Changelog

Data pipelines provides expanded connectivity across enterprise apps, ERPs, databases, and file stores — with deep ERP, Finance, and HR reach — plus expanded warehouse and database destinations.

• ERP & Finance — NetSuite, Oracle EBS, Oracle Fusion Cloud, SAP Table Reader, SAP BW OHD, MS Dynamics Business Central, Workday, Workday RaaS, Ellucian Banner, and Sage Intacct.
• Apps — Salesforce, ServiceNow, HubSpot, Coupa, Shopify, Stripe, Zendesk, Jira, Marketo, Intercom, QuickBooks, Zuora, HiBob, Cornerstone, Xero, Asana, BambooHR, Greenhouse, Gong, Anaplan, LinkedIn, Facebook, and more.
• Databases & File Systems — Oracle DB, Microsoft SQL Server, Snowflake, MySQL, Postgres, Google BigQuery, Amazon S3, Google Drive, Google Sheets, SFTP, Azure Blob Storage, and Google Cloud Storage.
• Destinations — Snowflake, Databricks, BigQuery, Microsoft SQL Server On-Prem, Postgres On-Prem, and Amazon Redshift.

Learn more in Data Pipelines.

Data Pipelines connectivity just got a serious upgrade — with expanded sources and destinations, smarter replication, enhanced visibility, and enterprise-grade security built for scale.

• Expanded Connectivity — Sources now include Salesforce, NetSuite, Amazon S3, Azure Blob Storage, Google Cloud Storage, Google Drive, HubSpot, Workday, SAP, ServiceNow, Oracle, and many more. New destinations include Snowflake, Databricks, BigQuery, and Microsoft SQL Server On-Prem.
• Smarter Replication — Automatic schema drift handling, full CDC and incremental sync with deleted record detection, parallel object execution, and auto-error recovery keep pipelines running without manual intervention.
• Visibility & Control — A centralized pipeline dashboard with object-level monitoring and selective resyncs lets teams isolate and fix issues without rerunning entire pipelines.
• Enterprise Security & Flexibility — Built-in data masking, custom cron-based scheduling, source filtering, concurrency controls, and schema-based destination organization give teams full control over how data moves.
• Workato-Native Orchestration — The PipelineOps trigger automatically kicks off downstream recipes the moment data lands, closing the loop between data movement and action.

Learn more in Data Pipelines.

Two new enhancements are available to simplify and streamline working with Snowflake's internal stages:

• Upload to internal stage: Provides a dedicated solution for uploading files to Snowflake's internal stage, simplifying the process of transferring data into Snowflake for further processing or analysis
• Bulk load from stage: Allows for loading files directly from Snowflake's internal stages into a table.

Long actions for databases give users the ability to handle long-running SQL queries and exports of datasets. The timeout limit for long actions is 120 minutes. New actions include:

• Run long query using custom SQL, for executing queries that require extended processing times
• Export query result, for bulk extraction from databases, without worrying about row limits

Long actions for databases are currently available for Snowflake, PostgreSQL, MySQL, and Oracle.

Queries can now aggregate and transform data from Workato data tables, in addition to applications and files, and fetch the query results as CSV to be used in downstream actions.

SQL queries can now process and transform Excel files, providing the ability to aggregate and query across both CSV and Excel data types.

To further enhance our data integration solution, we have updated and added bulk and batch CSV actions and triggers for Workato FileStorage to assist in processing, configuring, and monitoring data.

With FileStorage actions:

• Store a CSV file along with its column data type schema in a directory (Create file action)
• Append CSV lines to an already existing file with column data type validation (Append to file action)
• Fetch CSV lines from a file in batches from an existing file (Get CSV lines from file action)

With FileStorage triggers:

• Trigger and fetch any data type as a byte stream once it is available (New file bulk trigger)
• Extract complete CSV file data in batches (New CSV file batch trigger)
• Monitor a particular CSV file and fetch new lines appended to the file (New lines in a CSV file batch trigger)