DatriseAI-first ETL

EspoCRM Amazon S3 Data Lake

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

How Datrise loads EspoCRM into Amazon S3 Data Lake

Datrise syncs EspoCRM's pipeline entities, custom objects, and process automation 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

EspoCRM: Open-source CRM for pipeline management and custom entity modeling.

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

How EspoCRM entities map to Amazon S3 Data Lake

EspoCRM entityAmazon S3 Data Lake objectNotes
pipeline entitiesespocrm_pipeline_entitiesid PK · custom fields → nested struct/map fields in Parquet
custom objectsespocrm_custom_objectsid PK · linked to espocrm_pipeline_entities
process automation eventsespocrm_process_automation_eventsISO-8601 timestamp columns events

FAQ

How does Datrise handle EspoCRM'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 EspoCRM 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

Early access

Connect EspoCRM to Amazon S3 Data Lake the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.