Support Changes

Technical Preview

Technical Preview Initiated

Data Flow Analytics

You can use Data Flow Analytics to view analytical data about PowerCenter mappings in the catalog. You can use the data to improve the mapping quality and efficiency in your enterprise. You can also view mappings as diagrams for ease of understanding.

The analytical data includes details about the following mapping metrics:

• - Similar mappings
• - Duplicate mappings
• - Reusable mapplets, transformations, and user functions for complex expressions

In addition, the data also includes the mapping group details and the mapping discovery details to provide additional insights into the mapping data. You can download the analytical data as reports for further analysis or enhance the reports using third-party reporting solutions.

For more information, see the Enterprise Data Catalog 10.5 User Guide.

Graphical Informatica Installer

You can use a graphical user interface based installer to install PowerCenter and Data Engineering Integration on Linux platforms. The installer provides an easy and interactive interface to run the Pre-installation (i10Pi) System Check Tool, install the Informatica domain, and configure the domain and application services.

Informatica Domain on Kubernetes Cluster

You can run Informatica domain, ISP nodes, and application services on a Microsoft Azure Kubernetes cluster. The auto-scaling of the active-active services such as Data Integration Service and PowerCenter Integration Service are supported.

For more information, see the Install PowerCenter on Kubernetes with Informatica Deployment Manager or the Install Data Engineering Integration on Kubernetes with Informatica Deployment Manager.

Intelligent structure models based on an XSD file

When you create an intelligent structure model you can select an XSD file to base the model on.

Mapping audits on the Blaze engine

The Blaze engine can run mappings with audit rules and conditions.

For more information, see the Data Engineering Integration 10.5 User Guide.

PowerExchange for Microsoft Azure Data Lake Storage Gen2

The following functionality is available for technical preview:

• - You can read objects stored in subdirectories in Microsoft Azure Data Lake Storage Gen2 in the native environment or on the Spark engine.
• - You can use wildcard characters to specify the source directory name or the source file name when you read data from a flat file or complex file in the native environment or on the Spark engine.

Technical Preview Lifted

Catalog Backup

For Enterprise Data Catalog, you can back up the catalog without disabling the Catalog Service. You can back up the catalog using the REST APIs, the Enterprise Data Catalog interface, or the Catalog Administrator. For more information, see the Catalog Backup and Log Collection Utilities section in the Informatica 10.5 Catalog Administrator Guide or the Informatica 10.5 Enterprise Data Catalog User Guide.

Cloudera CDP integration

For Data Engineering Integration, you can use Cloudera Data Platform (CDP) as a compute cluster to execute data engineering jobs in the Hadoop environment. You can use Cloudera CDP when you run data engineering jobs on the Spark engine. Cloudera CDP uses a base cluster and workload clusters to execute data engineering jobs. This architecture allows you to deploy workloads and share data among components by utilizing a shared catalog, unified security, consistent governance, and data life cycle management.

You can use Cloudera CDP when you run a mapping in the Hadoop environment with the following connections:

• - PowerExchange for Google Cloud Storage
• - PowerExchange for Google BigQuery
• - PowerExchange for Google Analytics
• - PowerExchange for Google Cloud Spanner

For more information, see the Data Engineering 10.5 Integration Guide.

Compact view

You can choose to display the compact view of the lineage and Impact view. The compact lineage and impact view displays the lineage and impact diagram summarized at the resource level.

For more information, see the "View Lineage and Impact" chapter in the Informatica 10.5 Enterprise Data Catalog User Guide.

Rollover parameters for Amazon S3 and Azure Data Lake Storage Gen2 targets

For Data Engineering Streaming, you can use different rollover parameters for Amazon S3 and Azure Data Lake Storage Gen2 targets to decide the rollover time or size for each target. For more information, see Data Engineering Streaming 10.5 User Guide.

SAP S/4HANA resource

For Enterprise Data Catalog, you can extract metadata from the SAP S/4HANA data source. For more information, see the Enterprise Data Catalog 10.5 Scanner Configuration Guide.

Service and Resource Log Collection

For Enterprise Data Catalog, you can collect logs for resources. Additionally, you can collect logs for the following services:

• - Enterprise Data Catalog services such as Catalog Service, Informatica Cluster Service, EBF history, and PhoneHome
• - Apache Solr
• - MongoDB
• - Nomad orchestration service and scan jobs
• - Apache Zookeeper
• - Postgres

You can collect the logs using the REST APIs, the Enterprise Data Catalog interface, or the Catalog Administrator. For more information, see the Catalog Backup and Log Collection Utilities section in the Informatica 10.5 Catalog Administrator Guide or the Informatica 10.5 Enterprise Data Catalog User Guide.

Unique Key Inference

You can generate unique key candidates from the columns in the source data object. You can view the unique key inference results to understand data quality issues by identifying the columns that have duplicate values. You can accept or reject the inferred unique key inference results. After you accept or reject an inferred unique key inference, you can reset the unique key inference to restore the inferred status.

For more information, see the "View Assets" chapter in the Informatica 10.4.0 Enterprise Data Catalog User Guide.

Deprecation

Enterprise Data Catalog Installation on Hadoop Clusters

Effective in version 10.5, you cannot install Enterprise Data Catalog on a Hadoop cluster.

For more information, see the Informatica 10.5 Installation for Enterprise Data Catalog.

Extract Data Lineage at the Column Level

Effective in version 10.5, the functionality to extract data lineage at the column level for stored procedures in Oracle and SQL Server is deprecated and will be removed in a future release of Enterprise Data Catalog.

Informatica recommends that you use Advanced Scanners to extract data lineage at the column level for stored procedures in Oracle and SQL Server.

For more information, see the Enterprise Data Catalog Advanced Scanners Administrator Guide.

Extract Metadata for Data Lineage at the Column Level

Effective in version 10.5, the functionality to extract metadata for data lineage at the column level including transformation logic from the following data sources is deprecated and will be removed in a future release of Enterprise Data Catalog.

• - Oracle Data Integrator data source
• - IBM InfoSphere DataStage data source

Informatica recommends that you use Advanced Scanners to extract metadata for data lineage at the column level for Oracle Data Integrator data source and IBM InfoSphere DataStage data sources.

For more information, see the Enterprise Data Catalog Advanced Scanners Administrator Guide.

compareMapping DIS Command

Effective in version 10.5, the infacmd dis compareMapping is deprecated and will be removed in a future release.

If you are using scripts based on infacmd dis compareMapping, Informatica recommends that you update the scripts with the new infacmd dis compareObject command. Deprecated functionality is supported, but Informatica intends to drop support in a future release. Informatica requests that you to transition to different functionality before the functionality is dropped.

Universal Connectivity Framework

Effective in version 10.5, support for Universal Connectivity Framework is deprecated. For additional information, contact Informatica Global Customer Support.

Deferment

• •AIX
• •Cloudera CDH 6.3