SlideShare a Scribd company logo

Managing BI Objects in an ITIL Environment

•Download as PPT, PDF•
•
D
David Waters

The document discusses migration and source control of SAS business intelligence objects in an ITIL environment. It recommends using Subversion (SVN) for source control, with work units exported from SAS tools and imported into SVN. The development and release methodology involves exporting metadata from tools like DI Studio and OLAP Cube Studio into SVN, then full development, testing, and migration cycles to move changes through environments.

1 of 35
Migration and Source Control of SAS Business Intelligence objects in an ITIL environment ,[object Object],[object Object],[object Object]
ITIL - Information Technology Infrastructure Library ,[object Object],[object Object]
The Most Boring Topic Imaginable ,[object Object],[object Object],[object Object]
Unless ,[object Object],[object Object],[object Object]
About Griffith ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Technology in Use at Griffith ,[object Object],[object Object],[object Object]
Technology in Use at Griffith ,[object Object],[object Object],[object Object]
Technology in Use at Griffith ,[object Object],[object Object],[object Object]
Design Methodology ,[object Object],[object Object],[object Object]
Design Methodology ,[object Object],[object Object],[object Object],[object Object]
Design Methodology ,[object Object],[object Object],[object Object],[object Object],[object Object]
Development/Release Methodology ,[object Object],[object Object]
Development/Release Methodology
Source Control ,[object Object],[object Object]
Exporting Metadata DI Studio
Exporting Metadata DI Studio
Exporting Metadata OLAP Cube Studio
Exporting Metadata OLAP Cube Studio
Exporting Metadata
Exporting Metadata
Exporting Metadata
DI Studio
Tortoise SVN
SVN ,[object Object],[object Object]
SVN ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Tortoise SVN
Tortoise SVN
Tortoise SVN
Tortoise SVN
Tortoise SVN
Some Process Details – ETL Jobs ,[object Object],[object Object],[object Object],[object Object],[object Object]
e.g
Some Process Details – OLAP ,[object Object],[object Object]
Some Process Details – Info Maps ,[object Object],[object Object],[object Object],[object Object]
Questions? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Recommended

NLP-Focused Applied ML at Scale for Global Fleet Analytics at ExxonMobil
NLP-Focused Applied ML at Scale for Global Fleet Analytics at ExxonMobilNLP-Focused Applied ML at Scale for Global Fleet Analytics at ExxonMobil
NLP-Focused Applied ML at Scale for Global Fleet Analytics at ExxonMobilDatabricks
 
Near real-time anomaly detection at Lyft
Near real-time anomaly detection at LyftNear real-time anomaly detection at Lyft
Near real-time anomaly detection at Lyftmarkgrover
 
Spark and machine learning in microservices architecture
Spark and machine learning in microservices architectureSpark and machine learning in microservices architecture
Spark and machine learning in microservices architectureStepan Pushkarev
 
Multi runtime serving pipelines for machine learning
Multi runtime serving pipelines for machine learningMulti runtime serving pipelines for machine learning
Multi runtime serving pipelines for machine learningStepan Pushkarev
 
Spark ML Pipeline serving
Spark ML Pipeline servingSpark ML Pipeline serving
Spark ML Pipeline servingStepan Pushkarev
 
Productionizing Deep Reinforcement Learning with Spark and MLflow
Productionizing Deep Reinforcement Learning with Spark and MLflowProductionizing Deep Reinforcement Learning with Spark and MLflow
Productionizing Deep Reinforcement Learning with Spark and MLflowDatabricks
 
Enabling Physics and Empirical-Based Algorithms with Spark Using the Integrat...
Enabling Physics and Empirical-Based Algorithms with Spark Using the Integrat...Enabling Physics and Empirical-Based Algorithms with Spark Using the Integrat...
Enabling Physics and Empirical-Based Algorithms with Spark Using the Integrat...Databricks
 
Big Data at Speed
Big Data at SpeedBig Data at Speed
Big Data at Speedmarkgrover
 

Recommended

NLP-Focused Applied ML at Scale for Global Fleet Analytics at ExxonMobil
NLP-Focused Applied ML at Scale for Global Fleet Analytics at ExxonMobilNLP-Focused Applied ML at Scale for Global Fleet Analytics at ExxonMobil
NLP-Focused Applied ML at Scale for Global Fleet Analytics at ExxonMobilDatabricks
 
Near real-time anomaly detection at Lyft
Near real-time anomaly detection at LyftNear real-time anomaly detection at Lyft
Near real-time anomaly detection at Lyftmarkgrover
 
Spark and machine learning in microservices architecture
Spark and machine learning in microservices architectureSpark and machine learning in microservices architecture
Spark and machine learning in microservices architectureStepan Pushkarev
 
Multi runtime serving pipelines for machine learning
Multi runtime serving pipelines for machine learningMulti runtime serving pipelines for machine learning
Multi runtime serving pipelines for machine learningStepan Pushkarev
 
Spark ML Pipeline serving
Spark ML Pipeline servingSpark ML Pipeline serving
Spark ML Pipeline servingStepan Pushkarev
 
Productionizing Deep Reinforcement Learning with Spark and MLflow
Productionizing Deep Reinforcement Learning with Spark and MLflowProductionizing Deep Reinforcement Learning with Spark and MLflow
Productionizing Deep Reinforcement Learning with Spark and MLflowDatabricks
 
Enabling Physics and Empirical-Based Algorithms with Spark Using the Integrat...
Enabling Physics and Empirical-Based Algorithms with Spark Using the Integrat...Enabling Physics and Empirical-Based Algorithms with Spark Using the Integrat...
Enabling Physics and Empirical-Based Algorithms with Spark Using the Integrat...Databricks
 
Big Data at Speed
Big Data at SpeedBig Data at Speed
Big Data at Speedmarkgrover
 
How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2....
How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2....How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2....
How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2....Databricks
 
Seamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflowSeamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflowDatabricks
 
Feature drift monitoring as a service for machine learning models at scale
Feature drift monitoring as a service for machine learning models at scaleFeature drift monitoring as a service for machine learning models at scale
Feature drift monitoring as a service for machine learning models at scaleNoriaki Tatsumi
 
Lessons Learned from Using Spark for Evaluating Road Detection at BMW Autonom...
Lessons Learned from Using Spark for Evaluating Road Detection at BMW Autonom...Lessons Learned from Using Spark for Evaluating Road Detection at BMW Autonom...
Lessons Learned from Using Spark for Evaluating Road Detection at BMW Autonom...Databricks
 
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache SparkProcessing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache SparkDatabricks
 
Model versioning done right: A ModelDB 2.0 Walkthrough
Model versioning done right: A ModelDB 2.0 WalkthroughModel versioning done right: A ModelDB 2.0 Walkthrough
Model versioning done right: A ModelDB 2.0 WalkthroughManasi Vartak
 
Machine Learning Models in Production
Machine Learning Models in ProductionMachine Learning Models in Production
Machine Learning Models in ProductionDataWorks Summit
 
Apache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning Models
Apache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning ModelsApache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning Models
Apache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning ModelsAnyscale
 
Patterns and Anti-Patterns for Memorializing Data Science Project Artifacts
Patterns and Anti-Patterns for Memorializing Data Science Project ArtifactsPatterns and Anti-Patterns for Memorializing Data Science Project Artifacts
Patterns and Anti-Patterns for Memorializing Data Science Project ArtifactsDatabricks
 
MLOps - Build pipelines with Tensor Flow Extended & Kubeflow
MLOps - Build pipelines with Tensor Flow Extended & KubeflowMLOps - Build pipelines with Tensor Flow Extended & Kubeflow
MLOps - Build pipelines with Tensor Flow Extended & KubeflowJan Kirenz
 
Porting R Models into Scala Spark
Porting R Models into Scala SparkPorting R Models into Scala Spark
Porting R Models into Scala Sparkcarl_pulley
 
Data Quality With or Without Apache Spark and Its Ecosystem
Data Quality With or Without Apache Spark and Its EcosystemData Quality With or Without Apache Spark and Its Ecosystem
Data Quality With or Without Apache Spark and Its EcosystemDatabricks
 
IncQuery Group's presentation for the INCOSE Polish Chapter 20220310
IncQuery Group's presentation for the INCOSE Polish Chapter 20220310IncQuery Group's presentation for the INCOSE Polish Chapter 20220310
IncQuery Group's presentation for the INCOSE Polish Chapter 20220310IncQuery Labs
 
IncQuery-D: Incremental Queries in the Cloud
IncQuery-D: Incremental Queries in the CloudIncQuery-D: Incremental Queries in the Cloud
IncQuery-D: Incremental Queries in the CloudGábor Szárnyas
 
MLOps with a Feature Store: Filling the Gap in ML Infrastructure
MLOps with a Feature Store: Filling the Gap in ML InfrastructureMLOps with a Feature Store: Filling the Gap in ML Infrastructure
MLOps with a Feature Store: Filling the Gap in ML InfrastructureData Science Milan
 
Phar Data Platform: From the Lakehouse Paradigm to the Reality
Phar Data Platform: From the Lakehouse Paradigm to the RealityPhar Data Platform: From the Lakehouse Paradigm to the Reality
Phar Data Platform: From the Lakehouse Paradigm to the RealityDatabricks
 
IncQuery Suite demo for INCOSE 2022IW
IncQuery Suite demo for INCOSE 2022IWIncQuery Suite demo for INCOSE 2022IW
IncQuery Suite demo for INCOSE 2022IWIncQuery Labs
 
Cloud-based Modelling Solutions Empowering Tool Integration
Cloud-based Modelling Solutions Empowering Tool IntegrationCloud-based Modelling Solutions Empowering Tool Integration
Cloud-based Modelling Solutions Empowering Tool IntegrationIstvan Rath
 
Extending Machine Learning Algorithms with PySpark
Extending Machine Learning Algorithms with PySparkExtending Machine Learning Algorithms with PySpark
Extending Machine Learning Algorithms with PySparkDatabricks
 
Building A Production-Level Machine Learning Pipeline
Building A Production-Level Machine Learning PipelineBuilding A Production-Level Machine Learning Pipeline
Building A Production-Level Machine Learning PipelineRobert Dempsey
 
宋慶齡 Soong-Ching-Ling
宋慶齡 Soong-Ching-Ling宋慶齡 Soong-Ching-Ling
宋慶齡 Soong-Ching-Lingleechest
 
Photo Essay
Photo EssayPhoto Essay
Photo Essayjromanchak526
 

More Related Content

What's hot

How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2....
How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2....How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2....
How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2....Databricks
 
Seamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflowSeamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflowDatabricks
 
Feature drift monitoring as a service for machine learning models at scale
Feature drift monitoring as a service for machine learning models at scaleFeature drift monitoring as a service for machine learning models at scale
Feature drift monitoring as a service for machine learning models at scaleNoriaki Tatsumi
 
Lessons Learned from Using Spark for Evaluating Road Detection at BMW Autonom...
Lessons Learned from Using Spark for Evaluating Road Detection at BMW Autonom...Lessons Learned from Using Spark for Evaluating Road Detection at BMW Autonom...
Lessons Learned from Using Spark for Evaluating Road Detection at BMW Autonom...Databricks
 
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache SparkProcessing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache SparkDatabricks
 
Model versioning done right: A ModelDB 2.0 Walkthrough
Model versioning done right: A ModelDB 2.0 WalkthroughModel versioning done right: A ModelDB 2.0 Walkthrough
Model versioning done right: A ModelDB 2.0 WalkthroughManasi Vartak
 
Machine Learning Models in Production
Machine Learning Models in ProductionMachine Learning Models in Production
Machine Learning Models in ProductionDataWorks Summit
 
Apache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning Models
Apache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning ModelsApache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning Models
Apache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning ModelsAnyscale
 
Patterns and Anti-Patterns for Memorializing Data Science Project Artifacts
Patterns and Anti-Patterns for Memorializing Data Science Project ArtifactsPatterns and Anti-Patterns for Memorializing Data Science Project Artifacts
Patterns and Anti-Patterns for Memorializing Data Science Project ArtifactsDatabricks
 
MLOps - Build pipelines with Tensor Flow Extended & Kubeflow
MLOps - Build pipelines with Tensor Flow Extended & KubeflowMLOps - Build pipelines with Tensor Flow Extended & Kubeflow
MLOps - Build pipelines with Tensor Flow Extended & KubeflowJan Kirenz
 
Porting R Models into Scala Spark
Porting R Models into Scala SparkPorting R Models into Scala Spark
Porting R Models into Scala Sparkcarl_pulley
 
Data Quality With or Without Apache Spark and Its Ecosystem
Data Quality With or Without Apache Spark and Its EcosystemData Quality With or Without Apache Spark and Its Ecosystem
Data Quality With or Without Apache Spark and Its EcosystemDatabricks
 
IncQuery Group's presentation for the INCOSE Polish Chapter 20220310
IncQuery Group's presentation for the INCOSE Polish Chapter 20220310IncQuery Group's presentation for the INCOSE Polish Chapter 20220310
IncQuery Group's presentation for the INCOSE Polish Chapter 20220310IncQuery Labs
 
IncQuery-D: Incremental Queries in the Cloud
IncQuery-D: Incremental Queries in the CloudIncQuery-D: Incremental Queries in the Cloud
IncQuery-D: Incremental Queries in the CloudGábor Szárnyas
 
MLOps with a Feature Store: Filling the Gap in ML Infrastructure
MLOps with a Feature Store: Filling the Gap in ML InfrastructureMLOps with a Feature Store: Filling the Gap in ML Infrastructure
MLOps with a Feature Store: Filling the Gap in ML InfrastructureData Science Milan
 
Phar Data Platform: From the Lakehouse Paradigm to the Reality
Phar Data Platform: From the Lakehouse Paradigm to the RealityPhar Data Platform: From the Lakehouse Paradigm to the Reality
Phar Data Platform: From the Lakehouse Paradigm to the RealityDatabricks
 
IncQuery Suite demo for INCOSE 2022IW
IncQuery Suite demo for INCOSE 2022IWIncQuery Suite demo for INCOSE 2022IW
IncQuery Suite demo for INCOSE 2022IWIncQuery Labs
 
Cloud-based Modelling Solutions Empowering Tool Integration
Cloud-based Modelling Solutions Empowering Tool IntegrationCloud-based Modelling Solutions Empowering Tool Integration
Cloud-based Modelling Solutions Empowering Tool IntegrationIstvan Rath
 
Extending Machine Learning Algorithms with PySpark
Extending Machine Learning Algorithms with PySparkExtending Machine Learning Algorithms with PySpark
Extending Machine Learning Algorithms with PySparkDatabricks
 
Building A Production-Level Machine Learning Pipeline
Building A Production-Level Machine Learning PipelineBuilding A Production-Level Machine Learning Pipeline
Building A Production-Level Machine Learning PipelineRobert Dempsey
 

What's hot (20)

How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2....
How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2....How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2....
How to Productionize Your Machine Learning Models Using Apache Spark MLlib 2....
 
Seamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflowSeamless MLOps with Seldon and MLflow
Seamless MLOps with Seldon and MLflow
 
Feature drift monitoring as a service for machine learning models at scale
Feature drift monitoring as a service for machine learning models at scaleFeature drift monitoring as a service for machine learning models at scale
Feature drift monitoring as a service for machine learning models at scale
 
Lessons Learned from Using Spark for Evaluating Road Detection at BMW Autonom...
Lessons Learned from Using Spark for Evaluating Road Detection at BMW Autonom...Lessons Learned from Using Spark for Evaluating Road Detection at BMW Autonom...
Lessons Learned from Using Spark for Evaluating Road Detection at BMW Autonom...
 
Processing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache SparkProcessing Large Datasets for ADAS Applications using Apache Spark
Processing Large Datasets for ADAS Applications using Apache Spark
 
Model versioning done right: A ModelDB 2.0 Walkthrough
Model versioning done right: A ModelDB 2.0 WalkthroughModel versioning done right: A ModelDB 2.0 Walkthrough
Model versioning done right: A ModelDB 2.0 Walkthrough
 
Machine Learning Models in Production
Machine Learning Models in ProductionMachine Learning Models in Production
Machine Learning Models in Production
 
Apache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning Models
Apache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning ModelsApache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning Models
Apache ® Spark™ MLlib 2.x: How to Productionize your Machine Learning Models
 
Patterns and Anti-Patterns for Memorializing Data Science Project Artifacts
Patterns and Anti-Patterns for Memorializing Data Science Project ArtifactsPatterns and Anti-Patterns for Memorializing Data Science Project Artifacts
Patterns and Anti-Patterns for Memorializing Data Science Project Artifacts
 
MLOps - Build pipelines with Tensor Flow Extended & Kubeflow
MLOps - Build pipelines with Tensor Flow Extended & KubeflowMLOps - Build pipelines with Tensor Flow Extended & Kubeflow
MLOps - Build pipelines with Tensor Flow Extended & Kubeflow
 
Porting R Models into Scala Spark
Porting R Models into Scala SparkPorting R Models into Scala Spark
Porting R Models into Scala Spark
 
Data Quality With or Without Apache Spark and Its Ecosystem
Data Quality With or Without Apache Spark and Its EcosystemData Quality With or Without Apache Spark and Its Ecosystem
Data Quality With or Without Apache Spark and Its Ecosystem
 
IncQuery Group's presentation for the INCOSE Polish Chapter 20220310
IncQuery Group's presentation for the INCOSE Polish Chapter 20220310IncQuery Group's presentation for the INCOSE Polish Chapter 20220310
IncQuery Group's presentation for the INCOSE Polish Chapter 20220310
 
IncQuery-D: Incremental Queries in the Cloud
IncQuery-D: Incremental Queries in the CloudIncQuery-D: Incremental Queries in the Cloud
IncQuery-D: Incremental Queries in the Cloud
 
MLOps with a Feature Store: Filling the Gap in ML Infrastructure
MLOps with a Feature Store: Filling the Gap in ML InfrastructureMLOps with a Feature Store: Filling the Gap in ML Infrastructure
MLOps with a Feature Store: Filling the Gap in ML Infrastructure
 
Phar Data Platform: From the Lakehouse Paradigm to the Reality
Phar Data Platform: From the Lakehouse Paradigm to the RealityPhar Data Platform: From the Lakehouse Paradigm to the Reality
Phar Data Platform: From the Lakehouse Paradigm to the Reality
 
IncQuery Suite demo for INCOSE 2022IW
IncQuery Suite demo for INCOSE 2022IWIncQuery Suite demo for INCOSE 2022IW
IncQuery Suite demo for INCOSE 2022IW
 
Cloud-based Modelling Solutions Empowering Tool Integration
Cloud-based Modelling Solutions Empowering Tool IntegrationCloud-based Modelling Solutions Empowering Tool Integration
Cloud-based Modelling Solutions Empowering Tool Integration
 
Extending Machine Learning Algorithms with PySpark
Extending Machine Learning Algorithms with PySparkExtending Machine Learning Algorithms with PySpark
Extending Machine Learning Algorithms with PySpark
 
Building A Production-Level Machine Learning Pipeline
Building A Production-Level Machine Learning PipelineBuilding A Production-Level Machine Learning Pipeline
Building A Production-Level Machine Learning Pipeline
 

Viewers also liked

宋慶齡 Soong-Ching-Ling
宋慶齡 Soong-Ching-Ling宋慶齡 Soong-Ching-Ling
宋慶齡 Soong-Ching-Lingleechest
 
Phase 1 Compliance
Phase 1 CompliancePhase 1 Compliance
Phase 1 Compliancejwichelt
 
Getting The Runs On The Board One Run At A Time V5
Getting The Runs On The Board One Run At A Time V5Getting The Runs On The Board One Run At A Time V5
Getting The Runs On The Board One Run At A Time V5David Waters
 
A Voltaao Mundo(W)
A Voltaao Mundo(W)A Voltaao Mundo(W)
A Voltaao Mundo(W)minedu
 
Oop! iLife
Oop! iLifeOop! iLife
Oop! iLifeiLife
 
Proposal m pendidikan islam upsi 2011
Proposal m pendidikan islam upsi 2011Proposal m pendidikan islam upsi 2011
Proposal m pendidikan islam upsi 2011Sanofi
 
PEMULIHAN JAWI j QAF
PEMULIHAN JAWI j QAFPEMULIHAN JAWI j QAF
PEMULIHAN JAWI j QAFSanofi
 

Viewers also liked (10)

宋慶齡 Soong-Ching-Ling
宋慶齡 Soong-Ching-Ling宋慶齡 Soong-Ching-Ling
宋慶齡 Soong-Ching-Ling
 
Photo Essay
Photo EssayPhoto Essay
Photo Essay
 
XenServer und Storage
XenServer und StorageXenServer und Storage
XenServer und Storage
 
Wp1011 Citrix Apple V004
Wp1011 Citrix Apple V004Wp1011 Citrix Apple V004
Wp1011 Citrix Apple V004
 
Phase 1 Compliance
Phase 1 CompliancePhase 1 Compliance
Phase 1 Compliance
 
Getting The Runs On The Board One Run At A Time V5
Getting The Runs On The Board One Run At A Time V5Getting The Runs On The Board One Run At A Time V5
Getting The Runs On The Board One Run At A Time V5
 
A Voltaao Mundo(W)
A Voltaao Mundo(W)A Voltaao Mundo(W)
A Voltaao Mundo(W)
 
Oop! iLife
Oop! iLifeOop! iLife
Oop! iLife
 
Proposal m pendidikan islam upsi 2011
Proposal m pendidikan islam upsi 2011Proposal m pendidikan islam upsi 2011
Proposal m pendidikan islam upsi 2011
 
PEMULIHAN JAWI j QAF
PEMULIHAN JAWI j QAFPEMULIHAN JAWI j QAF
PEMULIHAN JAWI j QAF
 

Similar to Managing BI Objects in an ITIL Environment

Datastage Online Training @ Adithya Elearning
Datastage Online Training @ Adithya ElearningDatastage Online Training @ Adithya Elearning
Datastage Online Training @ Adithya Elearningshanmukha rao dondapati
 
Amit Kumar_Resume
Amit Kumar_ResumeAmit Kumar_Resume
Amit Kumar_ResumeAmit Kumar
 
M kawish siddiqui linkedin profile
M kawish siddiqui linkedin profileM kawish siddiqui linkedin profile
M kawish siddiqui linkedin profilekawish_siddiqui
 
Yuvaraj Shanmugam - Application Architect
Yuvaraj Shanmugam - Application ArchitectYuvaraj Shanmugam - Application Architect
Yuvaraj Shanmugam - Application ArchitectYuvaraj Shanmugam
 
Basha_ETL_Developer
Basha_ETL_DeveloperBasha_ETL_Developer
Basha_ETL_Developerbasha shaik
 
Architecting Agile Data Applications for Scale
Architecting Agile Data Applications for ScaleArchitecting Agile Data Applications for Scale
Architecting Agile Data Applications for ScaleDatabricks
 
Sf big analytics_2018_04_18: Evolution of the GoPro's data platform
Sf big analytics_2018_04_18: Evolution of the GoPro's data platformSf big analytics_2018_04_18: Evolution of the GoPro's data platform
Sf big analytics_2018_04_18: Evolution of the GoPro's data platformChester Chen
 
GeoKettle: A powerful open source spatial ETL tool
GeoKettle: A powerful open source spatial ETL toolGeoKettle: A powerful open source spatial ETL tool
GeoKettle: A powerful open source spatial ETL toolThierry Badard
 
rough-work.pptx
rough-work.pptxrough-work.pptx
rough-work.pptxsharpan
 
Sakthi Shenbagam - Data warehousing Consultant
Sakthi Shenbagam - Data warehousing ConsultantSakthi Shenbagam - Data warehousing Consultant
Sakthi Shenbagam - Data warehousing ConsultantSakthi Shenbagam
 
Online Datastage training
Online Datastage trainingOnline Datastage training
Online Datastage trainingchpriyaa1
 
Webinar september 2013
Webinar september 2013Webinar september 2013
Webinar september 2013Marc Gille
 
SQL Server 2008 Integration Services
SQL Server 2008 Integration ServicesSQL Server 2008 Integration Services
SQL Server 2008 Integration ServicesEduardo Castro
 

Similar to Managing BI Objects in an ITIL Environment (20)

Resume_Gulley_Oct7_2016
Resume_Gulley_Oct7_2016Resume_Gulley_Oct7_2016
Resume_Gulley_Oct7_2016
 
Datastage Online Training @ Adithya Elearning
Datastage Online Training @ Adithya ElearningDatastage Online Training @ Adithya Elearning
Datastage Online Training @ Adithya Elearning
 
Amit Kumar_Resume
Amit Kumar_ResumeAmit Kumar_Resume
Amit Kumar_Resume
 
M Kawish Siddiqui
M Kawish SiddiquiM Kawish Siddiqui
M Kawish Siddiqui
 
M kawish siddiqui linkedin profile
M kawish siddiqui linkedin profileM kawish siddiqui linkedin profile
M kawish siddiqui linkedin profile
 
Rahul_Vijay_Gosewade
Rahul_Vijay_GosewadeRahul_Vijay_Gosewade
Rahul_Vijay_Gosewade
 
Yuvaraj Shanmugam - Application Architect
Yuvaraj Shanmugam - Application ArchitectYuvaraj Shanmugam - Application Architect
Yuvaraj Shanmugam - Application Architect
 
Basha_ETL_Developer
Basha_ETL_DeveloperBasha_ETL_Developer
Basha_ETL_Developer
 
Architecting Agile Data Applications for Scale
Architecting Agile Data Applications for ScaleArchitecting Agile Data Applications for Scale
Architecting Agile Data Applications for Scale
 
smrutikanta jena
smrutikanta jenasmrutikanta jena
smrutikanta jena
 
Sf big analytics_2018_04_18: Evolution of the GoPro's data platform
Sf big analytics_2018_04_18: Evolution of the GoPro's data platformSf big analytics_2018_04_18: Evolution of the GoPro's data platform
Sf big analytics_2018_04_18: Evolution of the GoPro's data platform
 
GeoKettle: A powerful open source spatial ETL tool
GeoKettle: A powerful open source spatial ETL toolGeoKettle: A powerful open source spatial ETL tool
GeoKettle: A powerful open source spatial ETL tool
 
rough-work.pptx
rough-work.pptxrough-work.pptx
rough-work.pptx
 
Sakthi Shenbagam - Data warehousing Consultant
Sakthi Shenbagam - Data warehousing ConsultantSakthi Shenbagam - Data warehousing Consultant
Sakthi Shenbagam - Data warehousing Consultant
 
Ganesh CV
Ganesh CVGanesh CV
Ganesh CV
 
Online Datastage training
Online Datastage trainingOnline Datastage training
Online Datastage training
 
Resume - RK
Resume - RKResume - RK
Resume - RK
 
Webinar september 2013
Webinar september 2013Webinar september 2013
Webinar september 2013
 
SQL Server 2008 Integration Services
SQL Server 2008 Integration ServicesSQL Server 2008 Integration Services
SQL Server 2008 Integration Services
 
Jayachandran_Resume
Jayachandran_ResumeJayachandran_Resume
Jayachandran_Resume
 

Managing BI Objects in an ITIL Environment