This integration allows users to schedule, trigger, monitor, and orchestrate the Google BigQuery process directly from the Universal Controller. Key Features:  Users can perform the below Google BigQuery operations: BigQuery SQL. List dataset. List tables in a dataset. View job information. Create a dataset. Load local file to a table. Load cloud storage data to a table. Export table data. Additional Info: This task uses Python google-cloud-bigquery and google-auth modules to make REST API calls to Google BigQuery. This task will use the GCP project ID, BigQuery SQL or schema, dataset ID, job ID, location, table ID, cloud storage URI, source file format as parameters of BigQuery function and GCP KeyFile (API KEY) of service account for authenticating the REST API calls to Google BigQuery.

Stream data from one object store to another without intermediate storage.The Inter-Cloud Data Transfer integration allows users to transfer data to, from, and between any of the major private and public cloud providers like AWS, Google Cloud, and Microsoft Azure. It also supports the transfer of data to and from a Hadoop Distributed File System (HDFS) and to major cloud applications like OneDrive and SharePoint. Integrations within this solution package include: AWS S3. Google Cloud. SharePoint. Dropbox. OneDrive. Hadoop HDFS. Key Features: Transfer data to, from, and between any cloud provider. Transfer between any major storage applications like SharePoint or Dropbox. Transfer data to and from a Hadoop HDFS. Stream data from one object store to another – no intermediate storage. Executes quickly if the object stores are in the same region. Preserves always timestamps and verifies checksums. Supports encryption, caching, compression, and chunking. Supports regular expressions based on include/exclude filter rules. Supported actions are: List objects, list directory. Copy/move. Remove object/object store. Perform dry-runs. Monitor object. Copy URL.