Every source into Microsoft SharePoint, 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 SharePoint 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 (tenant ID defaults to common) or a Microsoft service principal (client ID, client secret, tenant ID and scopes). The account needs write access to the target path.

You pick a SharePoint Site, then a Drive within it, then a Path (folder). Output is CSV, JSON, JSONL or Parquet, with file-name tokens such as {{today|Ymd}} and {{90d1}}.

Both. truncate_insert (the default) refreshes one latest file at the resolved name; insert keeps a new file per run for a partitioned history that Power BI and Excel can read directly.

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 SharePoint 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.