Network
Data Engineering
Data Engineering Integration
Enterprise Data Catalog
Enterprise Data Preparation
Cloud Integration
Cloud Application Integration
Cloud Data Integration
Cloud Customer 360
DiscoveryIQ
Cloud Data Wizard
Informatica for AWS
Informatica for Microsoft
Cloud Integration Hub
Complex Event Processing
Proactive Healthcare Decision Management
Proactive Monitoring
Real-Time Alert Manager
Rule Point
Data Integration
B2B Data Exchange
B2B Data Transformation
Data Integration Hub
Data Replication
Data Services
Data Validation Option
Fast Clone
Informatica Platform
Metadata Manager
PowerCenter
PowerCenter Express
PowerExchange
PowerExchange Adapters
Data Quality
Axon Data Governance
Data as a Service
Data Explorer
Data Quality
Data Security Group (Formerly ILM)
Data Archive
Data Centric Security
Secure@Source
Secure Testing
Master Data Management
Identity Resolution
MDM - Relate 360
Multidomain MDM
MDM Registry Edition
Process Automation
ActiveVOS
Process Automation
Product Information Management
Informatica Procurement
MDM - Product 360
Ultra Messaging
Ultra Messaging Options
Ultra Messaging Persistence Edition
Ultra Messaging Queuing Edition
Ultra Messaging Streaming Edition
Edge Data Streaming
Knowledge Base
Resources
PAM (Product Availability Matrices)
Support TV
Velocity (Best Practices)
Mapping Templates
Debugging Tools
User Groups
Documentation
English
English
English
Español
Spanish
Deutsch
German
Français
French
日本語
Japanese
한국어
Korean
Português
Portuguese
中文
Chinese
Log Out
Log In
Sign Up
Data Engineering Integration
10.1
H2L
10.4.1
10.4.0
10.2.2 HotFix 1
10.2.2 Service Pack 1
10.2.2
10.2.1
10.2 HotFix 2
10.2 HotFix 1
10.2
10.1.1 Update 2
10.1.1 HotFix 1
10.1.1
10.1
10.0 Update 1
10.0
User Guide
Data Engineering Integration
All Products
Table of Contents
Search
No Results
Preface
Introduction to Informatica Big Data Management
Informatica Big Data Management Overview
Example
Big Data Management Tasks
Read from and Write to Big Data Sources and Targets
Perform Data Discovery
Perform Data Lineage on Big Data Sources
Stream Machine Data
Manage Big Data Relationships
Big Data Management Component Architecture
Clients and Tools
Application Services
Repositories
Hadoop Environment
Hadoop Utilities
Big Data Management Engines
Blaze Engine Architecture
Spark Engine Architecture
Hive Engine Architecture
Big Data Process
Step 1. Collect the Data
Step 2. Cleanse the Data
Step 3. Transform the Data
Step 4. Process the Data
Step 5. Monitor Jobs
Connections
Connections
Hadoop Connection Properties
HDFS Connection Properties
HBase Connection Properties
Hive Connection Properties
JDBC Connection Properties
Sqoop Connection-Level Arguments
Creating a Connection to Access Sources or Targets
Creating a Hadoop Connection
Mappings in a Hadoop Environment
Mappings in a Hadoop Environment Overview
Mapping Run-time Properties
Validation Environments
Execution Environment
Data Warehouse Optimization Mapping Example
Sqoop Mappings in a Hadoop Environment
Sqoop Mapping-Level Arguments
Configuring Sqoop Properties in the Mapping
Rules and Guidelines for Mappings in a Hadoop Environment
Workflows that Run Mappings in a Hadoop Environment
Configuring a Mapping to Run in a Hadoop Environment
Mapping Execution Plans
Blaze Engine Execution Plan Details
Spark Engine Execution Plan Details
Hive Engine Execution Plan Details
Viewing the Execution Plan for a Mapping in the Developer Tool
Monitor Jobs
Accessing the Monitoring URL
Monitor Blaze Engine Jobs
Monitoring a Mapping
Hadoop Environment Logs
YARN Web User Interface
Blaze Engine Logs
Viewing Logs in the Blaze Job Monitor
Spark Engine Logs
Viewing Spark Logs
Hive Engine Logs
Viewing Hadoop Environment Logs in the Administrator Tool
Optimization for the Hadoop Environment
Blaze Engine High Availability
Truncating Partitions in a Hive Target
Enabling Data Compression on Temporary Staging Tables
Step 1. Configure the Hive Connection to Enable Data Compression on Temporary Staging Tables
Step 2. Configure the Hadoop Cluster to Enable Compression on Temporary Staging Tables
Parallel Sorting
Troubleshooting a Mapping in a Hadoop Environment
Mapping Objects in a Hadoop Environment
Sources in a Hadoop Environment
Flat File Sources
Hive Sources
Complex File Sources
Relational Sources
Sqoop Sources
Targets in a Hadoop Environment
Flat File Targets
HDFS Flat File Targets
Hive Targets
Complex File Targets
Relational Targets
Sqoop Targets
Transformations in a Hadoop Environment
Transformation Support on the Blaze Engine
Transformation Support on the Spark Engine
Transformation Support on the Hive Engine
Function and Data Type Processing
Rules and Guidelines for Spark Engine Processing
Rules and Guidelines for Hive Engine Processing
Mappings in the Native Environment
Mappings in the Native Environment Overview
Data Processor Mappings
HDFS Mappings
HDFS Data Extraction Mapping Example
Hive Mappings
Hive Mapping Example
Social Media Mappings
Twitter Mapping Example
Profiles
Profiles Overview
Native Environment
Hadoop Environment
Column Profiles for Sqoop Data Sources
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
Troubleshooting
Native Environment Optimization
Native Environment Optimization Overview
Processing Big Data on a Grid
Data Integration Service Grid
Grid Optimization
Processing Big Data on Partitions
Partitioned Model Repository Mappings
Partition Optimization
High Availability
Data Type Reference
Data Type Reference Overview
Transformation Data Type Support in a Hadoop Environment
Hive Data Types and Transformation Data Types
Hive Complex Data Types
Sqoop Data Types
Function Reference
Function Support in a Hadoop Environment
Parameter Reference
Parameters Overview
Parameter Usage
User Guide
User Guide
10.1
10.4.1
10.4.0
10.2.2 HotFix 1
10.2.2 Service Pack 1
10.2.2
10.2.1
10.2 HotFix 1
10.2
10.1.1 Update 2
10.1.1 HotFix 1
10.1.1
10.0
Back
Next
Hive Mappings
Hive Mappings
Based on the mapping environment, you can read data from or write data to Hive.
In a native environment, you can read data from Hive. To read data from Hive, complete the following steps:
Create a Hive connection.
Configure the Hive connection mode to access Hive as a source or target.
Use the Hive connection to create a data object to read from Hive.
Add the data object to a mapping and configure the mapping to run in the native environment.
You can write to Hive in a Hadoop environment. To write data to Hive, complete the following steps:
Create a Hive connection.
Configure the Hive connection mode to access Hive as a source or target.
Use the Hive connection to create a data object to write to Hive.
Add the data object to a mapping and configure the mapping to run in the Hadoop environment.
You can define the following types of objects in a Hive mapping:
A read data object to read data from Hive
Transformations
A target or an SQL data service. You can write to Hive if you run the mapping in a Hadoop cluster.
Validate and run the mapping. You can deploy the mapping and run it or add the mapping to a Mapping task in a workflow.
Mappings in the Native Environment
Hive Mapping Example
Updated July 03, 2018
Download Guide
Send Feedback
Explore Informatica Network
Communities
Knowledge Base
Success Portal
Back to Top
Back
Next