The October 2026 release of Data Integration Connectors includes the following new features and enhancements.
Common
This release includes the following enhancement across multiple connectors:
When you create a Source transformation, you can select from multiple supported operations. You can use the request message editor to configure the input parameters for the operation that you want to perform.
This enhancement applies to the connectors listed in the following table:
- Amplitude Connector
- Klaviyo Connector
- Mixpanel Connector
- SurveyMonkey Connector
- Xero Connector
Amazon Redshift V2 Connector
Amazon Redshift V2 Connector uses JDBC driver version 2.2.5.
Amazon S3 V2 Connector
You can use the MS 932 code page when you read from and write to a flat file.
This release includes the following enhancements for Databricks Connector:
•You can use OAuth Machine-to-Machine authentication for service principals to read from and write to Databricks objects in a mapping in advanced mode. OAuth Machine-to-Machine authentication uses the OAuth 2.0 protocol to authenticate access to Databricks objects.
•You can use IAM AssumeRole authentication for secure and controlled access to the Amazon S3 staging bucket when you run a mapping in advanced mode.
•To optimize reader performance, you can configure the Secure Agent to stage the data as flat files in a local temporary folder after it reads from Databricks. You can also use Gzip compression for the flat files.
•You can specify the number of rows to fetch from the database per batch in a mapping that reads from Databricks.
You can use Workload Identity Federation with the client credentials grant to authenticate the Google BigQuery V2 connection. As a result, you can connect through your existing identity provider and access datasets across Google Cloud projects.
This release includes the following enhancements for Google Cloud Storage V2 Connector:
•You can use Workload Identity Federation with the client credentials grant to authenticate the Google Cloud Storage V2 connection. As a result, you can authenticate through your existing identity provider and access data across Google Cloud projects.
•You can configure a flat file source to read field values that contain line breaks as part of a single record. As a result, you can read source data with multi-line values correctly and avoid splitting a record into multiple records.
You can run mappings that connect to Confluent Kafka with Schema Registry when the Schema Registry response contains the metadata field. As a result, you can maintain reliable, real-time data integration with Confluent Kafka environments that return additional schema metadata.
Microsoft Azure Blob Storage V3 Connector uses the Azure Blob File System (ABFS) driver, replacing the Windows Azure Storage Blob (WASB) driver, for complex file formats. The connector also uses Microsoft Azure Storage SDK V2, replacing the earlier SDK version that is reaching end-of-life.
Microsoft Azure Synapse SQL Connector
You can configure the Microsoft recommended JDBC driver version 13.4.0.jre8 available in the BASE folder to run all your mappings and tasks.
This release includes the following enhancements for Microsoft Fabric Data Warehouse Connector:
•You can use the Copy command to stage data as Parquet files in Microsoft Fabric OneLake and load the data into Microsoft Fabric Data Warehouse tables.
•You can read data from Microsoft Fabric Data Warehouse tables that contain an IDENTITY column.
•You can push the IIF logical function to Microsoft Fabric Data Warehouse.
For a list of functions that you can push down to Microsoft Fabric Data Warehouse, see the Microsoft Fabric Data Warehouse Connector documentation.
•You can configure the Microsoft recommended JDBC driver version 13.4.0.jre8 available in the BASE folder to run all your mappings and tasks.
When you write data to a MongoDB V2 target in a mapping task configured with an error threshold, you can capture the rejected rows in an error file. This enhancement doesn't apply to mapping tasks that are based on mappings in advanced mode.
This release includes the following enhancements for Open Table Connector:
•You can read from and write to Apache Iceberg tables that are managed by the Nessie catalog and stored in MinIO.
•You can commit all the rows at once or periodically based on the commit interval in a mapping that writes to Apache Iceberg tables.
•You can use a custom query as a source object in a mapping.
You can create a custom query to perform a complicated join of multiple tables or to reduce the number of fields that enter the data flow in a very large source.
•You can read from and write to Apache Iceberg tables that are managed by the REST Catalog and stored in Amazon S3 in a mapping.
•You can read from and write to Apache Iceberg tables that are managed by the REST Catalog and stored in Microsoft Azure Data Lake Storage Gen2 in a mapping in advanced mode.
•When you query data using a specific snapshot or timestamp, you can use absolute values or relative references, such as latest or latest-n.
•The ODBC driver available in the Latest folder uses OpenSSL 3.5.
Oracle Autonomous Database Connector
You can read from and write to Oracle Autonomous Database tables that contain TIMESTAMP WITH TIME ZONE and TIMESTAMP WITH LOCAL TIME ZONE columns. As a result, you can process time-zone-sensitive data in your integrations and maintain time zone accuracy.
You can configure host patterns to bypass the configured forward proxy and connect directly to Oracle Cloud Object Storage. As a result, you can route traffic according to your network policies instead of sending every connection through the proxy.
•You can connect to PostgreSQL databases through an HTTP proxy server configured on the Secure Agent. As a result, you can integrate PostgreSQL data in secured networks that route outbound traffic through a proxy.