Connectors

Warehouses & lakehouses

Land governed, query-ready tables where your analysts already look.

Landing files in a bucket is easy; landing data your analysts can actually find is the harder part. These connectors write into the analytical platform you already operate and register the result where teams expect it — a managed Delta table under Unity Catalog, a Fabric lakehouse or warehouse, an Iceberg table in your own catalog. Several are self-contained, meaning the connector owns the whole path from Arrow batches to a registered table; others stage files and apply them with the platform's own load command. Write modes cover append, overwrite and merge, so a table can be kept current incrementally instead of rebuilt every night.

Databricks Managed Delta TableSink

Writes managed Delta tables under Unity Catalog. Data is staged in a volume and applied with COPY INTO, MERGE INTO or a full replace, so the table ends up registered and query-ready.

Databricks Managed Delta (ADBC)Sink

The direct-streaming route into Databricks: Arrow batches are ingested over ADBC into a staging table and merged, skipping volume staging entirely.

SnowflakeSink

Loads into Snowflake by staging files and running COPY INTO, with append, overwrite and merge all supported.

Google BigQuerySink

Loads Parquet partitions into BigQuery through native load jobs. The dataset and table are expected to exist, which fits a managed-schema setup.

Microsoft Fabric LakehouseSink

Writes into the Tables area of a Fabric lakehouse. Data is staged in OneLake and applied by generated PySpark running in a Fabric session.

Microsoft Fabric WarehouseSink

Writes into a Fabric warehouse over its SQL endpoint, either with COPY INTO or through a staging table and a MERGE, depending on the write mode.

Apache IcebergSink

Writes Iceberg tables against the catalog of your choice (see below), using a storage connection you have already configured. Append and upsert are supported, the latter through equality deletes.

AWS S3 TablesSink

Writes managed Iceberg tables on S3, committing snapshots through the REST catalog. Append, overwrite and merge are supported.

Apache PolarisSink

Register your Iceberg tables in an Apache Polaris catalog: BARRY commits snapshots through the Polaris REST catalog with OAuth2 client credentials, so engines that share the catalog see the data immediately.

Project NessieSink

An Iceberg catalog with git-style branches and commits. BARRY writes snapshots to the branch you choose through the Nessie REST catalog, so you can stage and promote data changes like code.

AWS Glue (Iceberg)Sink

Register Iceberg tables in the AWS Glue Data Catalog through its Iceberg REST endpoint, with requests signed by SigV4 against a storage connection you have already configured.

DremioSinkPlanned

On the roadmap: Dremio speaks the Iceberg REST catalog, so once BARRY exposes its generic REST-catalog option you will be able to land Iceberg tables that Dremio queries directly.

Databricks LakeflowSink

A pull endpoint rather than a push: Databricks Lakeflow reads from BARRY on demand, so nothing has to be staged or copied in advance.

Spark DirectSink

Exposes data to Spark as a readable source, so a cluster reads directly from BARRY instead of waiting for a landed copy to appear.

Looking for something else?

Every connection is Source, Sink, Trigger or Alert — derived from the connector itself, never set by hand. Browse the full catalogue, or ask us about a system you don’t see.

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