Preface
Introduction to Informatica Data Engineering Integration
- Informatica Data Engineering Integration Overview
  - Example
- Data Engineering Integration Component Architecture
- Data Engineering Integration Engines
- Data Engineering Process
- Data Warehouse Optimization Mapping Example
Mappings
- Overview of Mappings
- Mapping Run-time Properties
- PreSQL and PostSQL Queries for JDBC Sources
- Sqoop Mappings in a Hadoop Environment
- Mapping Output Binding
- Rules and Guidelines for Mappings in a Non-native Environment
- Workflows that Run Mappings in a Non-native Environment
- Configuring a Mapping to Run in a Non-native Environment
- Mapping Execution Plans
- Troubleshooting Mappings in a Non-native Environment
- Mappings in the Native Environment
Mapping Optimization
- Mapping Optimization
- Mapping Recommendations and Analysis
- Enabling Data Compression on Temporary Staging Tables
  - Step 1. Enable Data Compression in the Hadoop Connection
  - Step 2. Enable Data Compression on the Hadoop Environment
- Truncating Partitions in a Hive Target
- Hive Warehouse Connector and Hive LLAP
- Scheduling, Queuing, and Node Labeling
- Data Engineering Recovery
- Spark Engine Optimization for Sqoop Pass-Through Mappings
Sources
- Overview of Sources
- PowerExchange Adapter Sources
- Sources on Databricks
- File Sources on Hadoop
- Relational Sources on Hadoop
- Hive Sources on Hadoop
  - PreSQL and PostSQL Commands
  - Rules and Guidelines for Hive Sources on the Blaze Engine
- Sqoop Sources on Hadoop
Targets
- Overview of Targets
- PowerExchange Adapter Targets
- Targets on Databricks
- File Targets on Hadoop
- Message Targets on Hadoop
- Relational Targets on Hadoop
- Hive Targets on Hadoop
- Sqoop Targets on Hadoop
  - Rules and Guidelines for Sqoop Targets
Transformations
- Overview of Transformations
- Address Validator Transformation in a Non-native Environment
  - Address Validator Transformation on the Blaze Engine
  - Address Validator Transformation on the Spark Engine
    - Address Validator Transformation in a Streaming Mapping
- Aggregator Transformation in a Non-native Environment
  - Aggregator Transformation on the Blaze Engine
  - Aggregator Transformation on the Spark Engine
    - Aggregator Transformation in a Streaming Mapping
  - Aggregator Transformation on the Databricks Spark Engine
- Case Converter Transformation in a Non-native Environment
- Classifier Transformation in a Non-native Environment
- Comparison Transformation in a Non-native Environment
- Consolidation Transformation in a Non-native Environment
  - Consolidation Transformation on the Blaze Engine
  - Consolidation Transformation on the Spark Engine
- Data Masking Transformation in a Non-native Environment
  - Data Masking Transformation on the Blaze Engine
  - Data Masking Transformation on the Spark Engine
    - Data Masking Transformation in a Streaming Mapping
- Data Processor Transformation in a Non-native Environment
- Decision Transformation in a Non-native Environment
  - Decision Transformation on the Spark Engine
- Expression Transformation in a Non-native Environment
  - Expression Transformation on the Blaze Engine
  - Expression Transformation on the Spark Engine
    - Expression Transformation in a Streaming Mapping
  - Expression Transformation on the Databricks Spark Engine
- Filter Transformation in a Non-native Environment
  - Filter Transformation on the Blaze Engine
- Hierarchical to Relational Transformation in a Non-native Environment
- Java Transformation in a Non-native Environment
  - Java Transformation on the Blaze Engine
  - Java Transformation on the Spark Engine
    - Java Transformation in a Streaming Mapping
- Joiner Transformation in a Non-native Environment
  - Joiner Transformation on the Blaze Engine
  - Joiner Transformation on the Spark Engine
    - Joiner Transformation in a Streaming Mapping
  - Joiner Transformation on the Databricks Spark Engine
- Key Generator Transformation in a Non-native Environment
- Labeler Transformation in a Non-native Environment
- Lookup Transformation in a Non-native Environment
  - Lookup Transformation on the Blaze Engine
  - Lookup Transformation on the Spark Engine
    - Lookup Transformation in a Streaming Mapping
  - Lookup Transformation on the Databricks Spark Engine
- Match Transformation in a Non-native Environment
  - Match Transformation on the Blaze Engine
  - Match Transformation on the Spark Engine
- Merge Transformation in a Non-native Environment
- Normalizer Transformation in a Non-native Environment
- Parser Transformation in a Non-native Environment
- Rank Transformation in a Non-native Environment
  - Rank Transformation on the Blaze Engine
  - Rank Transformation on the Spark Engine
    - Rank Transformation in a Streaming Mapping
  - Rank Transformation on the Databricks Spark Engine
- Relational to Hierarchical Transformation in a Non-native Environment
- Router Transformation in a Non-native Environment
- Sequence Generator Transformation in a Non-native Environment
  - Sequence Generator Transformation on the Blaze Engine
  - Sequence Generator Transformation on the Spark Engine
- Sorter Transformation in a Non-native Environment
  - Sorter Transformation on the Blaze Engine
  - Sorter Transformation on the Spark Engine
    - Sorter Transformation in a Streaming Mapping
  - Sorter Transformation on the Databricks Spark Engine
- Standardizer Transformation in a Non-native Environment
- Union Transformation in a Non-native Environment
  - Union Transformation in a Streaming Mapping
- Update Strategy Transformation in a Non-native Environment
  - Update Strategy Transformation on the Blaze Engine
  - Update Strategy Transformation on the Spark Engine
  - Update Strategy Transformation on the Databricks Spark Engine
- Weighted Average Transformation in a Non-native Environment
Python Transformation
- Python Transformation Overview
- Python Transformation Ports
- Python Transformation Advanced Properties
- Python Transformation Components
  - Resource File
  - Python Code
- Rules and Guidelines for the Python Transformation
  - Python Transformation in a Streaming Mapping
- Creating a Python Transformation
  - Creating a Reusable Python Transformation
  - Creating a Non-Reusable Python Transformation
- Example: Add an ID Column to Nonpartitioned Data
- Example: Use Partitions to Find the Highest Salary
- Use Case: Operationalize a Pre-Trained Model
Data Preview
- Overview of Data Preview
  - Connections and Cluster Distributions that Support Data Preview
- Data Preview Process
- Previewing Data
- Data Preview Interface for Hierarchical Data
- Data Preview on Transformations
- Data Preview Logs
- Rules and Guidelines for Data Preview on the Spark Engine
Cluster Workflows
- Cluster Workflows Overview
  - Cluster Workflows Platform Support
- Cluster Workflow Components
- Cluster Workflows Process
- Create Cluster Task Properties
- Mapping Task Properties
- Add a Delete Cluster Task
- Deploy and Run the Workflow
  - Monitoring Azure HDInsight Cluster Workflow Jobs
Profiles
- Profiles Overview
- Native Environment
- Hadoop Environment
  - Column Profiles for Sqoop Data Sources
- Sampling Options
- Creating a Single Data Object Profile in Informatica Developer
- Creating an Enterprise Discovery Profile in Informatica Developer
- Creating a Column Profile in Informatica Analyst
- Creating an Enterprise Discovery Profile in Informatica Analyst
- Creating a Scorecard in Informatica Analyst
- Monitoring a Profile
- Profiling Functionality Support
- Troubleshooting
Monitoring
- Overview of Monitoring
- Hadoop Environment Logs
- Blaze Engine Monitoring
- Spark Engine Monitoring
Hierarchical Data Processing
- Overview of Hierarchical Data Processing
- How to Develop a Mapping to Process Hierarchical Data
- Complex Data Types
- Complex Ports
- Complex Data Type Definitions
- Type Configuration
- Complex Operators
  - Extracting an Array Element Using a Subscript Operator
  - Extracting a Struct Element Using the Dot Operator
- Complex Functions
- Midstream Parsing of Hierarchical Data
Hierarchical Data Processing Configuration
- Hierarchical Data Conversion
- Convert Relational or Hierarchical Data to Struct Data
  - Creating a Struct Port
- Convert Relational or Hierarchical Data to Nested Struct Data
  - Creating A Nested Complex Port
- Extract Elements from Hierarchical Data
  - Extracting Elements from a Complex Port
- Flatten Hierarchical Data
  - Flattening a Complex Port
Hierarchical Data Processing with Schema Changes
- Overview of Hierarchical Data Processing with Schema Changes
- How to Develop a Dynamic Mapping to Process Schema Changes in Hierarchical Data
- Dynamic Complex Ports
  - Dynamic Ports and Dynamic Complex Ports
  - Dynamic Complex Ports in Transformations
- Input Rules for a Dynamic Complex Port
- Port Selectors for Dynamic Complex Ports
- Dynamic Expressions
  - Example - Dynamic Expression to Construct a Dynamic Struct
- Complex Operators
- Complex Functions
- Rules and Guidelines for Dynamic Complex Ports
- Optimized Mappings
Intelligent Structure Models
- Overview of Intelligent Structure Models
- Intelligent Structure Discovery Process
- Use Case
- Using an Intelligent Structure Model in a Mapping
- Rules and Guidelines for Intelligent Structure Models
- How to Develop and Run a Mapping to Process Data with an Intelligent Structure Model
  - Mapping Example
- Create an Intelligent Structure Model in Cloud Data Integration
Blockchain
- Blockchain Overview
  - Blockchain Process
- Blockchain Data Objects
- Blockchain Data Object Operations
- Use Case: Using a Blockchain Source to Improve Services in a Vehicle Lifecycle
  - Mapping Overview
Stateful Computing
- Overview of Stateful Computing
- Windowing Configuration
- Window Functions
- Windowing Examples
Appendix A: Connections Reference
- Connections Overview
- Cloud Provisioning Configuration
  - AWS Cloud Provisioning Configuration Properties
  - Azure Cloud Provisioning Configuration Properties
  - Databricks Cloud Provisioning Configuration Properties
- Amazon Redshift Connection Properties
- Amazon S3 Connection Properties
- Blockchain Connection Properties
- Cassandra Connection Properties
- Databricks Connection Properties
- Google Analytics Connection Properties
- Google BigQuery Connection Properties
- Google Cloud Spanner Connection Properties
- Google Cloud Storage Connection Properties
- Hadoop Connection Properties
  - Hadoop Cluster Properties
  - Common Properties
  - Reject Directory Properties
  - Blaze Configuration
  - Spark Configuration
- HDFS Connection Properties
- HBase Connection Properties
- HBase Connection Properties for MapR-DB
- Hive Connection Properties
- JDBC Connection Properties
  - JDBC Connection String
  - Sqoop Connection-Level Arguments
- JDBC V2 Connection Properties
- Kafka Connection Properties
- Microsoft Azure Blob Storage Connection Properties
- Microsoft Azure Cosmos DB SQL API Connection Properties
- Microsoft Azure Data Lake Storage Gen1 Connection Properties
- Microsoft Azure Data Lake Storage Gen2 Connection Properties
- Microsoft Azure SQL Data Warehouse Connection Properties
- Snowflake Connection Properties
- Creating a Connection to Access Sources or Targets
- Creating a Hadoop Connection
- Configuring Hadoop Connection Properties
  - Cluster Environment Variables
  - Cluster Library Path
  - Common Advanced Properties
  - Blaze Engine Advanced Properties
  - Spark Advanced Properties
Appendix B: Data Type Reference
- Data Type Reference Overview
- Transformation Data Type Support in a Non-native Environment
- Complex File and Transformation Data Types
- Hive Data Types and Transformation Data Types
  - Hive Complex Data Types
- Sqoop Data Types
Appendix C: Function Reference
- Function Support in a Non-native Environment
- Function and Data Type Processing

User Guide

10.4.0
- 10.5.7
- 10.5.6
- 10.5.5
- 10.5.4
- 10.5.3
- 10.5.2
- 10.5.1
- 10.5
- 10.4.1
- 10.2.2 HotFix 1
- 10.2.2 Service Pack 1
- 10.2.2
- 10.2.1

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JDBC Connection Properties

You can use a JDBC connection to access tables in a database. You can create and manage a JDBC connection in the Administrator tool, the Developer tool, or the Analyst tool.

The order of the connection properties might vary depending on the tool where you view them.

The following table describes JDBC connection properties:

Property	Description
Database Type	The database type.
Name	Name of the connection. The name is not case sensitive and must be unique within the domain. The name cannot exceed 128 characters, contain spaces, or contain the following special characters: ~ ` ! $ % ^ & * ( ) - + = { [ } ] \| \ : ; " ' < , > . ? /
ID	String that the Data Integration Service uses to identify the connection. The ID is not case sensitive. It must be 255 characters or less and must be unique in the domain. You cannot change this property after you create the connection. Default value is the connection name.
Description	The description of the connection. The description cannot exceed 765 characters.
User Name	The database user name.
Password	The password for the database user name.
JDBC Driver Class Name	Name of the JDBC driver class. The following list provides the driver class name that you can enter for the applicable database type: DataDirect JDBC driver class name for Oracle: com.informatica.jdbc.oracle.OracleDriver DataDirect JDBC driver class name for IBM DB2: com.informatica.jdbc.db2.DB2Driver DataDirect JDBC driver class name for Microsoft SQL Server: com.informatica.jdbc.sqlserver.SQLServerDriver DataDirect JDBC driver class name for Sybase ASE: com.informatica.jdbc.sybase.SybaseDriver DataDirect JDBC driver class name for Informix: com.informatica.jdbc.informix.InformixDriver DataDirect JDBC driver class name for MySQL: com.informatica.jdbc.mysql.MySQLDriver JDBC driver for Databricks Delta Lake: the name of the driver that you downloaded from Databricks. For information about the driver, see the topic on configuring storage access in the "Before You Begin Databricks Integration" chapter of the Data Engineering Integration Guide . For more information about which driver class to use with specific databases, see the vendor documentation.
Connection String	Connection string to connect to the database. Use the following connection string: jdbc:<subprotocol>:<subname> For more information about the connection string to use with specific drivers, see the vendor documentation.
Environment SQL	Optional. Enter SQL commands to set the database environment when you connect to the database. The Data Integration Service executes the connection environment SQL each time it connects to the database. If you enable Sqoop, Sqoop ignores this property.
Transaction SQL	Optional. Enter SQL commands to set the database environment when you connect to the database. The Data Integration Service executes the transaction environment SQL at the beginning of each transaction. If you enable Sqoop, Sqoop ignores this property.
SQL Identifier Character	Type of character that the database uses to enclose delimited identifiers in SQL queries. The available characters depend on the database type. Select (None) if the database uses regular identifiers. When the Data Integration Service generates SQL queries, the service does not place delimited characters around any identifiers. Select a character if the database uses delimited identifiers. When the Data Integration Service generates SQL queries, the service encloses delimited identifiers within this character. If you enable Sqoop, Sqoop ignores this property.
Support Mixed-case Identifiers	Enable if the database uses case-sensitive identifiers. When enabled, the Data Integration Service encloses all identifiers within the character selected for the SQL Identifier Character property. When the SQL Identifier Character property is set to none, the Support Mixed-case Identifiers property is disabled. If you enable Sqoop, Sqoop honors this property when you generate and execute a DDL script to create or replace a target at run time. In all other scenarios, Sqoop ignores this property.
Use Sqoop Connector	Enables Sqoop connectivity for the data object that uses the JDBC connection. The Data Integration Service runs the mapping in the Hadoop run-time environment through Sqoop. You can configure Sqoop connectivity for relational data objects, customized data objects, and logical data objects that are based on a JDBC-compliant database. Select Sqoop v1.x to enable Sqoop connectivity. Default is None .
Sqoop Arguments	Enter the arguments that Sqoop must use to connect to the database. Separate multiple arguments with a space. To run the mapping on the Blaze engine with the Teradata Connector for Hadoop (TDCH) specialized connectors for Sqoop, you must define the TDCH connection factory class in the Sqoop arguments. The connection factory class varies based on the TDCH Sqoop Connector that you want to use. To use Cloudera Connector Powered by Teradata, configure the following Sqoop argument: -Dsqoop.connection.factories=com.cloudera.connector.teradata.TeradataManagerFactory To use Hortonworks Connector for Teradata (powered by the Teradata Connector for Hadoop), configure the following Sqoop argument: -Dsqoop.connection.factories=org.apache.sqoop.teradata.TeradataManagerFactory To run the mapping on the Spark engine, you do not need to define the TDCH connection factory class in the Sqoop arguments. The Data Integration Service invokes the Cloudera Connector Powered by Teradata and Hortonworks Connector for Teradata (powered by the Teradata Connector for Hadoop) by default. To run the mapping with a generic JDBC connector instead of the specialized Cloudera or Hortonworks connector, you must define the --driver and --connection-manager Sqoop arguments in the JDBC connection. If you define the --driver and --connection-manager arguments in the Read or Write transformation of the mapping, Sqoop ignores the arguments. If you do not enter Sqoop arguments, the Data Integration Service constructs the Sqoop command based on the JDBC connection properties.