PowerExchange Adapters

PowerExchange Adapters for Informatica

PowerExchange for Amazon Redshift

• •You can read and write data of TIMESTAMPZ data type when you run a mapping in the native environment.
• •You can configure key range partitioning when you read data from an Amazon Redshift source and run the mapping in the native environment.
• •You can configure dynamic partitioning when you write data to an Amazon Redshift target and run the mapping in the native environment.

PowerExchange for Amazon S3

• •When you run a mapping on the Spark engine, you can read data from and write data to Avro, ORC, and Parquet files organized based on directories.
• •You can configure an Amazon S3-compatible storage to access and manage the data that is stored over an S3 compliant interface. You can use the Scality RING S3-compatible storage. Use the Amazon S3 connection to connect to Scality RING and perform the read and write operations similar to Amazon S3.
• •You can configure Federated Single Sign-On (SSO) authentication to securely access the Amazon S3 resources.
• •You can configure a cached lookup operation to cache the lookup data when you run a mapping on the Spark engine.
• •You can read and write flat files with and without headers.
• •You can configure row delimiter and define qualifier scope when you read and write flat files.
• •You can perform audits for read operations in Amazon S3 mappings that run in the native environment or on the Spark engine.

PowerExchange for Google BigQuery

• •You can write empty strings from a source as null values to a Google BigQuery target.
• •You can use the Merge query to perform update, upsert, or delete operations in a single statement when you write to a Google BigQuery target.

PowerExchange for Google Cloud Storage

• •You can read the complete path and names of the Google Cloud Storage source files.
• •You can read and write flat files with and without headers.
• •You can configure row delimiter and define qualifier scope when you read and write flat files.

PowerExchange for HDFS

• •When you run a mapping on the Spark engine, you can read data from and write data to Avro, ORC, and Parquet files that are partitioned based on directories.
• •You can perform audits for read operations for complex files such as Avro, Parquet, and JSON in HDFS mappings that run in the native environment or on the Spark engine.

PowerExchange for Hive

PowerExchange for JDBC V2

• •You can use JDBC V2 objects as dynamic sources and targets in mappings.
• •You can configure a mapping to create a JDBC V2 target at run time.
• •You can use a JDBC V2 connection with the SAP HANA Database subtype in mappings to read from or write to HANA tables. You can also read from HANA data modelling views, such as attribute, analytic, and calculation views. You can validate and run JDBC V2 mappings on the Spark or Databricks Spark engine.
• •You can perform audits for read operations in JDBC V2 mappings that run in the native environment or on the Spark engine.

PowerExchange for Microsoft Azure Blob Storage

• •You can read and write flat files with and without headers.
• •You can configure row delimiter and define qualifier scope when you read and write flat files.

PowerExchange for Microsoft Azure Data Lake Storage Gen1

• •You can read and write flat files with and without headers.
• •You can configure row delimiter and define qualifier scope when you read and write flat files.

PowerExchange for Microsoft Azure Data Lake Storage Gen2

• •When you run a mapping on the Spark engine, you can read data from and write data to Avro, ORC, and Parquet files that are partitioned based on directories.
• •You can read and write flat files with and without headers.
• •You can configure row delimiter and define qualifier scope when you read and write flat files.

PowerExchange for Microsoft Azure SQL Data Warehouse

• •You can connect to Microsoft Azure Data Lake Storage Gen2 to stage files at runtime.
• •You can stage files in Parquet format when you read data from Microsoft Azure SQL Data Warehouse.
• •You can override the schema name and table name when you read data from and write data to Microsoft Azure SQL Data Warehouse.
• •You can use Gzip compression to compress data in Gzip format when you write data to Microsoft Azure SQL Data Warehouse in the native environment.
• •You can read and write data of Datetimeoffset data type when you run a mapping on the Spark Engine.
• •You can perform audits for read operations in Microsoft Azure SQL Data Warehouse mappings that run in the native environment or on the Spark engine.
• •You can read data from and write data to Microsoft Azure SQL Data Warehouse case-sensitive databases.

PowerExchange for Salesforce Marketing Cloud

PowerExchange for Snowflake

• •You can read from and write to Snowflake data warehouse that is enabled for staging data in Google Cloud Platform.
• •You can perform audits for read operations in Snowflake mappings that run in the native environment or on the Spark engine.

PowerExchange Adapters for PowerCenter

PowerExchange for Google BigQuery

• •When you perform pushdown optimization using the Google BigQuery ODBC connection, you can use the 2.2.5.1012 version of the Informatica ODBC Driver for Google BigQuery to connect to Google BigQuery and push CHR(), DATEDIFF(), DECODE(), LPAD(), and RPAD() functions to the Google BigQuery database.
Contact Informatica Global Customer Support to obtain the Informatica ODBC Driver for Google BigQuery.
• •You can use the Merge query to perform update, upsert, or delete operations in a single statement when you write to a Google BigQuery target.
• •When you configure a session to write data to Google BigQuery, you can write empty strings from a source as null values to a Google BigQuery target.

PowerExchange for Google Cloud Storage

• •You can read the complete path and names of the Google Cloud Storage source files.
• •You can read and write flat files with and without headers.
• •You can configure row delimiter and define qualifier scope when you read and write flat files.

PowerExchange for Greenplum

PowerExchange for Kafka

PowerExchange for Snowflake