_revenue_process_dataid PK · linked to odoo_crm_modular_crm_entities

FAQ

How does Datrise handle Odoo 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 Odoo 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.

Related pipelines

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