DatriseAI-first ETL

Creatio Amazon S3 Data Lake

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

How Datrise loads Creatio into Amazon S3 Data Lake

Datrise syncs Creatio's no-code CRM processes, entities, and cross-team workflow orchestration 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

Creatio: No-code CRM and process automation platform for enterprise teams.

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

How Creatio entities map to Amazon S3 Data Lake

Creatio entityAmazon S3 Data Lake objectNotes
no-code CRM processescreatio_no_code_crm_processesid PK · custom fields → nested struct/map fields in Parquet
entitiescreatio_entitiesid PK · linked to creatio_no_code_crm_processes
cross-team workflow orchestrationcreatio_cross_team_workflow_orchestrationid PK · linked to creatio_no_code_crm_processes

FAQ

How does Datrise handle Creatio'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 Creatio 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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