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What are we announcing?

Informatica Big Data Release 10.2.2


Who would benefit from this release?

This release is for all customers and prospects who want to take advantage of the latest Big Data Management, Big Data Quality, Big Data Streaming, Enterprise Data Catalog, and Enterprise Data Lake capabilities.


What’s in this release?

This update provides the latest ecosystem support, security, connectivity, cloud, and performance while improving the end-to-end user experience.


Big Data Management (BDM)


Enterprise Class

  • Zero client configuration: Developers can now import the metadata from Hadoop clusters without configuring Kerberos Keytabs and configuration files on individual workstations by leveraging the Metadata Access Service. Metadata Access Service now supports OS Profiles (when enabled) and can be executed on multiple nodes as a GRID
  • Mass ingestion: Data analysts can now ingest relational data into HDFS and Hive for both initial and incremental loads. Mass Ingestion service can now fetch incremental data based on date columns or numeric columns, persist the last values fetched in the previous run and automatically use them in the subsequent runs.
  • SQOOP enhancements: SQOOP connector now supports high levels of concurrency and the ability to fetch incremental data
  • Bitbucket Support: Big Data Management administrators can now configure BitBucket (in addition to Perforce, SVN, and Git) as the external versioning repository
  • Go-Live assistance: Release managers can now incrementally deploy objects into applications instead of overwriting the entire applications
  • Robustness & Concurrency: Data Integration Service is now highly robust can process 6 times more concurrent requests than it did in previous releases. The startup time of the Data Integration Service is improved by 2x.
  • Resilience: The Data Integration Service can now automatically reparent to the jobs that continue to run on the Hadoop clusters - even after the Data Integration Service experiences a crash or unexpected restart
  • Queuing: Data Integration Service is now enhanced to queue the jobs submitted to it and persists the requests so that the requests do not have to be resubmitted in case the Data Integration Service experiences a crash or unexpected restart
  • REST Operations Hub: Operations teams can now perform REST queries that fetch the job status, row level statistics and other monitoring information for deployed mappings
  • Dynamic mappings: Dynamic mappings can now be used across various data types and various ecosystems including AWS and Azure 

Advanced Spark

  • Advanced Data Integration: Spark now supports high precision decimals and executes the Python transformation many times faster than previous releases
  • Dynamic mappings: Complex data types such as Arrays, Structs and Maps can now be used in dynamic mappings
  • Debugging made easy: With the introduction of automatic Spark-based data preview, developers can now debug advanced spark mappings that contain complex types and stateful functions as easily as they preview the native mappings
  • CLAIRE integration: Big Data Management now integrates with Intelligent Structure Discovery (that is part of Informatica Intelligent Cloud Services) to provide machine learning capabilities in parsing the complex file formats such as Weblogs

Cloud and Connectivity

  • Core connectivity:
    • SQOOP connector is optimized to run faster and is designed to eliminate staging wherever possible
    • HBase sources and targets can be used with dynamic mappings
    • Schema drift is now supported. Changes in the source systems can now be applied on to the Hive targets
    • Data can now be loaded to Hive in Native mode
  • Amazon ecosystem:
    • Developer productivity is increased with the ability to use S3 and Redshift sources and targets in dynamic mappings
    • S3 data objects now support wild card characters in the file names.
    • File names can be dynamically generated for S3 objects using the target based FileName port
    • Many additional properties in the Redshift data objects can now be parameterized.
  • Azure ecosystem:
    • Developer productivity is increased manifold with the ability to use ADLS and WASB sources and targets in dynamic mappings
    • Intelligent Structure Discovery is now supported with Azure ADLS and WASB
  • Azure Databricks: BDM now offers support for managed cluster computation on Azure Databricks
  • Containerization: Implementation teams can now build docker containers of BDM images and deploy them per their enterprise needs


Platform PAM Update


  • Operating System Update:
    • RHEL - 7.3 & 6.7 - Added
    • RHEL - 7.0 , 7.1 ,7.2 and 6.5 , 6.6 - Dropped
    • SUSE 12 SP2 - Added
    • SUSE 12 SP0 & SP1 -  Dropped
    • SUSE 11 SP4 - Added
    • SUSE 11 SP2 & SP3 - Dropped
  • Database support:
    • Azure SQL DB  - Added
  • Authentication Support:
    • Windows 2012 R2 & 2016 (LDAP and Kerberos) - Added
    • Azure Active Directory (LDAP only) - Added
  • Tomcat Support:
    • v 7.0.88 (No update)
  • JVM Support Update:
    • Azul OpenJDK 1.8.0_192 - Added
    • Oracle Java - Removed
    • Effective Informatica 10.2.2, Informatica platform supports Azul OpenJDK, instead of Oracle Java since Oracle has changed its Java licensing policy, ending public updates for Java 8 effective January 2019. Azul OpenJDK would come bundled with the product.
  • Model Repository - Versioned Controlled
    • BitBucket Server 5.16 Linux (hosting service for repositories) - Added
    • Perforce - 2017.2 - updated
    • Visual SVN - 3.9 - updated
    • Collabnet Subversion Edge - 5.2.2 - updated
  • Others
    • Microsoft Edge Browser (Win 10) 40.15 - updated
    • Internet Explorer -11.x - updated
    • Google Chrome- 68.0.x - updated
    • Safari - 11.1.2 ( MacOS 10.13 High Sierra) - updated
    • Adobe Flash Player  - 27.x - updated

Informatica Docker Utility

  • Use the Informatica Docker Utility to create a custom Docker Container images for Big Data Management and then run the Docker Container image to create an Informatica Domain. The Informatica Docker utility provides a quick and easy process to install the Informatica Domain in Docker Containers.


Big Data Streaming (BDS)


Enterprise Class Streaming Data Integration

  • CLAIRE integration: Big Data Streaming now integrates with Intelligent Structure Discovery (that is part of Informatica Intelligent Cloud Services) to provide machine learning capabilities in parsing the complex file formats and support dynamically evolving schemas
  • Resilience: Data Integration Service can now automatically reparent to the jobs that continue to run on the Hadoop clusters - even after DIS experiences a crash or unexpected restart
  • Queuing: The Data Integration Service is now enhanced to queue the jobs submitted to it and persist the requests so that the requests do not have to be resubmitted in cases the Data Integration Service experiences a crash or unexpected restart
  • Incremental Deployment: Release managers can now incrementally deploy objects into applications instead of overwriting the entire applications
  • Latest Spark Version support: Big Data Streaming now supports Spark 2.3.1
  • Java Transformation support on HType Data


Advanced Cloud Support

  • Amazon ecosystem:
    • Profile based authentication support for AWS Kinesis service
    • Cross Account authentication support for AWS Kinesis Streams service
    • Support for secure EMR cluster with Kerberos authentication
  • Azure ecosystem:
    • Support for deploying streaming mappings with an Azure EventHub source in Azure cloud with an HDInsight cluster


Enhanced Streaming Data Processing and Analytics

  • Spark Structured Streaming support
    • Big Data Streaming now supports processing based on event time
    • Big Data Streaming can now integrate “out of order” data and process it in the same order as it was generated at the source
  • Message Header support
    • Better streaming data processing based on message metadata
    • Supports header metadata-based analytics without parsing the complete message
  • Machine Learning and Advanced Analytics support
    • Supports execution of Python script in streaming mapping with improved performance
  • Latest Apache Kafka version support
    • Big Data Streaming now supports Kafka 2.0


Intelligent Structure Discovery


Intelligent Structure Discovery is now integrated with Big Data Management and Big Data Streaming on Spark to allow high performance parsing of various file types with data drift handling.

  • Performance enhancements
    • Improved runtime performance for some use cases (JSON and XML) by up to 10X compared to the previous release
  • Improved handling of Data Drift with an Unassigned Port
    • Data not identified by the model will be routed to the unassigned port and not dropped
  • Data Type propagation
    • Intelligent Structure Discovery automatically discovers the model field names data types. When importing the model to the platform these Identified fields are propagated to the transformation with the corresponding data type
  • Handling of “Special” Characters
    • Intelligent Structure Discovery Models that contain characters that do not comply with the platform naming convention are automatically replaced with a compliant character
  • Enhanced Parsing Engine
    • Improved handling of XML files (Attributes and namespaces).
    • Support Discovery and parsing of multiple sheet Excel files
  • Improved Design Time
    • The Intelligent Cloud Design is enhanced with a Find functionality and the ability to apply actions on multiple elements (for example rename)


Enterprise Data Lake (EDL)


Core Data Preparation


  • New Advanced Data Preparation functions: Users can utilizemore than 50 new advanced data preparation functions for Statistical, Text, Math, Date/Time manipulations. Window functions help with calculation on a data window such as Rolling Average, Rolling Sum, Lead/Lag, Fill, Sessionize etc. Cluster and Categorize function uses phonetic algorithms to cluster data and then help users easily standardize. Delete duplicate rows function helps removing exact duplicates from the data.
  • Apply Active Rules: Users can Apply external pre-built rules with Active Transformations to support DQ processing like Fuzzy Matching and Consolidation. Expert users can use Informatica Developer Tool to build complex rules including active transformation and then expose those to the data preparation users. This helps collaboration, standardization, extensibility and re-usability.
  • Data Preparation for Avro and Parquet files: Users can add Avro and Parquet files to a project in addition to Hive tables and other file formats such as delimited files and JSONL files. This eliminates the need for creating a Hive table on top of files. They can structure the hierarchical data in row-column form by extracting specific attributes from the hierarchy, and canexpanding (or exploding) arrays into rows in the worksheet.


Self-service and Collaboration

  • Functional and UX Improvements: Users can apply conditions during aggregation, reorder sheets as needed. Recipe panel clearly shows steps that failed during data and recipe refresh. The “Back-in-time” functionality is now more on-demand to improve user experience for Edit step, Copy/Paste step etc.
  • Improved CLAIRE based recommendations: Improved user productivity with additional CLAIRE based recommendations for alternate assets upstream and related by PK-FK. During join, users are prompted to review sampling criteria in case of low overlap of join keys. User also get new data prep suggestions based on data types that are handy shortcuts to frequently used functions.
  • Ability to add recipe comments: Users can add comments to various recipe steps, view comments by other users for better collaboration and auditing.
  • Save mappings for Recipes: Users have option to save mappings corresponding to recipes for a worksheet instead of executing full at-scale execution and creation of new output table. This way expert IT users can inspect the mapping and execute at the appropriate time and resource levels.


Enterprise Focus

  • Support for S3, ADLS/WASB and MapR-FS files: Users can prepare data files directly from various file systems such as AWS S3, Azure ADLS and WASB and also for MapR-FS in addition to HDFS.
  • Spark Execution: Spark is used as the default execution engine for better performance and it also allows data prep users apply executing rules built with mapplets using advanced transformations such as Python transforms .
  • Autoscaling on AWS EMR: Customers can start with minimal number of EMR nodes and then auto-scale based on rules for resource consumption to lower overall total cost of ownership for data lakes in AWS.
  • Integration with Informatica Dynamic Data Masking: Data protection and governance is improved using Informatica Dynamic Data Masking. Based on DDM policies, data will be masked at various touch points such as preview, prepare, publish and download etc.
  • Scalability improvements: Performance, scalability and longevity improvements have been made in various services to support enterprise scale deployments with large number of users.


Enterprise Data Catalog (EDC)


  • Collaboration: Data Analysts, Data Scientists and Line of Business users will now be able to find the most relevant, most trusted datasets for their analytic needs faster with Enterprise Data Catalog(EDC) v10.2.2. EDC 10.2.2 includes both top down and bottom up collaboration capabilities that bring to forefront the otherwise deeply siloed knowledge about trustworthiness and usefulness of datasets. This new capability will help data consumers save weeks, sometimes months of efforts in finding and using the right dataset.
    • Dataset Certifications: With EDC v10.2.2, Subject Matter Experts, Data Stewards and Data Owners will be able to certify datasets and data elements adding context information like data usage and constraints. Using EDC’s machine learning based semantic search, EDC will surface these certified datasets at the top of the search results to guide users to use these certified datasets among all other similarly named datasets in the organization.
    • Reviews and Ratings: Data consumers like Data Analysts and Data Scientists can now review and rate datasets. EDC pushes datasets that are rated highly to the top of the search results. There are new facets that are available to narrow down search results to highly rated datasets only.
    • Questions and Answers: Users will be able to use a new question/answer platform that allows subject matter experts to answer the most common questions of the data consumers. This will help data consumers to find experts, ask questions and see answers in the context of the dataset. For subject matter experts, this will mean less work and more reuse of information as they need not response to multiple emails and phone calls for the same queries on data.
  • Change Notifications: With change notifications, EDC will provide data consumers an easy way to stay on top of any metadata changes happening to their data assets. Users will be able to follow any datasets in the catalog and whenever scanners detect any changes to these datasets, both in-app and email notifications will be sent to the user. Additionally super users like database administrators, stewards and owners can follow entire databases and other metadata resources to get notified on any changes happening in the database.
  • Intelligent Business Glossary Associations: One of the most important and most tedious data governance tasks is to associate business glossary terms to physical data assets. In EDC v10.2.2 the glossary association process is a lot more easier. By using the CLAIRE based AI engine, the right business glossary terms are matched with the right physical assets at the data element level. This method uses the data domain discovery and data similarity capabilities to power automatic glossary associations with the goal of making the data stewards and business analysts responsible for this task about 2X more productive by providing these machine learning based assistants.
    • Business Glossary Assignment Report: EDC v10.2.2 includes a new business glossary assignment report at the resource level to help data stewards understand glossary association coverage for a resource in one place. Data Stewards will also be able to curate(accept/reject) all glossary recommendations from this new report as well.
  • Metadata and Profile Filters: Catalog and profile only selected metadata from databases, data warehouses and big data sources. Users will be able to provide both inclusion and exclusion criteria to filter datasets that are cataloged and profiled. The filter criteria can be a list of names or regular expressions that are matched against table/view names.
  • Remote Metadata Scanner: Catalog metadata from data sources that are behind a firewall or are remote with port restrictions. With EDC v10.2.2 a direct network connection from the Catalog to the data source is no longer required. Remote Metadata Scanner Utility can be downloaded and setup in a server close to the data source/in the same network and the extracted metadata can be uploaded to the catalog. Currently only metadata scan is supported for Oracle, SQL Server and Teradata.
  • New Scanners
    • Workday: Manage Workday metadata for governance, risk/compliance andself service analytics
    • Google BigQuery: Manage Google BigQuery metadata for governance, risk/compliance andself service analytics
  • Performance Improvements: EDC v10.2.2 includes a new graph schema that improves the performance of tasks like parameter assignment (63x faster), resource purge (5x faster) and re-index (2.5x faster). Additionally, there are all round scanner performance improvements in the areas of auto-connection assignments (340x faster), SAP Business Objects scanner (1.5x faster), Oracle scanner (2x faster) and IBM Cognos scanner (2x faster).


PAM for Informatica 10.2.2:


Informatica 10.2.2 Release Notes



Customers use Informatica Big Data Management (BDM) product to access metadata (using developer client tool) as well as data (through Data Integration Service) for Hadoop based sources (HDFS files, HBase, Hive, MapR-DB) as well as non-Hadoop sources. One of the major pain points with accessing Hadoop data sources is related to the non-trivial configuration effort that goes in configuring access to the Hadoop systems including the Kerberos configuration using kinit tool, keytab files etc. This document explains how Metadata Access Service simplifies the configuration effort and also enables more secure metadata access from Hadoop data sources.

Metadata access process without Metadata Access Service

The metadata access process before Metadata Access Service to import metadata from a Hadoop data source like HDFS files, HBase, Hive, MapR-DB included the following steps. Note that the below steps were needed to be performed on each developer client box installation that needed to be configured to access metadata from a Hadoop data source.

  1. Informatica developer needs to execute kinit command on the developer client box to get and cache the initial ticket-granting ticket from the KDC server. This requires providing appropriate keytab and krb5 configuration files to individual developers and asking them to execute the command manually before requesting metadata using the developer client. Since keytab files include sensitive information, distributing the same to each developer box appropriately and asking developers to execute these commands manually requires a lot of careful handling on the customer side.
  2. If the cluster is SSL enabled, developer needs to import the corresponding certificates in each developer client installation using keytool commands to import the certificates in the jre folder.
  3. Developer also needs to export the cluster configuration XML from Informatica Admin console and manually extract the zip file and place the same into the 'conf' folder under the appropriate Hadoop distribution folder on the developer client installation.
  4. Developer also needs to update the variable 'INFA_HADOOP_DIST_DIR' defined in 'developerCore.ini' file on each client box under the client installation if connecting to a Hadoop distribution other than the default Cloudera version.
  5. Finally, after performing the above steps (needed on each developer client box), developer can launch the import wizard for the data source in the developer client to import and save the metadata.

As apparent from the above steps, performing the above steps on each developer client box (customer may potentially have tens or even hundreds of developer client installations) is a big hassle. Metadata Access Service was introduced to ease the above configuration and also provide improved security architecture w.r.t metadata access for Hadoop data sources.


Metadata access using Metadata Access Service

Metadata Access Service is intended for enabling metadata access to Hadoop data sources(HDFS Files, HBase, Hive and, MapR-DB) from the developer client tool. This is a mandatory service that must be created before any metadata access from Hadoop sources listed can be performed via the developer tool. The service can be created using either Informatica admin console or through command line using infacmd tool.

Metadata Access Service needs to be configured only once by the Informatica Administrator (similar to how existing services like Data Integration Service used for data access by Informatica mappings). If there is a single metadata access service configured and enabled, it'd get picked up automatically by the developer client installations, else the default metadata access service can be selected once at the developer client level and the developer selection gets cached until changed. Metadata Access Service provides a lot of features and advantages compared to the accessing metadata directly from developer client tools.

  1. Metadata Access Service enables configuration of Kerberos specific attributes like keytab location, principal name at a single location.  There is no longer a need to run the kinit command on any developer client boxes since appropriate keytab file location and Service Principal Name can be provided in the MAS configuration in the Admin Console. This is also more secure as we no longer need to distribute and use sensitive information to developer boxes.
  2. Developer can configure multiple metadata access services either for load balancing purpose (to reduce the load on a single service process) or to connect a different Hadoop distribution type (Cloudera, Hortonworks, MapR etc) or a system with a different configuration (keytab, Service Principal Name) is desired. Hence, there is no need to perform steps like downloading cluster configuration files into localHadoop distribution folder on the developer client. Developer can select the appropriate Metadata Access Service name in the developer client as the default service for the current developer client session in case multiple services are configured (similar to how default DIS service is selected).
  3. Configuring access to SSL enabled clusters is also simplified as the SSL certificates for the clusters need to be imported (using keytool command) on a single node (where MAS is configured to run) and not on each developer client box.
  4. Developer can also enable the option to use 'logged in user as impersonation user' similar to DIS to enable current logged in user credentials in developer client box to be used while accessing any Hadoop resources.
  5. Support for centralized logging is enabled, hence, any metadata access related error message would also get captured and persisted in a centralized location (in addition to showing in a popup dialog box as earlier) just like other services like DIS and can be viewed (with features like filtering enabled) in Informatica Admin Console service log console in a similar manner. Without Metadata Access Service, the error messages were shown in a pop-up dialog only in developer tool but could not be retrieved later once the dialog box with an error message is closed by the developer.
  6. Metadata Access Service can be configured to interact with developer client tool over either http or more secure https protocol just like other Informatica services like DIS. Developer can configure the appropriate port (http/https) and keystore/truststore password/file locations (if https is enabled) as part of Metadata Access Service configuration through Admin Console or infacmd.
  7. Support for backup node is also enabled for high availability for Metadata Access Service. Hence, if the primary node where metadata access service runs goes down, the service should come back up on the backup node automatically.
  8. The local Hadoop distribution folders on the developer client boxes are now used during metadata access only if any metadata is accessed from the local file on the same box (where developer client box is installed) eg when a local avro or parquet file is used to import metadata without using a Hadoop File System connection object. Hence, the variable 'INFA_HADOOP_DIST_DIR' needs to be configured (or updated) only if metadata needs to be imported from a local file. In scenarios where a connection is used to import metadata, this variable is no longer required to be configured on the developer client side for metadata access. The size of Informatica Hadoop distribution folder on the developer client is also significantly reduced (by more than 1 GB) as most of the Hadoop distribution related jars/files are required to be deployed as part of only Informatica server-side installation.


'Metadata Access Service' provides a significant architectural improvement resulting in better security and easier configuration for Informatica Big Data Management client-side developer tool. This reduces the amount of time Informatica developers and administrator need to spend on configuring connectivity to Hadoop adapters like HDFS files, HBase, Hive and MapR-DB for importing metadata into the repository.


Sandeep Kathuria, Senior Staff Engineer

What are we announcing?

Informatica 10.2.1 Service Pack 1

Who would benefit from this release?

This release is for all Big Data Management and Big Data Quality customers and prospects who want to take advantage of the fixes to the Core Platform, Connectivity and installing new Service Pack, or upgrading from previous versions.

What’s in this release?

Big Data Management

  • This update provides the latest Service Pack improving the user experience. All big data customers are recommended to apply this Service Pack

Enterprise Data Catalog

  • This update provides bug fixes for functional and performance improvements. All Enterprise Data Catalog customers on 10.2.1 are recommended to apply this Service Pack.

Enterprise Data Lake

  • This update provides bug fixes for functional and performance improvements.
  • It also includes support for Spark engine execution and Autoscaling in AWS EMR deployments. 


Informatica 10.2.1 Service Pack 1 Big Data Release Notes


PAM for Informatica 10.2.1


Team Based Development refer to the capabilities in Informatica Big Data Management (BDM) that allow various developers to access, share, collaborate and reuse objects developed by others within the team. BDM has several capabilities that allow developers to work collectively without stepping on each other's work and without accidental overwrites.

Integration with version control system

Model Repository Service (MRS) can be integrated with any supported version control system such as GIT, SVN and Perforce. Model Repository completely abstracts all the complex version control operations to the Informatica developers. As the developers check-in / check-out objects, MRS seamlessly translates these into necessary operations for the underlying version control system. Integrating Model Repository Service with an external version control system is a single step process as demonstrated in the screenshot here:

BDM integration with external version control system such as GIT


When integrated with a version control system, MRS will preserve the latest version (current version) of an object in the model repository and all other versions in the external version control system.

Versioned objects

Developers can use the check-in objects into or check out objects out of the version control system. Developers can perform such operations on multiple objects at a time.

Versioned objects

Version History

All the historical versions stored in the external version control system can be directly accessed from the Developer tool itself. The View Version History menu opens the Version History pane.


Multiple Informatica developers can work and collaborate with each other. Developers can in parallel edit and operate on multiple related objects. For example, consider a mapping with a some reusable and non-reusable transformations. While a developer (developer-1) is editing the mapping, another user (developer-2) can edit the data object and a 3rd user (developer-3) can change / update the reusable transformations used within the mapping. Depending on the complexity of the mapping, multiple users can edit several components of it at the same time. Users can also edit Mapplet, Workflows and other related objects at the same time.

Collaboration in BDM


Administrators and other super users will be able to view edits in progress from the Administrator console - as described in the next section.

Collaboration Locks in Admin Console


Intent based object locking

BDM has in-built capability to acquire write locks for objects that the developers edit. A classic lock acquisition mechanism would acquire the write lock on the object as soon as user opens it in the workspace. While this eliminates the accidental overwrites, it is often an administration overhead when large teams are involved. A developer may just wish to have a read-only copy of a certain object open in their workspace as a reference to something they are working on. With globalization and developer teams spread across the oceans, acquiring a write lock for a user who opens an object makes collaboration a nightmare for developers. BDM uses intent based object locking to provide a more seamless collaboration experience. BDM acquires a write lock on the objects that on the first attempt to edit an object. This way many users can have the object open in the workspace and not interfere with the active users. Intent based locking is available for all top level objects including mappings, profiles and workflows. Locks acquired by the developers are automatically released when the objects are closed.


Developers have complete visibility on the objects that are locked by other developers, the time since this the user acquired the lock and other details. Users with elevated privileges

Locked objects in Developer

Administrators and elevated users can leverage the Administrator console to similarly manage the object locks and release the locks that are no longer valid and active

Intent based locks in Administrator


Informatica's Big Data Management has capabilities that allows various developers to work in parallel in a model repository that is version control enabled. Developers can check-in and check-out objects from the model repository that are seamlessly sent to an external version control system such as GIT. Big Data Management automatically maintains locks on the objects while allowing users to contribute and collaborate.


Informatica® Big Data Management allows users to build big data pipelines that can be seamlessly ported on to any big data ecosystem such as Amazon AWS, Azure HDInsight and so on. A pipeline built in the Big Data Management (BDM) is known as a mapping and typically defines a data flow from one or more sources to one or more targets with optional transformations in between. The mappings and other associated data objects are stored in a Model Repository via a Model Repository Service (MRS). In design-time environment, mappings are often organized into folders within projects. A mapping can refer to objects across projects and folders. Mappings can be grouped together into a workflow for orchestration. Workflow defines the sequence of execution of various objects including mappings.

Deployment overview

Mappings, workflows and other objects developed by Informatica developers are stored in the model repository that the MRS is integrated with. These design-time objects are deployed to the run-time DIS for execution. In a typical enterprise, there is more than one Informatica environment and the code developed in the Development domain is deployed to several non-production environment such as QA and UAT before deployed into Production. While the Development environments contain both design-time and run-time services, it is not necessary for the subsequent environments to be configured with both design-time and run-time services. For deploying objects from one environment to another, the objects must be added into containers called Applications. Applications can be deployed to a runtime Data Integration Service (DIS) or to an Application Archive (.iar) file. The application archive file can subsequently be deployed to data integration services in the same or different domain as depicted below.

BDM Deployment Process


There are two recommended ways of deployment: Classic deployment model and the CI/CD deployment model. In the example below, the migration and deployment of objects 

Classic deployment

In classic deployment model, the following process is followed:

  1. Metadata / objects that need to be deployed are deployed into the run-time DIS of the development environment
  2. Once unit testing is complete, the objects can be migrated to subsequent environment's MRS (such as QA) via XML export/import or via application export
  3. From the MRS of QA environment, application is rebuilt and deployed to the QA DIS
  4. Once functional testing is complete, the objects are migrated from QA MRS to Production MRS via XML export/import or via application export
  5. From the Production MRS, application is rebuilt and deployed to the Production DIS


Classic deployment model in BDM


In this approach, a design-time copy of the mappings and workflows are maintained in the MRS of every single environment. Application is rebuilt in each environment and deployed to the corresponding DIS. During migration of objects from one MRS to another, one of the available replacement strategies can be selected. Replacement strategies include replacing objects from the source upon conflict, reusing the objects in the target repository, etc. Upon conflicts, if the objects in the target repository are not replaced from the source, the application built in each environment may not match with that of the other as the dependency resolution can happen with different versions of the objects or different objects altogether

Agile deployment

In Agile deployment model, the following process is followed:

  1. An application archive is built in the Development repository
  2. This application archive (.iar) file is uploaded into a version control system such as GIT
  3. The application archive (.iar) file from version control system is then downloaded and deployed to the Development DIS using infacmd CLI
  4. Once unit testing is complete, the same step is repeated to deploy the application in to QA DIS
  5. Once functional testing is complete, the same step is repeated to deploy the application in to Production DIS


Agile deployment in BDM

In this approach, a single application archive file is used across several environments and hence consistency is assured. Though not common, the application archive can optionally be imported into MRS to maintain a design-time copy of the objects.


infacmd CLI can be used perform deployment in an automated manner. Both of the deployment models described above can be automated using the CLI. Automation server tools such as Jenkins can be used to automate the overall process of deployment as described in the blog: Continuous delivery with Informatica  BDM.


In Big Data Management, there are many ways to migrate and deploy objects  from one environment to another. Customers can choose the approach that best suits their needs. All approaches can be automated using infacmd CLI and automation tools such as Jenkins.


Informatica® Big Data Management allows users to build big data pipelines that can be seamlessly ported on to any big data ecosystem such as Amazon AWS, Azure HDInsight and so on. A pipeline built in the Big Data Management (BDM) is known as a mapping and typically defines a data flow from one or more sources to one or more targets with optional transformations in between. The mappings and other associated data objects are stored in a Model Repository via a Model Repository Service (MRS). In design-time environment, mappings are often organized into folders within projects. A mapping can refer to objects across projects and folders. Mappings can be grouped together into a workflow for orchestration. Workflow defines the sequence of execution of various objects including mappings.


Deployment process overview

For mappings and workflows to be deployed and executed in the run-time, they are grouped into applications. Application is a container that holds executable objects such as mappings and workflows. Applications are defined in the Developer and deployed to a Data Integration Service for execution. Once deployed, Data Integration Service persists a copy of the Application. Application can also be deployed to a file known as Informatica Application Archive (.iar) file, which can subsequently be deployed to a Data Integration Service in same or different domain. The overall process flow for deployment in BDM is as shown here:

BDM Deployment Process


The process of deploying a design-time application to an Informatica application archive (.iar) file can be executed via a infacmd CLI with Object Import Export (oie) plugin. A sample of the deploy application command is as follows: oie deployApplication -dn $infaDomainName -un $infaUserName -pd $infaPassword -sdn $infaSecurityDomain -rs $designTimeMRSName -ap $applicationPath -od $Output_Directory


The above example uses several user-defined environment variables. They can be named as per the individual organization standards. The password provided is case sensitive. Alternatively, an encrypted password string can be stored in the predefined environment variable INFA_DEFAULT_DOMAIN_PASSWORD. When an encrypted password is used, -pd option is not required. This command is documented in detail in Informatica documentation at Command Reference Guide → infacmd OIE Command Reference → Deploy Application


Once the application archive file is created, it can be optionally checked into GIT or other version control system for audit and tracking purposes.


Subsequently, the application archive file can be deployed to Data Integration Service of the same or different domain. Typically the application archive file is created out of a development domain and is eventually deployed into QA, UAT and Production domains. This can be achieved via infacmd CLI with Data Integration Service (dis)  plugin. A sample of such deployment command is as follows: dis deployApplication -dn $infaDomainName -un $infaUserName -pd $infaPassword -sdn $infaSecurityDomain -sn $dataIntegrationServiceName -a $applicationName -f $applicationArchiveFileName


This command is documented in detail in Informatica documentation at Command Reference Guide → infacmd DIS Command Reference → Deploy Application. Once deployment is successful the listApplications and listApplicationObjects in the DIS plugin can be used to get a list of the deployed applications and their contents respectively. This information can be used for post-deployment verification / sanity checks.


Integration with Jenkins

The CLI described above can be used to initiate the deployment process from within a Jenkins task. A "Build Step" of type "Execute Shell" can be added to the Jenkins. The step can be configured to execute one of the infacmd commands as shown in the example below


BDM deployment in Jenkins


A sample template file for Jenkins is attached (Jenkins-Template-App-Deployment) . The template contains the commands to:

  1. Create an Informatica Application Archive (.iar) file
  2. Commit the application archive file to GIT
  3. Deploy the application into DIS



Informatica BDM jobs can be deployed using Jenkins without any need for 3ʳᵈ party plugins. infacmd CLI commands can be directly used in Jenkins just as they can be used in an enterprise scheduling tool.



  • Keshav Vadrevu, Principal Product Manager
  • Paul Siddal, Big Data Presales Specialist




Dear Customer,


The Informatica Global Customer Support Team is excited to announce an all-new technical webinar and demo series – Meet the Experts, in partnership with our technical product experts and Product management. These technical sessions are designed to encourage interaction and knowledge gathering around some of our latest innovations and capabilities across Data Integration, Data Quality, Big Data etc. In these sessions, we will strive to provide you with as much technical details including new features and functionalities as possible, and where relevant, show you a demo or product walk-through as well.


Topic and Agenda


Topic: Meet the Experts Webinar - Sizing and Tuning for Spark in Informatica Big Data 10.2.1

Date: 22 August 2018

Time: 8:00 AM PST

Duration: 1 Hour


Informatica Big Data Management is the industry’s best solution for faster, more flexible, and more repeatable data ingestion and integration on Hadoop. Hundreds of organizations have adopted Informatica Big Data Management to take advantage of the power of Hadoop without the risks and delays of manual and specialized approaches.  To help you get the most out of Big Data Management, join this webinar to learn best practices for high-performance tuning, sizing, and security to get the most out of Informatica Big Data Management.


Learn about:


  • Sizing & Capacity Planning for Informatica’s platform and the underlying Hadoop cluster
  • Special Sizing Guidelines for Cloud environments like AWS and Azure
  • Optimal Deployment Architectures
  • Performance Tuning Tips for getting the most of out of engines like Apache Spark


Speaker: Vishal Kamath, Senior Manager, Performance



To register for this meeting


1. Go to

2. Register for the meeting.

3. Check for the confirmation email with instructions on how to join


To view in other time zones or languages, please click the link:




For assistance


1. Go to

2. On the left navigation bar, click "Support".


You can also contact us at:



MeetTheExperts Team

This blog post shows how to call webservice in BDM using Spark.

We will be using the python transformation that’s introduced in BDM 10.2.1 to call the web-service.


Java tx is another option to call the webservice.




Python and jep package need to be installed on BDM DIS server, refer to the install documentation to configure python transformation with BDM.


Post python Installation, edit the Hadoop connection by going to window --> preferences --> connections --> click on your hadoop connection




Edit the hadoop connection and go to spark tab




Under the spark tab , advanced properties --> click the Edit button.  The first 3 properties in the screenshot are the python properties which come by default and we will put in the values for those 3 properties. Change values as per your python installation.



Web-service Details



We will be using the following webservice to get the states for any given country


For example if we pass the country “USA” to the above url, the webservice will return all the state information within USA along with other details like area,capital, largest city etc.


To test the webservice for USA open the following URL in your browser and it will return json output.




Calling the web-service in BDM Mapping



We will create 2 mappings in BDM


In the first mapping we we will pass the country names from an input file,  then use the python tx to call the web-service and finally write the output to a HDFS file. The output will be a json file.


In the second mapping we will parse the json  output from the above mapping and write to Hive.



Mapping 1:


We have an input file is on hdfs with the following contents. We will pass the country names from this input file and get the states.


Create a flat file object file in developer client, in the advanced properties go to the read section and point to the HDFS connection and directory.




Create a new mapping and drag the flatfile object in the mapping and  choose the read operation.




Add a python transformation to the mapping and drag the country_name to input of python tx.

Create an output port for python tx and add call it states_data_json. The python tx ports should look like below.



Go to the python tab of the python tx and add the following code



import requests
import json

input_string = country_name
input_url =

states = requests.get(input_url + input_string + "/all")
states_data = states.json()
states_data_json = json.dumps(states_data)



Connect the output port of python transformation to a flatfile data  object writing to hdfs.




The target data object properties look like below



Change the execution mode of the mapping to run in spark



Execute the mapping and verify the status of the mapping in the admin console.




Verify the output of the mapping on hdfs and you will see the output in json format.




Mapping 2:

In this mapping we will parse the output file in the previous which is json making it structured.


Create a complex data object by right clicking on physical data objects -> New -> Physcial Data Object



Choose complex file data object and click Next



Name the complex file data objects as “cfr_states” and click on the browse button under connection and choose your hdfs connection and Under “selected resources” click on the Add button



In the Add resource, navigate to the hdfs file location (this is the output file location we gave in the previous mapping) and click on the json file and click OK





Click finish on the next step



Now create a dataprocessor transformation by right clicking on transformations -> New -> Transformation



Choose data processor transformation from the list of transformations



Name the data processor transformation as “dp_ws_state” and choose the “create a data processor using a wizard”




Since the input to the data processor transformation is coming as JSON , choose json In the next step and click next




Make sure you have sample output from the first mapping on the developer machine and choose the “sample json file” option and browse the sample json file and click next




Choose relational output and click finish




After you click on the finish button the data processor transformation will look like below


Create a hive table using the following DDL in your target database and import the hive table as a relational data object into the developer client


CREATE TABLE infa_pushdown.ws_states (

            FKey_states BIGINT,

            id DOUBLE,

            country STRING,

            name STRING,

            abbr STRING,

            area STRING,

            largest_city STRING,

            capital STRING

) ;



Now drag the compex file reader, the data processor transformation and the Hive target into the mapping. The connect the data port from CFR to the input of data processor and the output of data processor to Hive target.


The final mapping should look like below.



The mapping is tested in BDM 10.2.1 and in this version data processor is not supported in spark mode so we will run the second mapping using Blaze. Once data processor support is added in spark the second mapping can be eliminated by adding data processor transformation in the first mapping. Screen shot showing blaze as the execution engine.



Execute the mapping and verify the output of the target table by running the data viewer on target data object


Rest In Peace MapReduce

Posted by KVadrevu Jul 11, 2018

The Past

Introduced in the early days of big data, MapReduce is a framework that can be used to develop applications that process large amounts of data in a distributed computing environment.

A typical MapReduce job splits the input data set into chunks that are processed in parallel by map tasks. The framework sorts the output of the map tasks and passes the output to reduce tasks. The result is stored in a file system such as Hadoop Distributed File System (HDFS).

In 2012, Informatica Big Data Management (BDM) product introduced the ability to push down mapping logic to Hadoop clusters by leveraging the MapReduce framework. Big Data Management translated mappings into HiveQL, and then into MapReduce programs, which were executed on a Hadoop cluster.

By using this technique to convert mapping logic to HiveQL and pushing its processing to the Hadoop cluster, Informatica was the first (and still leading) vendor to offer the ability to push down processing logic to Hadoop without having to learn MapReduce. Developers simply had to select the "Hive" checkbox in the runtime properties of a mapping to run the mapping in MapReduce mode. This enabled hundreds of BDM customers to reuse their traditional Data Integration jobs and onboard them to the Hadoop ecosystem.

MapReduce as execution engine for BDM Mappings

The Present

With time, the Hadoop ecosystem evolved. For starters, MapReduce is no longer the only job processing framework. Tez, Spark and other processing frameworks are used throughout the industry as viable alternatives for MapReduce.

Recently, Spark has been widely adopted by vendors and customers alike. Several ecosystems such as Microsoft Azure use Spark as their default processing framework.

UPDATE: Hadoop distribution vendors have started to move away from MapReduce. As of HDP 3.0, MapReduce is no longer supported as an execution engine. Please refer to this link for more details on Hortonworks recommendations of execution engines: Apache Hive 3 architectural overview. Hive execution engine (including MapReduce) was deprecated in Big Data Management 2018 Spring release and has reached End of Life (EOL) Big Data Management 2019 Spring Release (10.2.2). Hive will continue to be supported as Source and Target in other execution modes such as Blaze and Spark.

Big Data Management adopted Spark several years ago and currently supports the latest versions of Spark. Please refer to Product Availability Matrix for the Spark version support. Big Data Management supports running Data Integration, Data Quality and Data Masking transformations using Spark.

For the most part, developers do not have to make any changes to mappings to leverage Spark. To use Spark to run mappings, they simply change the execution engine from Hive to Spark as shown in the following screenshot from Big Data Management 2018 Spring Release (BDM 10.2.1)

Migration from MapReduce to Spark

As a result of this simple change, the Data Integration Service generates Spark Scala code instead of MapReduce and executes it on the cluster.

The deprecation and End of Life of MapReduce

To accommodate the evolution of the Hadoop ecosystem, Informatica has announced the deprecation of MapReduce in Big Data Management in 2018 Spring release and has announced End of Life in 2019 Spring release (BDM 10.2.2). Customers previously leveraging Map Reduce are recommended to migrate to Spark. Customers currently on older versions of Big Data Management (including Spring release 2018 / BDM 10.2.1) are strongly recommended to migrate to Spark execution engine by selecting the Spark checkbox in Run-time properties of the mapping

Hive will continue to be supported as Source and Target in other execution modes such as Blaze and Spark.

Informatica's EOL of MapReduce applies only to Big Data Management mappings that use MapReduce as the run-time engine. It will not affect any Hadoop components (such as SQOOP) that internally rely on MapReduce or other third-party components. For example, when a customer uses SQOOP as a source in a Big Data Management mapping, Big Data Management will invoke SQOOP, which internally invokes MapReduce for processing. This will continue to be the case even after End of Life for the MapReduce execution mode.

Migration from MapReduce

To mitigate the continual evolution of big data ecosystems, Informatica recommends that developers practice an inclusive strategy for running mappings. In the Run-time mappings properties, select all Hadoop run-time engines, as shown in the following screenshot:

Polyglot computing in Big Data Management (BDM) including Spark

When all Hadoop run-time engines are selected, Informatica chooses the right execution engine at runtime for processing. Beginning with Big Data Management version 2018 Spring release (BDM 10.2.1), mappings default to Spark when Spark is selected with other execution engines. When customers use this inclusive strategy, the mappings with all Hadoop engines selected will automatically run in Spark mode.

Mappings that have only Hive (MapReduce) selected can be changed in bulk to leverage Spark. Several infacmd commands allow you to change the execution engine for mappings. Mappings that already exist as objects in the Model repository can be migrated to Spark by using one of the following commands:

  • infacmd mrs enableMappingValidationEnvironment
  • infacmd mrs setMappingExecutionEnvironment

This command receives the Model repository and project names as input and changes the execution engine for all mappings in the given project. The MappingNamesFilter property can be used to provide a comma-separated list of mappings to change. You can use wildcard characters to define mapping names. For more information about using these commands, see the Informatica Command Reference Guide.Similarly, for the mappings that have been deployed to the Data Integration Service as part of an application, you can use the following commands to change the execution engine for multiple mappings:

  • infacmd dis enableMappingValidationEnvironment
  • infacmd dis setMappingExecutionEnvironment



Starting Big Data Management 2019 Spring release (BDM 10.2.2), Hive execution mode (including Map Reduce) is no longer supported. Customers are recommended to migrate to Spark.

This blog is a short overview about Apache Airflow and shows how to integrate BDM with Apache Airflow. I also have a sample template to orchestrate BDM mappings. Same concept can be extended with Powercenter and non-BDM mappings.


Apache Airflow overview


Airflow is a platform to programmatically author, schedule and monitor workflows.


Airflow is not a data streaming solution. Tasks do not move data from one to the other (though tasks can exchange metadata!). Airflow is not in the Spark Streaming or Storm space, it is more comparable to Oozie or Azkaban.


Generally, Airflow works in a distributed environment, as you can see in the diagram below. The airflow scheduler schedules jobs according to the dependencies defined in directed acyclic graphs (DAGs), and the airflow workers pick up and run jobs with their loads properly balanced. All job information is stored in the meta DB, which is updated in a timely manner. The users can monitor their jobs via a shiny Airflow web UI and/or the logs.



Installing Apache Airflow


The following installation method is for non-production type of uses. Refer to airflow documentation for production type of deployments.


Apache Airflow has various operators listed below. An operator describes a single task in a workflow.


To trigger Informatica BDM mappings we will be using the bashoperator i.e triggering the mappings through commandline.


  1. If apache airflow is running on a machine different than infa node, install Informatica command line utilities on the airflow worker nodes
  2. Python



Create a directory /opt/infa/airflow



Easy way to install to run the following command. Pip is a python utility to install various python packages.


pip install apache-airflow

set AIRFLOW_HOME environment variable


Create a folder called “dags” inside AIRFLOW_HOME folder.


Initialize the airflow DB by typing the command “airflow initdb”. This is where the metdata will be stored, we will be using the default aclchemy database that comes with airflow, if needed the configuration can be modified to make mysql or postgres as the backend for airflow.



If the initdb shows any errors its most likely because of some missing airflow packages and a complete list of packages and the commands to install them are in the below link.


start the airflow web UI using the following command


Start the airflow scheduler



Login into the Airflow UI using the URL http://hostname:8080, if you have installed examples you should see the example DAG’s listed in the UI.



Creating a DAG for BDM Mappings



For the Demo we deployed the following 3 BDM mappings in to DIS.







The 3 applications need to be orchestrated in the following way.


  1. Application_m_01_Get_States_Rest_Webservice and Application_m_01_Get_States_Rest_Webservice can run in parallel
  2. Application_m_02_Parse_Webservice_Output will run only if Application_m_01_Get_States_Rest_Webservice is successful




Save the following code as inside as under /opt/infa/airflow/dags folder.

There are different ways to call infacmd runmapping command, for example the command can be put in a shell script and the script can be called from the DAG.



#Start Code

import airflow
from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime, timedelta

# these args will get passed on to each operator
# you can override them on a per-task basis during operator initialization
default_args = {
'owner': 'infa',
'depends_on_past': False,
'email': [''],
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=1),
'start_date': - timedelta(seconds=10),
# 'queue': 'bash_queue',
# 'pool': 'backfill',
# 'priority_weight': 10,
# 'end_date': datetime(2016, 1, 1),
# 'wait_for_downstream': False,
# 'dag': dag,
# 'adhoc':False,
# 'sla': timedelta(hours=2),
# 'execution_timeout': timedelta(seconds=300),
# 'on_failure_callback': some_function,
# 'on_success_callback': some_other_function,
# 'on_retry_callback': another_function,
# 'trigger_rule': u'all_success'

dag = DAG(
description='A simple Informatica BDM DAG')

# Printing start date and time of the DAG

t1 = BashOperator(

t2 = BashOperator(
bash_command=' ms RunMapping -dn infa_dom_1021 -sn dis_bdm_cdh -un Administrator -m m_01_Get_States_Rest_Webservice -a Application_m_01_Get_States_Rest_Webservice -pd admin',

t3 = BashOperator(
bash_command=' ms RunMapping -dn infa_dom_1021 -sn dis_bdm_cdh -un Administrator -m m_02_Parse_Webservice_Output -a Application_m_02_Parse_Webservice_Output',

t4 = BashOperator(
bash_command=' ms RunMapping -dn infa_dom_1021 -sn dis_bdm_cdh -un Administrator -m m_Read_Oracle_Customers_Write_Hive_Python -a Application_m_Read_Oracle_Customers_Write_Hive_Python',




# End code



Restart the airflow webserver and the Informatica_Bigdata_Demo DAG will appear in the list of DAG’s




Click on the DAG and go to Graph View, it gives a better view of orchestration.




Run the DAG and you will see the status of the DAG’s running in the Airflow UI as well as the Informatica monitor




The above DAG code can be extended to get the mapping logs, status of the runs.

What are we announcing?

Informatica Big Data Release 10.2.1


Who would benefit from this release?

This release is for all customers and prospects who want to take advantage of the latest Big Data Management, Big Data Quality, Big Data Streaming, Enterprise Data Catalog, and Enterprise Data Lake capabilities.


What’s in this release?

This update provides the latest ecosystem support, security, connectivity, cloud, and performance while improving the user experience.


Big Data Management (BDM)


Enterprise Class


  • Zero client configuration: Developers can now import the metadata from Hadoop clusters without configuring Kerberos Keytabs and configuration files on individual workstations by leveraging the Metadata Access Service
  • Mass ingestion: Data analysts can now ingest relational data into HDFS and Hive with a simple point and click interface and without having to develop individual mappings. Mass Ingestion simplifies ingestion of thousands of objects and operationalizes them via a non-technical interface
  • CLAIRE integration: Big Data Management now integrates with Intelligent Structure Discovery (that is part of Informatica Intelligent Cloud Services) to provide machine learning capabilities in parsing the complex file formats such as Weblogs
  • SQOOP enhancements: SQOOP connector has been re-architected to support high concurrency and performance
  • Simplified server configuration: Cluster configuration object and Hadoop connections are enhanced to improve the usability and ability to perform advanced configurations from the UI
  • Increased developer productivity: Developers can now use the "Run mapping using advanced options" menu to execute undeployed mappings by providing parameter file/sets, tracing level and optimizer levels in the Developer tool. Developers can also view optimized mappings after the parameter binding is resolved using the new "Show mapping with resolved parameters" option.
  • PowerCenter Reuse enhancements: Import from PowerCenter functionality has been enhanced to support import of PowerCenter workflows into Big Data Management
  • GIT Support: Big Data Management administrators can now configure GIT (in addition to Perforce and SVN) as the external versioning repository


Advanced Spark Support


  • End to end functionality: End to end Data Integration and Data Quality use-cases can now be executed on the Spark Engine. New and improved functionality includes Sequence Generator transformation, Pre/Post SQL support for Hive, support for Hive ACID Merge statement on supported distributions, Address Validation and Data Masking.
  • Data science integration: Big Data customers can now integrate pre-trained data science models with Big Data Management mappings using our new Python transformation.
  • Enhanced hierarchical data processing support: With support for Map data types and support of Arrays, Structs and Maps in Java transformations, customers can now build complex hierarchical processing mappings to run on the Spark engine. Enhancements in gestures and UI enable customers to leverage this functionality in a simple yet effective manner
  • Spark 2.2 support: Big Data Management now uses Spark 2.2 on supported Hadoop distributions




  • Ephemeral cluster support: With out-of-the-box ephemeral cluster support for AWS and Azure ecosystems, customers can now auto deploy and auto scale compute clusters from a BDM workflow and push the mapping for processing to the automatically deployed clusters
  • Cloudera Altus support: Cloudera customers can now push the processing to Cloudera Altus compute clusters.
  • Improved AWS connectivity: Amazon S3 and Redshift connectors have received several functional, usability and performance updates
  • Enhanced Azure connectivity: Azure WASB/Blob and SQL DW connectors have received several functional, usability and performance updates


Platform PAM Update


Oracle 12cR2


SQL Server 2017


Azure SQL DB

(PaaS / DBaaS , Single database model)


SQL Server 2008 R2 & 2012 (EOL)


IBM DB2 9.7 & 10.1 (EOL)


Suse 12 Sp2


Suse 12 Sp0



Not Supported


Not Supported

Windows Server

Not Supported

Model Repository - Versioned Controlled





Oracle Java 1.8.0_162




Tomcat 7.0.84



Big Data Quality (BDQ)



  • Enable data quality processing on Spark
  • Updated Address Verification Engine (AddressDoctor 5.12)
  • Support for custom schemas for reference tables
  • Updated workflow engine


  • Support Spark scale and execution with Big Data Management
  • Enhanced Address Verification engine with world-wide certifications
  • Flexible use of reference data with enterprise DB procedures
  • Faster start times for workflow engine


Big Data Streaming (BDS)


Change in Product Name: The product name has changed from "Informatica Intelligent Streaming" to "Big Data Streaming"


Azure Cloud Ecosystem Support


  • Endpoint Support: Azure EventHub as source & target and ADLS as target
  • Cloud deployment: Run streaming jobs in Azure cluster on HDInsight


Enhanced Streaming Processing and Analytics


  • Stateful computing support on streaming data
  • Support for masking streaming data
  • Support for normalizer transformation
  • Support for un-cached lookup on HBase tables in streaming
  • Kafka Enhancements - Kafka 1.0 support & support for multiple Kafka versions


New Connectivity and PAM support


  • Spark Engine Enhancements - Spark 2.2.1 support in streaming, Truncate table, Spark concurrency
  • Relational DB as target - SQL Server and Oracle
  • New PAM - HDInsight
  • Latest version support on Cloudera, Hortonworks, EMR


Enterprise Data Lake (EDL)


Change in Product Name: The product name has changed from "Intelligent Data Lake" to "Enterprise Data Lake"


Core Data Preparation


  • Data Preparation for JSON Lines (JSONL) Files: Users can add JSONL files to a project and structure the hierarchical data in row-column form. They can extract specific attributes from the hierarchy and can expand (or explode) arrays into rows in the worksheet.
    Pivot and UnPivot: Users can pivot or unpivot columns in a worksheet to transpose/reshape the row and column data in a worksheet for advanced aggregation and analysis.
  • Categorize and One-hot-encoding functions: Users can easily categorize similar values into fewer values to make analysis easier. With one-hot-encoding, the user can convert categorical values in a worksheet to numeric values suitable for machine learning algorithms.
  • Column Browser with Quality Bar: A new panel for browsing columns is added to the left panel in the worksheet. This easy to use column browser interface allows users to show/hide columns, search for columns, highlight columns in the worksheet, etc. The panel also has a Quality bar that shows unique, duplicate and blank value count percentages within the column. The panel can also show any associated glossary terms.
  • Project Level Graphical View: For a project with a large number of assets, the graphical view helps users understand the relationships between input data sources, sheets created, assets published, and Apache Zeppelin notebooks created. Users can navigate to the asset, notebook or the worksheet directly.
  • Insert recipe step, add a filter to an existing step: Users can insert a new step at any location in the recipe. They can also add/modify existing filters for any recipe step.
  • Data Type Inferencing optimization: Users can revert undesired inferencing done by data preparation engine and apply appropriate functions. They can revert or re-infer types as needed.
  • Show where the data in a column comes from: The column overview in the bottom panel now has a Source property that shows if the column corresponds to a physical input source column, another worksheet or a step in the recipe. If the user hovers over a data source name, the application shows details of the formula when available and highlights the appropriate recipe step.
  • UX Improvements in Filter-in-effect, Sampling, Join and Apply Rule panels: The user interface has been improved for clarity of icons and language used, visibility of information and button and better user flow for these panels. Users can also input constant values as inputs in the Apply Rule panel for text based user inputs.


Self-service and Collaboration


  • Self-service scheduling: Data Analysts now have the ability to schedule import, publish and export activities. The Import/Publish/Export wizard offers the choice to perform the activity now, or to schedule it. For publish, a “snapshot” of recipes is saved for execution at the scheduled time. Users can continue to work on the project and modify recipes without affecting scheduled activity.
    The “My Scheduled Activities” tab provides details of upcoming activities. The “Manage My Schedules” tab provides details of schedules and enables users to modify schedules.
    Scheduled activities can be monitored on the My Activities page. Functionally it has the same effect as running the activity manually. All the schedules created in Enterprise Data Lake and activities scheduled in Enterprise Data Lake are also visible in the Administrator Console tool.
  • Project History: Users (and IT/Governance staff) can view the important events that happened within a given project. These include events related to Project, Collaborators, Assets, Worksheets, Publications, Scheduled Publications, Notebook etc.
  • Copy-Paste Recipe Steps: Users can copy specific steps or the whole recipe and paste into another sheet in the same project or another project. There is also a way to map the input columns used in the source sheet to the columns present in the target sheet. This enables reuse of each other’s or their own work in the creation of repetitive steps.
  • Quick Filters for asset search in the data lake: In the search results, users have a single-click filter to get all the assets in the data lake that match the search criteria.
  • Recommendation Card UX Improvements: The Recommendation cards in the Project view now show the reason an asset was recommended for inclusion in the project, and what action user should take.
  • Details of Source Filters during Publish: During Publication, the Publish Wizard shows the details of "Source Filters" so the user understands the impact of including or not including the filters.


Enterprise Focus


  • Single Installer for Big Data Management, Enterprise Data Catalog and Enterprise Data Lake: The installation and upgrade flows have been improved and simplified with a single installer. Enterprise Data Lake customers can now install all three products in a single install. The total size of the single installer is just ~7GB due to better compression, as compared to the previous combined size of ~13GB. The process requires fewer domain restarts, and additional configurations can also be enabled in the same single flow.
  • Blaze as Default Execution Engines for Enterprise Data Lake: All Enterprise Data Lake processes using Big Data Management mapping execution now use Blaze as the default engine. This has improved performance and consistency.
  • SAML based SSO: Enterprise Data Lake now supports SAML based Single-Sign-On.
  • Lake Resource Management UI: Administrators can manage the Enterprise Data Catalog resources that represent the external data sources and metadata repositories from which scanners extract metadata for use in the data lake. The Lake Resource Management page also verifies the validity of resources, the presence of at least one Hive resource, etc. so that Enterprise Data Lake functionality is usable. Changes done through the Lake Resource Management page do not require a service restart.
  • Data Encryption for Data Preparation Service node: The temporary data created on Data Preparation Service nodes is encrypted for better security.
  • Demo Version of IT Monitoring Dashboard: A dashboard created in Apache Zeppelin allows administrators to monitor Enterprise Data Lake user activities. The dashboard is not a product feature, but an example to show what is possible with the audit information. The dashboard is an Apache Zeppelin Notebook built on top of the Enterprise Data Lake user event auditing database. The Zeppelin Notebook and associated content are available on request, but it is unsupported. The Audit mechanism has been changed and improved now to support direct queries using JDBC. 
  • Performance Improvement in Import process using CLAIRE: Using the profiling metadata information available in CLAIRE, the import process optimizes the number of sub-processes created thereby improving the overall performance of Import


Enterprise Data Catalog (EDC)


  • Intelligence
    • Enhanced Smart Discovery: By clustering similar columns from across data sources, EDC enables users to quickly associate business terms as well as classify data elements. Unsupervised clustering of similar columns is now based on names, unique values and patterns in addition to the existing data overlap similarity.
    • Enhanced Unstructured Data Discovery (Tech Preview): Enhanced unstructured data support for accurate domain discovery using NLP and new file system connectivity.
    • New Data Domain Rules: Override rules and new scan options for more granular control on rule based data domain inference.
  • Connectivity
    • New Filesystems: Added support for cataloging of Sharepoint, Onedrive, Azure Data Lake Store(ADLS), Azure Blob and MapRFS
    • New File Formats: Avro and Parquet support added in 10.2.1.
    • Remote File Access Scanner: Mounting folders on Hadoop nodes not required for Linux and Windows filesystem, instead the new remote file access scanner uses SMB for Windows and SFTP for Linux for cataloging.
    • Deep Dive Lineage support for BDM: End to End data lineage from Big Data Management with transformation logic and support for dynamic mappings
    • Data Integration Hub: Users can now scan DIH to access metadata for all objects and its subscriptions and publications.
    • Data Lineage from SQL Scripts(Tech Preview): End to End data lineage from hand coded SQL scripts to understand column level data flows and data transformations- includes support for Oracle PLSQL, DB2 PLSQL, Teradata BTEQ, HiveQL. Stored Procedures are not supported in this release.
    • Qlikview: Scan reports and report lineage from Qlikview.
  • User Experience Improvements
    • Manage business context with in-place editing of wikipages of data assets. Businessuser friendly data asset overview page that provides all the business context about the data asset. Inherit descriptions from Axon associations or type your own.
    • SAML Support: For Single Sign-On.
    • Multiple Business Term Linking: Allows custom attribute creation with Axon or BG term type to allow users to link multiple business terms with a single asset.
    • Search Facet Reordering: Catalog Administrators can now reorder the default facet orders making business facets show up higher than the technical facets.
    • New Missing Asset Link Report: To help users identify linked and unlinked data assets for a lineage-type source.
  • Open and Extensible Platform
    • New REST APIs for starting and monitoring scan jobs
    • S@S Interop: Shared Infrastructure, Metadata Repository, Data Domain Definitions and Curation Results shared across EDC and S@S. Users can now scan a resource once to see it in both EDC and S@S.
    • Reduced Sizing: Upto 3X reduction in computation cores required on the Hadoop cluster across all sizing categories
    • Ease of Deployment – Improved validation utilities, updated distro(HDP v2.6) for embedded cluster.


Release Notes & Product Availability Matrix (PAM)


PAM for Informatica 10.2.1


Informatica 10.2.1 Release Notes




Performance issues are seen when processing huge EBCDIC files in hive pushdown mode. The mapping has a Complex Data Object as source to read the EBCDIC file in binary mode followed by a Data Processor streamer to chunk the input data and convert the Data to relational format and finally write data to flat file in HDFS.


We are not able to leverage Hadoop parallel distributed computing since only one map job is spawned reading the entire EBCDIC binary file.
This document discusses some performance tuning steps when processing such huge EBCDIC files in Hadoop pushdown mode. The EBCDIC files assumed in this article are fixed length records based on Cobol Copybooks.


Suggestions to Improve performance


The mapping assumed here has a Complex Data Object as source to read the EBCDIC file in binary mode followed by a Data Processor streamer to chunk the input data and convert the Data to relational format and finally write data to flat file in HDFS.



So, some options to improve performance -

  1. In the streamer data processor, look for the “count” property when you segment the binary input under repeating_segment. Set the count property to define the number of records that the Data Integration Service must treat as a batch. When you set the count property, the Data Processor Engine will be called once for each batch of records instead of calling the Data Processor Engine for every record. So, batch processing to improve performance.
  2. You use “org.apache.hadoop.mapreduce.lib.input.FixedLengthInputFormat” the Custom Input format to split the binary records into equal length. This can be configured as custom Input format under the Complex File Reader, so the EBCDIC file is split-able based on multiples of single record length. That would help create multiple map jobs for each split. This would help only if your data has a fixed length records in EBCDIC format. If it is variable length, this approach would not help. 
  3. Configure the Input Split size maximum and minimum in such a way that it creates multiple maps for each input split.
  4. There is also com.informatica.hadoop.reader.RegexInputFormat available for custom Input Format value to help with the split, but I am not sure if you can construct a regex given the data is in EBCDIC format.


Steps to Improve the performance by spawning multiple map jobs.


We will be using the custom input format org.apache.hadoop.mapreduce.lib.input.FixedLengthInputFormat” class to split the input. Note that the class file for the Input format org.apache.hadoop.mapreduce.lib.input.FixedLengthInputFormat” is already part of various Hadoop distribution vendor jars. So you need not worry about copying them to services/shared/hadoop/<distro>/infaLib directory.


Here is the proof from class finder utility


Now, the detailed Steps …


1. Add the below snippet in the core-site.xml file under services/shared/hadoop/<your distro>/conf directory. As you can see, this is where the fixed length record size 1026 (in my case) is specified.






2. Open under services/shared/hadoop/<your distro>/InfaConf directory and add the core-site.xml file to the infapdo.aux.jars.path as shown below



3. In the mapping Runtime properties, override the Input Split size so you can create multiple map jobs. In my case, the dfs block size is 128 MB. So in order to set the input split size as 64 MB, I set the below values in the mapping runtime properties


The split size is calculated by the formula:-
max(mapred.min.split.size, min(mapred.max.split.size, dfs.block.size))

            mapred.min.split.size : value 33554432

            mapred.max.split.size : value 67108864


I have also set the number of mappers and reducers as shown below.



4. Complex File reader with input format, as of 10.1.1 version, prepends the size of the input length to the buffer that it sends out. So we need to skip it in the parser. You can see the highlighted section below where I have skipped the record size in bytes (4 bytes) under the repeating group in the Data Processor generated script using Cobol to relational wizard.



5. Open the Streamer Data Processor and set the offset to split as (fixed record length + bytes need to store the size of the record) = (1026+4) = 1030 in my case. Set as shown below



6. Set the custom input format under the complex file reader to “org.apache.hadoop.mapreduce.lib.input.FixedLengthInputFormat”



7. Adjust port precision depending on your record size. I am attaching my sample mapping here.


8. You can also set the count to greater than 1 to enable batch processing by Streamer Data Processor. 


9. Run the mapping in Hadoop pushdown mode using Hive engine and check if multiple maps spawned.


10. Tune the performance by adjusting the input split size and also the batch processing count in the streamer.




In 10.1.1, the record length needs to be set as part of the core-site xml file. So, in case you need to process multiple EBCDIC files of different sizes, there is only crude workarounds to accomplish this currently. You can either have multiple Data Integration Service created [Or] use Fixed length binary record format code available in Internet, compile and place them under server/shared/hadoop/<distro>/infaLib directory with different package names for different hard-coded record lengths. Sample code:  This custom Input format code is derived from github and this is not owned by Informatica. GCS will not be responsible for any issues or bug fixes with this format.



Tested in Product & Version: BDM 10.1.1 Update2


Author Name : Sugi


PowerCenter customers can now build mappings that can read from and write to Hive. Hive connectivity is now supported through ODBC.



Some salient features include:

  • Reading from both internally and externally managed Hive tables
  • Extensive data type support
  • Easily ingest data from relational tables, flat files and others sources into Hive or vice-versa
  • Query override support for complex and custom HiveQLs
  • Read support for partitioned and bucketed Hive tables
  • ANSI SQL-92 support


Known limitations

Following are the known limitations of the driver*:

  • Write to partitioned Hive tables
  • Write to bucketed Hive tables


* Support may vary between Hadoop distributions



Here are the KB articles that describe the simple steps that allow PowerCenter to read from and write to Hive.

Under very specific conditions and datatype usage, Informatica Data Service mapping with Joiner transformation that has a single join condition, data type as string and Non-Unicode data, could cause potential data loss. This issue is being tracked as Change Request PLAT-19257 and has been isolated to mappings that have the following characteristics:


Joiner transformations that meet ALL of the following criteria:


  • Join type: Equi-Join,
  • Datatype: String
  • Number of conditions: Single
  • Data Transferred: ASCII (dynamically determined based on connection codepages)
  • Engine Type: DataIntegrationService (DIS)
  • Mode: Native


Affected Software


Informatica Data Quality 10.0/Informatica Big Data Management Edition 10.0

Informatica Data Quality 10.1.0/Informatica Big Data Management Edition 10.1.0

Informatica Data Quality 10.1.1/Informatica Big Data Management Edition 10.1.1

Informatica Data Quality 10.1.1 HotFix 1/Informatica Big Data Management 10.1.1 Edition HotFix 1


Suggested Actions


Refer to Knowledge base (KB) article, 520801 that has more details on how to check if mappings meet the criteria.


If impacted, apply patch or Emergency Bug Fix (EBF) that is available for download from the FTP location provided below for 10.1.1 HotFix 1.

For all other versions, a workaround is available as mentioned in the KB article.


Informatica strongly recommends that customers apply the patch or workaround suggested in the KB article if they have mappings that fall into the scope defined above.



Location: updates/Informatica10/10.1.1 HotFix1/EBF-10298


Please refer to the attached document for more information.

Today, I’m really excited to announce the latest innovation from Cloudera and Informatica’s partnership.  With both companies focusing on helping customers adopt data lakes in the cloud, we are working together to dramatically simplify the delivery of data lakes in the cloud. 

A few months ago, Cloudera announce its new platform of a service offering for data lakes in the cloud known as Altus.  And today, I’m pleased to announce a unique integration between Informatica Big Data Management and Cloudera Altus.  This unique integrated solution will enable customers to easily deploy large-scale data workloads in the Cloud by reducing operational overhead of managing a hadoop cluster.

Cloudera Altus is a platform-as-a-service with services that enable you to analyze and process large-scale data sets in the cloud. Altus provisions clusters quickly and manages Hadoop clusters cost-effectively.

Informatica Big Data Management (BDM) provides the most advanced data integration platform for Hadoop

With Big Data Management on Altus, users can focus on building the data pipeline logic without worrying about cluster management. For example, organizations that wish to gain better visibility into data while eliminating data silos can use this approach to deliver data swiftly for analytics. Creating a data lake solution in the cloud using Big Data Management and Altus has been significantly simplified.

Creating a Data Lake Solution using BDM and Altus

Use Informatica Big Data Management and Cloudera Altus to build and quickly deploy data lakes in the cloud while eliminating data silos and increasing productivity to quickly process and analyze data.

The following illustration shows a typical data lake solution implementation using BDM on Altus:


Step 1. Offload infrequently used data from the Enterprise Data Warehouse and load raw data in batches to a defined landing zone in Amazon S3. This frees up space in the Enterprise Data Warehouse.

Step 2. Collect and stream data generated by machines and sensors, including application and weblog files, directly to Amazon S3. Note that staging the data in a temporary file system or the data warehouse is not longer required.

Step 3. Discover and profile data stored on Amazon S3. Profile the data to better understand its structure and context. Easily add requirements for enterprise accountability, control, and governance for compliance with corporate and governmental regulations and business service level agreements.

Step 4. Parse and prepare data from weblogs, application server logs, or sensor data. Typically, these data types are in multi-structured or unstructured format, which can be parsed to extract features and entities and to apply data quality techniques. This allows one to easily execute pre-built transformations as well as data quality and matching rules in Cloudera Altus to prepare data for analysis.

Step 5. After cleansing and transforming data onto Cloudera Altus, move high-value curated data to Amazon S3 or to Redshift. From S3 or Redshift. This will directly access data with Business Intelligence reports and applications.



Prototyping a Data Lake Solution

During this next step, a prototype will illustrate how to deploy a data lake solution using Cloudera Altus and Informatica Big Data Management.  The example below demonstrates how to run Cloudera Altus on an Amazon ecosystem while starting an on-demand Spark job with Altus.

To reduce the cluster management cost and operational overhead, use Big Data Management to create and terminate the Altus cluster on demand. To create the cluster, specify the cluster configuration details, including the instance type and the number of worker nodes.

Creating a Workflow

Create a workflow in Informatica Big Data Management to implement the data lake solution.

The following image shows a typical workflow for the data lake solution:


The workflow contains command and mapping tasks as described in the following steps:

Step 1. This creates an Altus cluster by retrieving cluster configuration parameters from the user to create the Altus cluster on demand.

The following illustration demonstrates an Altus cluster:

Step 2. Ingests data to Amazon S3. This mapping task runs on the newly created Altus cluster.

Step 3. Prepares the data by cleansing and integrating with other datasets. This mapping task runs on the newly created Altus cluster.

The mapping tasks are fully integrated with Cloudera Altus. The mapping tasks will run natively on the Spark engine.

The following image shows the mapping for data preparation:

Step 4. Terminate the Altus cluster after the mapping processing.

Monitoring Spark Jobs

The Informatica monitoring console can be used to monitor Spark jobs that run on the Altus cluster.

The following image demonstrates the Informatica monitoring console running Spark jobs on Altus:

The job below demonstrates how an Altus “Jobs” page runs an Informatica Spark job(s):


The following illustration demonstrates a completed Informatica Spark job on Altus:

Video: Data Lake Solution using BDM and Altus

To learn more, watch Informatica Big Data Management in action solving the “Data Lake on Altus” use case:

Looking Ahead

As a strategic partner to Cloudera, Informatica is delighted to announce this new solution that showcases and Informatica Big Data Management with Cloudera Altus technologies working together. Integrating Big Data Management with Altus will reduce the cost and operational overhead of managing Hadoop clusters for data engineers and IT Administrators alike. 

To learn more, visit

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