Every source into Microsoft Fabric, in open file formats.

Parquet, CSV, JSON, and JSONL. Each flow run writes a file in the format you pick, with format-level controls - CSV delimiter, header, and date format, or the timestamp unit for Parquet, JSON, and JSONL.

Each flow run lands as its own dated file, so Microsoft Fabric is organized as a date-partitioned dataset. Query engines then read only the partitions they need instead of scanning everything, which keeps queries fast and costs predictable.

With OAuth 2.0 or a Microsoft service principal (client ID, client secret, tenant ID and scopes). The authorizing account needs at least admin-level permissions.

Dataddo writes into a Fabric Lakehouse through OneLake as CSV, JSON, JSONL or Parquet files. File names support tokens such as {{objectLabel}} and {{today|Ymd}} for date-based naming.

The target path needs write access and Microsoft Fabric must be reachable by Dataddo. See docs.dataddo.com/docs/microsoft-fabric for the current setup steps.

Yes. Insert keeps writing new files, and create-new-or-replace overwrites the file at the same name. For lakehouse table formats like Apache Iceberg, Dataddo applies warehouse-style write modes to the table itself.

Dataddo is SOC 2 Type II and ISO 27001 certified. Data is encrypted in transit and at rest, PII can be masked or hashed, and EU or US data residency is available. You connect Microsoft Fabric with your own keys or an assumed role.

Incremental loads write only new or changed data, and columnar Parquet compresses well - so you store and scan less. You control the schedule and the partition layout, which keeps storage and query costs predictable.

No. Data lands in open file formats in a bucket you own, and pipelines are storage-agnostic - you can add or switch object stores, or point the same sources at a warehouse, without rebuilding anything.