Visma e-conomic to Amazon S3 Data Lake Integration
AI-first ETL from Visma e-conomic into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Visma e-conomic into Amazon S3 Data Lake
Datrise syncs Visma e-conomic's invoices, customers, suppliers, journal entries, accounts, and products 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
Visma e-conomic: Nordic accounting, invoicing, and ledger records.
Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.
How Visma e-conomic entities map to Amazon S3 Data Lake
| Visma e-conomic entity | Amazon S3 Data Lake object | Notes |
|---|---|---|
| invoices | visma_e_conomic_invoices | id PK · custom fields → nested struct/map fields in Parquet |
| customers | visma_e_conomic_customers | id PK · linked to visma_e_conomic_invoices |
| suppliers | visma_e_conomic_suppliers | id PK · linked to visma_e_conomic_invoices |
| journal entries | visma_e_conomic_journal_entries | id PK · linked to visma_e_conomic_invoices |
FAQ
How does Datrise handle Visma e-conomic'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 Visma e-conomic 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
More destinations for Visma e-conomic
- Visma e-conomic → Azure Data Lake Storage
- Visma e-conomic → Azure Synapse
- Visma e-conomic → Spreadsheets
- Visma e-conomic → Airtable
- Visma e-conomic → CSV Files
- Visma e-conomic → MongoDB
- Visma e-conomic → Supabase
- Visma e-conomic → Neon
- Visma e-conomic → PlanetScale
- Visma e-conomic → Amazon DynamoDB
- Visma e-conomic → Looker
- Visma e-conomic → Looker Studio
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Early access
Connect Visma e-conomic to Amazon S3 Data Lake the easy way
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