DatriseAI-first ETL

Visma E Conomic Amazon Athena

AI-first ETL from Visma E Conomic into Amazon Athena. Governed entities, incremental sync, typed landing tables.

How Datrise loads Visma E Conomic into Amazon Athena

Datrise syncs Visma E Conomic's records, events, and configuration objects into Amazon Athena as partitioned Parquet in S3 exposed as an Athena table. Flexible or custom fields land in struct/map columns in Parquet, and timestamps such as created, updated, and status changes are typed as timestamp.

Sync is incremental: Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog, so re-runs update only what changed. Hive-style partitioning by load date so Athena scans only new data. Athena bills per byte scanned and small files hurt, so Datrise compacts to right-sized Parquet rather than many tiny objects.

Ideal for serverless SQL over an S3 lake without a running warehouse.

Endpoints

Visma E Conomic: SaaS or API data source for analytics and warehouse sync.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Visma E Conomic entities map to Amazon Athena

Visma E Conomic entityAmazon Athena objectNotes
recordsvisma_e_conomic_recordsid PK · custom fields → struct/map columns in Parquet
eventsvisma_e_conomic_eventstimestamp events
configuration objectsvisma_e_conomic_configuration_objectsid PK · linked to visma_e_conomic_records

FAQ

How does Datrise handle Visma E Conomic's custom fields in Amazon Athena?

Flexible values are stored as struct/map columns in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Athena types.

How does the Visma E Conomic to Amazon Athena sync stay up to date?

It runs incrementally — Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog.

Related pipelines

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