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Big Data Management

31 posts

What are we announcing?

Informatica 10.2.2 HotFix 1 Service Pack 1

Who would benefit from this release?

The release is for all Big Data customers and prospects who want to take advantage of updated Hadoop distribution support as well as fixes to core platform, connectivity and other functionality. You can apply this service pack after you install or upgrade to Informatica 10.2.2 HotFix 1.

What’s in this release?

Big Data PAM

Distribution Support:

  • Cloudera CDH: 6.2, 6.1, 5.16, 5.15, 5.14, 5.13
  • Hortonworks HDP: 2.6.x, 3.1.x
  • MapR: 6.0.1 with MEP 5.0, 6.1 with MEP 6.0
  • Azure HDInsight: 3.6.x WASB, ADLS Gen1, ADLS Gen2
  • Amazon EMR: 5.16.x, 5.20
  • Google Cloud Dataproc 1.3
  • Databricks 5.1, 5.3

Big Data Streaming

  • Apache Kafka version 2.3.x support

Enterprise Data Catalog

  • The update provides bug fixes for functional and performance improvements. Informatica recommends that Enterprise Data Catalog customers on 10.2.2 HF1 apply this service pack.

Enterprise Data Preparation 

  • Hadoop distribution support to align with Big Data Management
  • Ability to change delimiters and text qualifiers during file preparation

Release Notes & Product Availability Matrix (PAM)

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 and so on. In these sessions, we will strive to provide you with as many technical details as possible, including new features and functionalities, and where relevant, show you a demo or product walk-through as well.

 

Topic and Agenda

 

 

To get the most value from your data, you need to maintain a robust, stable production environment. Informatica Big Data Management (BDM) has been enhanced to help you do that with integrated DevOps and DataOps.

 

Join our complimentary webinar, "Operationalize Big Data Management With Integrated DevOps and DataOps," to learn more about what's new in BDM.

 

You will learn about:

  • Leveraging version control systems like Git
  • Invoking Informatica BDM processes from open source technologies like Jenkins
  • Using concurrency, stability, and other operationalization enhancements

 

Don't miss this opportunity to operationalize your big data management and extract more value from your big data.

What are we announcing?

Informatica 10.2.2 HotFix 1

 

Who would benefit from this release?

This release is for all Big Data and Enterprise Data Catalog customers and prospects who want to take advantage of the new capabilities and updated Hadoop distribution support. The release also includes fixes to core platform and connectivity. It includes support for new environments as well as fixes to support stable deployments.

 

What’s in this release?

This release includes Big Data Management, Big Data Quality, Big Data Streaming, Enterprise Data Catalog, and Enterprise Data Preparation capabilities.

  • Big Data PAM Update
    • Distribution Support:
      • Cloudera CDH: 6.2, 6.1, 5.16, 5.15, 5.14, 5.13
      • Hortonworks HDP: 2.6.x, 3.1 (Tech Preview)
      • MapR: 6.0.1 with MEP 5.0, 6.1 with MEP 6.0
      • Azure HDInsight: 3.6.x WASB, ADLS Gen1
      • Amazon EMR: 5.16.x, 5.20
      • Databricks 5.1
    • New Relational Systems:
      • Oracle 18c (Source/Target)
  • Enterprise Data Catalog Updates
    • Scanners
      • SAP HANA (Metadata Only): New scanner for SAP HANA that can extract object and lineage metadata. Lineage metadata includes calculation view to table lineage. Profiling is not supported.
      • ADLS Gen 2(Metadata Only): New scanner for Azure Data Lake Store Gen 2 to extract metadata from files and folders. All formats supported by ADLS Gen 1 scanner are supported for Gen 2. Profiling is not supported.
      • Profiling Warehouse Scanner: Extract profiling and domain discovery statistics from an IDQ or a BDQ profiling warehouse. Users who have already run profiling and enterprise discovery in IDQ/BDQ can now extract these profiling results and visualize them in EDC.
      • SAP PowerDesigner: Extract database model object from physical diagram including internal lineage. Model objects can be linked to physical object from other scanners.
      • (Tech Preview) Lineage Extraction from Stored Procedures: Ability to extract data lineage at the column level for stored procedures in Oracle and SQL Server.
      • (Tech Preview) Oracle Data Integrator: Ability to extract data lineage at the column level with transformation logic from Oracle Data Integration.
      • (Tech Preview) IBM Datastage: Ability to extract data lineage at the column level with transformation logic from IBM Datastage jobs.
      • Enhanced MS SQL Server scanner: support for windows based authentication using the EDC agent
    • Scanner Framework
      • Case insensitive linking: Ability to mark resources as case sensitive/insensitive. A new link ID is now generated for every object based on the above property. Useful for automatic lineage linking where ETL/BI tools refer to the object using a different case compared to the data source.
      • Offline scanner: support added for Sybase, IBM DB2 LUW, IBM DB2 z/OS, Netezza, Mysql, JDBC, PowerCenter, Informatica Platform, File Systems, Tableau, MicroStrategy, Hive, HDFS, Cloudera Navigator, Atlas
      • Custom Scanner Enhancements: The following new features are available for users of custom scanners:
        • Pre-Scripts: Users can now configure pre-scripts that are run before scanner execution. This allows running any custom extraction jobs or setup tasks.
        • File Path: Users can now configure a file path to pick the scanner assets csv, instead of uploading it to the catalog. The file should be either mounted or copied to the Informatica Domain machine with read permissions. This helps with automating and scheduling custom scanner runs.
      • Custom Scanner Framework Enhancements: The following new features are available for the developers of custom scanners:
        • Assign icons:  Ability to assign icons to types in the custom model
        • Detailed Lineage: Custom scanners can now include detailed lineage views which are rendered like transformation lineage from any native scanner.
        • Custom relationships: Ability to add custom relationships to be displayed in the relationship diagrams
      • Business User Experience
        • Search Operators: New search operators - AND, OR, NOT, double quotes, title: and description: for advanced search queries.
        • Search Tabs: Administrators can now create “Search Tabs” designed to personalize search experience by user groups and individual users. These search tabs are created with pre-selected facets that apply to a set of users/groups. EDC creates the following search tabs by default: “All”, “Data Objects”, “Data Elements”, “Business Terms”, “Reports” and “Resources”.
      • EDC Plug-In

        • Enterprise Data Catalog Tableau Extension: Enterprise Data Catalog Extension is a native extension for Tableau dashboard that you can use within Tableau within a Tableau Desktop, Tableau Server, and all the web browsers supported by Tableau version 2018.2.x onwards.
      • Supportability
        • Progress logs for re-index and bulk import/export
        • Utility to collect logs that is now expanded to support Cloudera in 10.2.2 HF1
      • (Tech Preview) Data Provisioning
        • (Tech Preview) Data Provisioning: After discovery, users can now move data to a target where it can be analyzed. EDC works with IICS to provision data for end users. Credentials are supplied by the users for both the source and the target.
          • Supported Sources in this release: Oracle, SQL Server
          • Supported Targets in this release: Amazon S3, Tableau Online, Oracle, Azure SQL DB
        • (Tech Preview) Live Data Preview: Users can now preview source data at the table level by providing source credentials.
      • CLAIRE
        • Intelligent Glossary Associations: The tech preview capability in 10.2.2 for linking glossaries to technical metadata is now GA. Additionally, EDC now supports auto-association of glossaries to objects at the table/file level.
      • PAM
        • Deployment Support
          • Cloudera: CDH 6.2, 6.1, 5.16, 5.15, 5.14
          • Hortonworks: HDP 2.6.x, (Tech Preview) HDP 3.1
        • Source Support
          • Hive, HDFS on CDH 6.1, 6.2
          • Hive, HDFS on HDP 3.1
          • Oracle Data Integrator 11g, 12c
          • Profile Warehouse on Oracle, SQL Server and IBM DB2 for Informatica 10.1.1 HF1, 10.2, 10.2.1, 10.2.2
          • SAP PowerDesigner 7.5.x to 16.x
          • SAP Hana DB 2.0
  • EDP Updates
    • Hadoop distribution support (aligned with Big Data Management)
    • Performance improvement in Preparation
    • Search alignment with Enterprise Data Catalog: Alignment with EDC in terms of search results and user experience (example: search tabs)
  • Connectivity Updates
    • Sqoop mapping with override query using aliases support in Spark mode
    • PAM certification for HBase for ecosystems: Cloudera, Hortonworks, MapR, AWS and Azure.
    • "--boundary-query" for specifying custom SQL query support for Sqoop import
  • Platform PAM Update
    • Oracle 18c - added
    • JVM support update: Azul OpenJDK 1.8.0_212 – updated

 

Release Notes & Product Availability Matrix (PAM)

 

Informatica 10.2.2 HotFix 1 Release Notes: https://docs.informatica.com/big-data-management/shared-content-for-big-data/10-2-2-hotfix-1/big-data-release-notes/abstract.html

 

PowerExchange Adapters 10.2.2 HotFix 1 Release Notes: https://docs.informatica.com/data-integration/powerexchange-adapters-for-informatica/10-2-2-hotfix-1/powerexchange-adapters-for-informatica-release-notes/abstract.html

 

PAM for Informatica 10.2.2 HotFix 1: https://network.informatica.com/docs/DOC-18280

 

You can download the Hotfixes from here.

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 and so on. In these sessions, we will strive to provide you with as many technical details as possible, including new features and functionalities, and where relevant, show you a demo or product walk-through as well.

 

Topic and Agenda

 

 

If you need to integrate and ingest large amounts of data at speed and scale, Informatica has two new big data cloud services to help.

 

Join our complimentary Meet the Experts webinar on July 16 to discover the capabilities of Informatica Intelligent Cloud Services (IICS) Integration at Scale and IICS Ingestion at Scale. You will learn:

  • How to lower overall TCO with CLAIRE-based auto scaling and provisioning of serverless Spark support
  • How to manage streaming and IoT data with real-time monitoring and lifecycle management
  • How to accelerate AI/ML and advanced analytics projects with Informatica Enterprise Data Preparation and DataRobot

 

If you want to create proof of concept for a big data project in just six weeks, turn your data lake into a modern data marketplace, and more, you won't want to miss this deep dive and demo.

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 and so on. In these sessions, we will strive to provide you with as many technical details as possible, including new features and functionalities, and where relevant, show you a demo or product walk-through as well.

 

Topic and Agenda

 

 

Once the host approves your request, you will receive a confirmation email with instructions for joining the meeting.

 

Here is the agenda for the webinar:

  • Spark Architecture
    • Spark Integration with BDM
    • Spark shuffle
    • Spark dynamic allocation
  • Journey from Hive, Blaze, to Spark
  • Spark troubleshooting and self-service
  • Spark Monitoring
  • References
  • Q & A

 

Speaker Details

 

The session will be presented by Vijay Vipin and Ramesh Jha, both Informatica BDM SMEs. They have been supporting our customers for over 5 years and have developed a niche across all aspects of BDM product portfolio.

What are we announcing?

Informatica 10.2.1 Service Pack 2

Who would benefit from this release?

This release is for all Big Data customers and prospects who want to take advantage of updated Hadoop distribution support as well as fixes to core platform, connectivity, and other functionality. You can apply this service pack after you install or upgrade to Informatica 10.2.1.

What’s in this release?

Big Data PAM Update

Applies to Big Data Management, Big Data Quality, and Big Data Streaming

  • Distribution Support
    • Cloudera CDH: 5.11.x, 5.12.x, 5.13.x, 5.14.x, 5.15.x
    • Hortonworks HDP: 2.5.x, 2.6.x
    • MapR 6.0 with MEP 5.x
    • Amazon EMR 5.14.x
    • Azure HDInsight 3.6.x

Enterprise Data Lake

  • Bug fixes for functional and performance improvements

Enterprise Data Catalog

  • Bug fixes for functional and performance improvements

Informatica recommends that all Enterprise Data Catalog customers on 10.2.1 apply this service pack.

Informatica 10.2.1 SP2 Release Notes

PAM for Informatica 10.2.1 SP2

You can download the Hotfixes from here.

What are we announcing?

Informatica 10.2.2 Service Pack 1

 

Who would benefit from this release?

This release is for all Big Data customers and prospects who want to take advantage of updated compute cluster support, updated streaming capabilities and security enhancements as well as fixes to core platform, connectivity, and other functionality. You can apply this service pack after you install or upgrade to Informatica 10.2.2.

 

What’s in this release?

 

Big Data PAM Update

Applies to Big Data Management, Big Data Quality, and Big Data Streaming

 

  • Distribution Support:
    • Cloudera CDH: 5.15, 5.16, 6.1
    • Hortonworks HDP: 2.6.5, 3.1 (Tech Preview)
    • MapR: 6.0.1 with MEP 5.0, 6.1 MEP 6.0
    • Azure HDInsight: 3.6.x WASB
    • Amazon EMR 5.16.x, EMR 5.20
    • Databricks 5.1
  • Security Enhancements:
    • Security enhancements for AWS. The following security mechanisms on AWS are now supported:
      • At rest:
        • SSE-S3
        • SSE-KMS
        • CSE-KMS
      • In transit:
        • SSE-SE
        • SSE-KMS

 

Big Data Streaming

  • Connectivity and Cloud
    • New connectivity: Native connectivity to Amazon S3 targets
    • Connectivity enhancements: Filename port support for HDFS targets
  • Stream processing and analytics
    • Message header support in streaming sources: JMS standard headers support
    • Enhanced MapR distribution support:
      • Support for Kafka in MapR distributions
      • Support for secured MapR Streams

Connectivity

  • Security Enhancements:
    • Certified SQL Server for SSL support with Sqoop

 

Enterprise Data Lake - now renamed to Enterprise Data Preparation

  • Product Rename:
    • With this release, Informatica Enterprise Data Lake is now renamed to Informatica Enterprise Data Preparation.
  • Distribution Support:
    • Cloudera CDH: 5.15, 5.16, 6.1
    • Hortonworks HDP: 2.6.5, 3.1 (Tech Preview)
    • MapR: 6.0.1 with MEP 5.0, 6.1 MEP 6.0
    • Azure HDInsight: 3.6.x WASB
    • Amazon EMR 5.16.x, EMR 5.20
  • Functional Improvements:
    • Users can preview and prepare Avro and Parquet files in the data lake.
    • Users can revert all data type inferencing within a single worksheet during data preparation.
    • Administrators can disable automatic data type inferencing for all worksheets in all projects.

 

Enterprise Data Catalog

  • Distribution Support Updates for EDC External Cluster Support:
    • Cloudera CDH: 6.1
    • Hortonworks HDP: 3.1 (Tech Preview)

 

Release Notes & Product Availability Matrix (PAM)

 

Informatica 10.2.2 SP1 Release Notes: https://docs.informatica.com/big-data-management/shared-content-for-big-data/10-2-2-service-pack-1/big-data-release-notes.html

 

PAM for Informatica 10.2.2 SP1:   https://network.informatica.com/docs/DOC-18072#comment-37896

Executive Summary:

 

Informatica Big Data Management (BDM) and Informatica Big Data Quality (BDQ) mappings having Decimal manipulations and source data size is greater than 256 MB, which when executed on Blaze engine can potentially cause data inconsistencies in decimal port values. This issue is being tracked as bug # BDM-24814 and is known to manifest under the following conditions:

 

  1. Active transformations with Decimal ports

Potential Data loss and dropped rows

    • Filters and Router with Decimal datatype ports on filter condition
    • Joiners with condition ports on Decimal datatype

     2. Passive transformations with Decimal ports

Potential data inconsistency with columns with decimal datatypes getting changed to NULL.

    • Expression with decimal manipulation

 

Affected Software

 

Informatica BDM/BDQ 10.0.x

Informatica BDM/BDQ 10.1.x

Informatica BDM/BDQ 10.2.0, 10.2.0 HF1, 10.2.0 HF2

Informatica BDM/BDQ 10.2.1, 10.2.1 SP1

Informatica BDM/BDQ 10.2.2

 

Suggested Actions

 

Step 1: Refer to Executive Summary and knowledge base article KB-575249 to identify if you are impacted

Step 2: If impacted, perform the following task to resolve the issue:

  • BDM/BDQ 10.2.1 – Apply Service Pack 2 (tentative release date is mid-May)
  • BDM/BDQ 10.2.2 – Apply Service pack 1 (tentative release date is mid-May)
  • BDM/BDQ 10.1.1 HF1 - Apply Emergency Bug Fix (EBF) that is available for download from https://tsftp.informatica.com

/updates/Informatica10/10.1.1 HotFix1/EBF-14519

  • Other BDM/BDQ versions: Please reach out to Informatica Global Customer Support

 

Informatica strongly recommends applying this patch for all Informatica environments that fall into the problem scope defined in the executive summary.

 

Frequently Asked Questions (FAQs) related to this advisory:

 

Q1: What is the scope of this advisory?

A: This advisory is applicable to Informatica Bigdata Management 10.0,10.1.0,10.1.1,10.2.0,10.2.1, 10.2.2 and running mappings in Hadoop pushdown mode using Blaze engine only. This advisory is not applicable if you are using any other versions of Informatica platforms like Informatica DataQuality and PowerCenter.

 

Q2: I am using one of the affected product versions and also have other Emergency Bug Fixes (EBFs) applied. What should I do?

A: You might need a combination EBF that includes the previous fix(es) as well as the fix for the issue covered in this advisory. Please contact Informatica Support to confirm if you would need a combination EBF.

 

Q3: Whom should I contact for additional questions?
A: For all questions related to this advisory, please contact your nearest Informatica Global Customer Support center.

https://www.informatica.com/services-and-training/support-services/contact-us.html

 

Disclaimer

INFORMATICA LLC   PROVIDES   THIS   INFORMATION ‘AS   IS’ WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES  OF  MERCHANTABILITY,  FITNESS FOR A  PARTICULAR  PURPOSE  AND ANY  WARRANTY  OR CONDITION OF NON-INFRINGEMENT

 

Revisions

V1.0 (April 22, 2019): Customer advisory published

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 and so on. In these sessions, we will strive to provide you with as many technical details as possible, including new features and functionalities, and where relevant, show you a demo or product walk-through as well.

 

Topic and Agenda

 

 

Once the host approves your request, you will receive a confirmation email with instructions for joining the meeting.

 

Here is the agenda for the webinar.

 

1. Overview of Blaze architecture and components

2. Blaze configuration (hadoopEnv.properties and beyond)

3. Logs location and collection

4. Common issues and troubleshooting

5. Tips and Tricks

 

This session is intended for BDM customers who are executing their mappings/profiles/scorecards using Blaze execution engine. At the end of this session, customers will be able to get insights into Blaze architecture, various components, and services associated with Blaze, how to troubleshoot the most common issues and how to access/provide logs to Informatica Support, which GCS requires for troubleshooting.

 

Speaker Details

 

The presenter for this session is Sujata, an Informatica GCS veteran, handling IDQ and BDM products for the past 5 years and has developed a niche in troubleshooting Blaze related issues.



Text Classification in BDM using NLP

 

This document shows how to do text classification in BDM using NLP. We will be using PredictionIO server to run our classification engine. The demo covers how to install predictionIO, build/train & deploy a text classification template and use that in BDM.

 

Apache PredictionIo Overview

 

Apache PredictionIO is an open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task.

 

It lets you:

 

  • Quickly build and deploy an engine as a web service on production with customizable templates;
  • Respond to dynamic queries in real-time once deployed as a web service;
  • Evaluate and tune multiple engine variants systematically;
  • Unify data from multiple platforms in batch or in real-time for comprehensive predictive analytics;
  • Speed up machine learning modeling with systematic processes and pre-built evaluation measures;
  • Support machine learning and data processing libraries such as Spark MLLib and OpenNLP;
  • Implement your own machine learning models and seamlessly incorporate them into your engine;
  • Simplify data infrastructure management.
  • Apache PredictionIO can be installed as a full machine learning stack, bundled with Apache Spark, MLlib, HBase, Spray and Elasticsearch, which simplifies and accelerates scalable machine learning infrastructure management.

 

 

 

PredictionIO Architecture

 

Apache PredictionIO consists of different components.

 

PredictionIO Platform: An open source machine learning stack built on the top of some state-of-the-art open source application such as Apache Spark, Apache Hadoop, Apache HBase and Elasticsearch.

 

Event Server: This continuously gathers data from your web server or mobile application server in real-time mode or batch mode. The gathered data can be used to train the engine or to provide a unified view for data analysis. The event server uses Apache HBase to store the data.

 

Engine Server: The engine server is responsible for making the actual prediction. It reads the training data from the data store and uses one or more machine learning algorithm for building the predictive models. An engine, once deployed as a web service, responds to the queries made by a web or mobile app using REST API or SDK.

 

Template Gallery: This gallery offers various types of pre-built engine templates. You can choose a template which is similar to your use case and modify it according to your requirements.

 

Prerequisites

 

PredictionIO can also be installed on an existing Hadoop cluster but for the demo we will be installing the following standalone components and configure with PredictionIO

 

  • Java 1.8
  • Apache Spark
  • Apache Hbase
  • Apache Hadoop
  • Elastic Search

 

 

Installing Apache PredictionIo

 

Make sure java is installed on the machine and set JAVA_HOME and add $JAVA_HOME/bin to your path

 

 

 

Download and Install Apache PredictionIo

 

Apache provides PredictionIo source files which can be downloaded and compiled locally.Create a temporary directory and compile the source file

 

mkdir /tmp/pio_sourcefiles

cd /tmp/pio_sourcefiles

 

 

Download the PredictionIO source file archive using any apache mirror site

 

wget http://apache.mirror.vexxhost.com/incubator/predictionio/0.12.0-incubating/apache-predictionio-0.12.0-incubating.tar.gz

 

Extract the archive and compile the source to create a distribution of PredictionIO

 

tar -xvf apache-predictionio-0.12.0-incubating.tar.gz

./make-distribution.sh

 

The above distribution will be built against the default versions of the dependencies, which are Scala 2.11.8, Spark 2.1.1, Hadoop 2.7.3 and ElasticSearch 5.5.2. The build will take approximately 10-15 mins.

 

You can also build PredictionIo using the latest supported version of spark, scala,Hadoop and hbase but you may see some warnings during the build as some functions might be deprecated. To run the build using your own version run ./make-distribution.sh -Dscala.version=2.11.11 -Dspark.version=2.1.2 -Dhadoop.version=2.7.4 -Delasticsearch.version=5.5.3, replacing the version number according to your choice.

 

Once the build successfully finishes, you will see the following message at the end.

 

PredictionIO binary distribution created at PredictionIO-0.12.0-incubating.tar.gz

 

The PredictionIO binary files will be saved in the PredictionIO-0.12.0-incubating.tar.gz archive. Extract the archive in the /opt directory and provide the ownership to the current user.

 

sudo tar xf PredictionIO-0.12.0-incubating.tar.gz -C /opt/

sudo chown -R $USER:$USER /opt/PredictionIO-0.12.0-incubating

 

 

Set the PIO_HOME environment variable.

 

echo "export PIO_HOME=/opt/PredictionIO-0.12.0-incubating" >> ~/.bash_profile

source ~/.bash_profile

 

 

Install Required Dependencies

 

Create a new directory to install PredictionIO dependencies such as HBase, Spark and Elasticsearch.

 

mkdir /opt/PredictionIO-0.12.0-incubating/vendors

 

Download Scala version 2.11.8 and extract it into the vendors directory.

 

wget https://downloads.lightbend.com/scala/2.11.8/scala-2.11.8.tgz

tar xf scala-2.11.8.tgz -C /opt/PredictionIO-0.12.0-incubating/vendors

 

Download Apache Hadoop version 2.7.3 and extract it into the vendors directory.

 

wget https://archive.apache.org/dist/hadoop/common/hadoop-2.7.3/hadoop-2.7.3.tar.gz

tar xf hadoop-2.7.3.tar.gz -C /opt/PredictionIO-0.12.0-incubating/vendors

 

Apache Spark is the default processing engine for PredictionIO. Download Spark version 2.1.1 and extract it into the vendors directory.

 

wget https://archive.apache.org/dist/spark/spark-2.1.1/spark-2.1.1-bin-hadoop2.7.tgz

tar xf spark-2.1.1-bin-hadoop2.7.tgz -C /opt/PredictionIO-0.12.0-incubating/vendors

 

Download Elasticsearch version 5.5.2 and extract it into the vendors directory.

 

wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-5.5.2.tar.gz

tar xf elasticsearch-5.5.2.tar.gz -C /opt/PredictionIO-0.12.0-incubating/vendors

 

Finally, download HBase version 1.2.6 and extract it into the vendors directory.

 

wget https://archive.apache.org/dist/hbase/stable/hbase-1.2.6-bin.tar.gz

tar xf hbase-1.2.6-bin.tar.gz -C /opt/PredictionIO-0.12.0-incubating/vendors

 

Open the hbase-site.xml configuration file to configure HBase to work in a standalone environment.

 

vi /opt/PredictionIO-0.12.0-incubating/vendors/hbase-1.2.6/conf/hbase-site.xml

Add the following block to the hbase configuration

 

hbase-site.xml

<configuration>

<property>

<name>hbase.rootdir</name>

<value>file:///opt/PredictionIO-0.12.0-incubating/vendors/hbase-1.2.6/data</value>

</property>

<property>

<name>hbase.zookeeper.property.dataDir</name>

<value>/opt/PredictionIO-0.12.0-incubating/vendors/hbase-1.2.6/zookeepe</value>

</property>

</configuration>

 

The hbase-site.xml should look like below.

 

The data directory will be created automatically by HBase. Edit the HBase environment file to set the JAVA_HOME path.

 

vi /opt/PredictionIO-0.12.0-incubating/vendors/hbase-1.2.6/conf/hbase-env.sh

 

Add JAVA_HOME on line 27 and and also comment line 46 and 47 as they are not needed for Java 8. The hbase-env.sh should look like below.

 

 

Configuring Apache PredictionIo

 

The default configuration in the PredictionIO environment file pio-env.sh assumes that we are using PostgreSQL or MySQL. As we have used HBase and Elasticsearch, we will need to modify nearly every configuration in the file. It's best to take a backup of the existing file and create a new PredictionIO environment file.

 

mv /opt/PredictionIO-0.12.0-incubating/conf/pio-env.sh /opt/PredictionIO-0.12.0-incubating/conf/pio-env.sh.bak

 

Create a new file for PredictionIO environment configuration.

 

vi /opt/PredictionIO-0.12.0-incubating/conf/pio-env.sh

The file should look like below

 

pio-env.sh

 

# PredictionIO Main Configuration

#

# This section controls core behavior of PredictionIO. It is very likely that

# you need to change these to fit your site.

 

# SPARK_HOME: Apache Spark is a hard dependency and must be configured.

SPARK_HOME=$PIO_HOME/vendors/spark-2.1.1-bin-hadoop2.7

 

# POSTGRES_JDBC_DRIVER=$PIO_HOME/lib/postgresql-42.0.0.jar

# MYSQL_JDBC_DRIVER=$PIO_HOME/lib/mysql-connector-java-5.1.41.jar

 

# ES_CONF_DIR: You must configure this if you have advanced configuration for

#              your Elasticsearch setup.

ES_CONF_DIR=$PIO_HOME/vendors/elasticsearch-5.5.2/config

 

# HADOOP_CONF_DIR: You must configure this if you intend to run PredictionIO

#                  with Hadoop 2.

HADOOP_CONF_DIR=$PIO_HOME/vendors/spark-2.1.1-bin-hadoop2.7/conf

 

# HBASE_CONF_DIR: You must configure this if you intend to run PredictionIO

#                 with HBase on a remote cluster.

HBASE_CONF_DIR=$PIO_HOME/vendors/hbase-1.2.6/conf

 

# Filesystem paths where PredictionIO uses as block storage.

PIO_FS_BASEDIR=$HOME/.pio_store

PIO_FS_ENGINESDIR=$PIO_FS_BASEDIR/engines

PIO_FS_TMPDIR=$PIO_FS_BASEDIR/tmp

 

# PredictionIO Storage Configuration

#

# This section controls programs that make use of PredictionIO's built-in

# storage facilities. Default values are shown below.

#

# For more information on storage configuration please refer to

# http://predictionio.incubator.apache.org/system/anotherdatastore/

 

# Storage Repositories

 

# Default is to use PostgreSQL

PIO_STORAGE_REPOSITORIES_METADATA_NAME=pio_meta

PIO_STORAGE_REPOSITORIES_METADATA_SOURCE=ELASTICSEARCH

 

PIO_STORAGE_REPOSITORIES_EVENTDATA_NAME=pio_event

PIO_STORAGE_REPOSITORIES_EVENTDATA_SOURCE=HBASE

 

PIO_STORAGE_REPOSITORIES_MODELDATA_NAME=pio_model

PIO_STORAGE_REPOSITORIES_MODELDATA_SOURCE=LOCALFS

 

# Storage Data Sources

 

# PostgreSQL Default Settings

# Please change "pio" to your database name in PIO_STORAGE_SOURCES_PGSQL_URL

# Please change PIO_STORAGE_SOURCES_PGSQL_USERNAME and

# PIO_STORAGE_SOURCES_PGSQL_PASSWORD accordingly

# PIO_STORAGE_SOURCES_PGSQL_TYPE=jdbc

# PIO_STORAGE_SOURCES_PGSQL_URL=jdbc:postgresql://localhost/pio

# PIO_STORAGE_SOURCES_PGSQL_USERNAME=pio

# PIO_STORAGE_SOURCES_PGSQL_PASSWORD=pio

 

# MySQL Example

# PIO_STORAGE_SOURCES_MYSQL_TYPE=jdbc

# PIO_STORAGE_SOURCES_MYSQL_URL=jdbc:mysql://localhost/pio

# PIO_STORAGE_SOURCES_MYSQL_USERNAME=pio

# PIO_STORAGE_SOURCES_MYSQL_PASSWORD=pio

 

# Elasticsearch Example

PIO_STORAGE_SOURCES_ELASTICSEARCH_TYPE=elasticsearch

PIO_STORAGE_SOURCES_ELASTICSEARCH_HOSTS=localhost

PIO_STORAGE_SOURCES_ELASTICSEARCH_PORTS=9200

PIO_STORAGE_SOURCES_ELASTICSEARCH_SCHEMES=http

PIO_STORAGE_SOURCES_ELASTICSEARCH_CLUSTERNAME=pio

PIO_STORAGE_SOURCES_ELASTICSEARCH_HOME=$PIO_HOME/vendors/elasticsearch-5.5.2

 

# Optional basic HTTP auth

# PIO_STORAGE_SOURCES_ELASTICSEARCH_USERNAME=my-name

# PIO_STORAGE_SOURCES_ELASTICSEARCH_PASSWORD=my-secret

# Elasticsearch 1.x Example

# PIO_STORAGE_SOURCES_ELASTICSEARCH_TYPE=elasticsearch

# PIO_STORAGE_SOURCES_ELASTICSEARCH_CLUSTERNAME=<elasticsearch_cluster_name>

# PIO_STORAGE_SOURCES_ELASTICSEARCH_HOSTS=localhost

# PIO_STORAGE_SOURCES_ELASTICSEARCH_PORTS=9300

# PIO_STORAGE_SOURCES_ELASTICSEARCH_HOME=$PIO_HOME/vendors/elasticsearch-1.7.6

 

# Local File System Example

PIO_STORAGE_SOURCES_LOCALFS_TYPE=localfs

PIO_STORAGE_SOURCES_LOCALFS_PATH=$PIO_FS_BASEDIR/models

 

# HBase Example

PIO_STORAGE_SOURCES_HBASE_TYPE=hbase

PIO_STORAGE_SOURCES_HBASE_HOME=$PIO_HOME/vendors/hbase-1.2.6

 

# AWS S3 Example

# PIO_STORAGE_SOURCES_S3_TYPE=s3

# PIO_STORAGE_SOURCES_S3_BUCKET_NAME=pio_bucket

# PIO_STORAGE_SOURCES_S3_BASE_PATH=pio_model

 

Open the Elasticsearch configuration file:

 

cat /opt/PredictionIO-0.12.0-incubating/vendors/elasticsearch-5.5.2/config/elasticsearch.yml

 

Uncomment the line and set the cluster name to exactly the same as the one provided in the PredictionIO environment file. The cluster name is set to pio in the configuration (in bold below)

 

elasticsearch.yml

 

# ======================== Elasticsearch Configuration =========================

#

# NOTE: Elasticsearch comes with reasonable defaults for most settings.

#       Before you set out to tweak and tune the configuration, make sure you

#       understand what are you trying to accomplish and the consequences.

#

# The primary way of configuring a node is via this file. This template lists

# the most important settings you may want to configure for a production cluster.

#

# Please consult the documentation for further information on configuration options:

# https://www.elastic.co/guide/en/elasticsearch/reference/index.html

#

# ---------------------------------- Cluster -----------------------------------

#

# Use a descriptive name for your cluster:

#

cluster.name: pio

#

# ------------------------------------ Node ------------------------------------

#

# Use a descriptive name for the node:

#

#node.name: node-1

#

# Add custom attributes to the node:

#

#node.attr.rack: r1

#

# ----------------------------------- Paths ------------------------------------

#

# Path to directory where to store the data (separate multiple locations by comma):

#

#path.data: /path/to/data

#

# Path to log files:

#

#path.logs: /path/to/logs

#

# ----------------------------------- Memory -----------------------------------

#

# Lock the memory on startup:

#

#bootstrap.memory_lock: true

#

# Make sure that the heap size is set to about half the memory available

# on the system and that the owner of the process is allowed to use this

# limit.

#

# Elasticsearch performs poorly when the system is swapping the memory.

#

# ---------------------------------- Network -----------------------------------

#

# Set the bind address to a specific IP (IPv4 or IPv6):

#

#network.host: 192.168.0.1

#

# Set a custom port for HTTP:

#

#http.port: 9200

#

# For more information, consult the network module documentation.

#

# --------------------------------- Discovery ----------------------------------

#

# Pass an initial list of hosts to perform discovery when new node is started:

# The default list of hosts is ["127.0.0.1", "[::1]"]

#

#discovery.zen.ping.unicast.hosts: ["host1", "host2"]

#

# Prevent the "split brain" by configuring the majority of nodes (total number of master-eligible nodes / 2 + 1):

#

#discovery.zen.minimum_master_nodes: 3

#

# For more information, consult the zen discovery module documentation.

#

# ---------------------------------- Gateway -----------------------------------

#

# Block initial recovery after a full cluster restart until N nodes are started:

#

#gateway.recover_after_nodes: 3

#

# For more information, consult the gateway module documentation.

#

# ---------------------------------- Various -----------------------------------

#

# Require explicit names when deleting indices:

#

#action.destructive_requires_name: true

 

Add the $PIO_HOME/bin directory into the PATH variable so that the PredictionIO executables are executed directly.

 

echo "export PATH=$PATH:$PIO_HOME/bin" >> ~/.bash_profile

source ~/.bash_profile

 

 

At this point, PredictionIO is successfully installed on your server.

 

 

Starting PredictionIo

 

 

You can start all the services in PredictionIO such as Elasticsearch, HBase and Event server using a single command.

 

 

You will see the following output.


Use the following command to check the status of the PredictionIO server.

 

You will see the following output.

pio-start-all

Starting Elasticsearch...

Starting HBase...

starting master, logging to /opt/PredictionIO-0.12.0-incubating/vendors/hbase-1.2.6/bin/../logs/hbase-user-master-vultr.guest.out

   Waiting 10 seconds for Storage Repositories to fully initialize...

   Starting PredictionIO Event Server...

 

Implementing an Engine Template

 

Several ready to use engine templates are available on the PredictionIO Template Gallery which can be easily installed on the PredictionIO server. You can browse through the list of engine templates to find the one that is close to your requirements or you can write your own engine.

 

In this tutorial, we will implement the Text Classification engine template to demonstrate the functionality of PredictionIO server using some sample data.

 

This engine template takes input like twitter or email or newsgroups data and tells us the topic inside the data like whether they are talking about a particular topic, you can send a query with the twitter or email data and the output will be the topic name.

 

Install Git, as it will be used to clone the repository.

 

sudo yum -y install git

Clone the text classification engine template on your system.

 

git clone https://github.com/amrgit/textclassification.git

cd template-classification-opennlp

 

You can choose any name for your application.

 

pio app new docclassification

You can type the following command to list the apps that are created inside PredictionIo

 

pio app list

 

Install PredictionIO python SDK using pip

 

pip install predictionio

 

 

Run the Python script to add the sample data to the event server. The git project already has some sample datasets which can be used to train the model. We will use the  20newsgroups training data set for this demo.

python3 data/import_data.py --access_key 8FhrUWaTIZJLLPcuS0bRu64O4TiZoYjgZFWjWm_Mik3QgoxoZAUO-7Ti4xo59ZcX --file datasets/20ng-train-no-stop.txt

 

 

If the import is successful you should see a message like below

 

 

The above script imports 11294 events To check if the events are imported or not, you can run the following query.

 

curl -i -X GET "http://localhost:7070/events.json?accessKey=8FhrUWaTIZJLLPcuS0bRu64O4TiZoYjgZFWjWm_Mik3QgoxoZAUO-7Ti4xo59ZcX"

 

 

The output will show you the list of all the imported events in JSON format.

 

Open the engine.json file into the editor. This file contains the configuration of the engine. Make sure the appId matches to the Id from “pio app list” command.

 

 

Build the application using the following command. If you do no want to run in verbose remove the verbose parameter.

 

pio build --verbose

 

You should see the message that the build is successful and ready for training.

 

 

Train the engine. During the training, the engine analyzes the data set and trains itself according to the provided algorithm.

pio train

If the train command fails with OOM errors use the following command to

 

Header 1

pio train -- --driver-memory 2g --executor-memory 4g

You should see a message that the train is successful

 

 

Before we deploy the application, we will need to open the port 8000 so that the status of the application can be viewed on the Web GUI. Also, the websites and applications using the event server will send and receive their queries through this port. You can also use a different port in the deploy command.

 

You can deploy the PredictionIO engine using the following command

 

pio deploy

You can increase the driver memory for deploy command using the following command and you use use a different port using the –port argument.

 

pio deploy -- --driver-memory 4G &

You will see a message that the engine is deployed and running.

 

 

Calling PredictionIO engine through BDM

 

BDM will be sending data to predictionIO event server which is then passed to the prediction engine and the the engine sends the results back to BDM

 

 

 

You can use the sample datasets provided in the github link for testing through BDM mapping or get your own dataset. For the demo im using the datasets on github and copied these datasets onto my Hadoop cluster.

 

 

Create a flatfile dataobject called opennlp_dataset in developer client and in the advanced tab change the connection type to Hadoop file system and connection name to your HDFS connection. Also change the source file directory to the hdfs location.

 

 

 

 

Create a new mapping and call it m_Text_Classification_OpenNLP

 

 

 

 

Drag the flatfile object opennlp_dataset created in the above step into the mapping workspace and choose read operation. The object will look like below in the mapping workspace

 

 

 

Add a python transformation and create an input port called “data” and an output port called “class_output”

 

 

 

In the python tab of python transformation add the following code

 

import predictionio
engine_client = predictionio.EngineClient(
url="http://infa1021.infaaws.com:8000")
text_class = (engine_client.send_query({
"sentence": data}))
for i in text_class:
class_output = text_class[i]

 

 

 

The mapping should like below at this point.

 

 

 

Add a pass through expression transformation and drag the class_output from python tx to expression tx.

 

You can right click on the expression transformation and click on create target, then click relational choose relational,choose hive from drop down,name the hive table as text_class_output

 

The final mapping should like this.

 

 

 

 

In the mapping  properties window choose spark as execution engine and the Hadoop connection.

 

 

Run the mapping and monitor it through admin console

 

 

 

Once the mapping is successful verify the output through beeline or any hive client. For the demo im using zeppelin to query the table and view the results as piechart.

 

 

 

You can also view the results in tabular format in zeppelin and as you can see in the screen shot below the count of messages and topic name.

 

 

PredictionIO has other engine templates which can be deployed in a similar fashion and used in BDM.

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: https://network.informatica.com/docs/DOC-18072

 

Informatica 10.2.2 Release Notes

 

Introduction

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.

Summary

'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.

Author

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

Introduction

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.

Collaboration

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

Summary

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.

Introduction

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.

Automation

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.

Summary

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.