DatriseAI-first ETL

Fastbill to Amazon S3 Data Lake Integration

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

How Datrise loads Fastbill into Amazon S3 Data Lake

Datrise syncs Fastbill's invoices, customers, line items, payments, expenses, and recurring invoices 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

Fastbill: Invoicing and accounting for small businesses.

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

How Fastbill entities map to Amazon S3 Data Lake

Fastbill entityAmazon S3 Data Lake objectNotes
invoicesfastbill_invoicesid PK · custom fields → nested struct/map fields in Parquet
customersfastbill_customersid PK · linked to fastbill_invoices
line itemsfastbill_line_itemsid PK · linked to fastbill_invoices
paymentsfastbill_paymentsid PK · linked to fastbill_invoices

FAQ

How does Datrise handle Fastbill'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 Fastbill 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 Fastbill 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.