Sumit Sarkar Real-time BO Universe to Cloud Data Sources Session #

Transcription

1 Sumit Sarkar Real-time BO Universe to Cloud Data Sources Session #

2 EXPERIENCE Talk to BI communities across SAP, Oracle, IBM, Microstrategy, Tableau, Tibco and Qlikview. Advocate for BI professionals at shows across Dreamforce, Hadoop Strata and MongoDBWorld Contributor to TDWI, Odata.org, Oracle Data Integration, Salesforce Developers, Progress Data Connections, and Microstrategy

3 LEARNING POINTS How to build a real-time Universe for cloud data Be the thought leader on cloud data sources, and other disruptive technologies

4 AGENDA Introduction to SAP Business Objects Cloud Universes Architecture options for Cloud Universes Best Practices and Lessons Learned

5 INTRO TO CLOUD UNIVERSE What is a Cloud Universe? Common Cloud Data Sources Common use cases in production

6 INTRO TO CLOUD UNIVERSE

7 INTRO TO CLOUD UNIVERSE COMMON SOURCES SaaS Salesforce Veeva CRM NetSuite ServiceNow Cloud9 WorkDay Tavant Kinaxis Rapid Response Cloud Databases Amazon Redshift SQL Server Azure Hosted DBs

8 INTRO TO CLOUD UNIVERSE COMMON USE CASES Salesforce reporting (native reporting inadequate) Migrating/Consolidating BI Platforms to Business Objects Real-time data blending in MSU to supplement the Data Warehouse with real-time Salesforce data Real-time Mobile Universe Web Intelligence

9 ARCHITECTURE OPTIONS FOR CLOUD UNIVERSE Real-time / Direct Data Warehouse Staging Database Hybrid (Real-time and Data Warehouse) Pros/Cons

10 ARCHITECTURE: REAL-TIME / DIRECT UNIVERSE

11 ARCHITECTURE: STAGING DATABASES UNIVERSE

12 ARCHITECTURE: DATA WAREHOUSE UNIVERSE

13 ARCHITECTURE: DATA WAREHOUSE & REAL-TIME UNIVERSE

14 ARCHITECTURE: PROS / CONS Real-time Direct Data Warehouse Staging Database DW and realtime (MSU) Self Service Y Rapid Development Y Real-time Y Y 360 view Y Y Local Connection Y Y Y

15 ARCHITECTURE: SaaS ODBC3 UNIVERSE CONNECTION

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20 BEST PRACTICES SaaS data sources are not relational databases or MPP warehouses (non-optimized joins) How to handle authentication Keeping up with the APIs Real-time versus ETL (MSU and SSU) Understand road map for new SaaS applications

21 BEST PRACTICES SaaS APIs VS DATABASE Determine if SaaS source has a query language What relationships are exposed between objects Capacity planning for larger in-memory operations LESSONS LEARNED Modeling Universe on top of unrelated objects from any SaaS application with large data volumes will be a challenge not really different from RDBMS.

22 BEST PRACTICES AUTHENTICATION Salesforce shops typically setup a common BI user Single Sign-On requirements LESSONS LEARNED How to delegate BOBJ SSO to Salesforce SSO?

23 BEST PRACTICES KEEPING UP WITH THE APIs Find out how often APIs change for your SaaS source Schema management for new objects/fields Refresh schema? Understand API call limits for 24 hour period LESSONS LEARNED Salesforce API changes quarterly and requires updates to connectors to support latest fields/objects. This is reason native connector with BODS does not work well.

24 BEST PRACTICES REAL-TIME VS ETL Understand the performance of the APIs What data volumes are required? LESSONS LEARNED Pulling very large data volumes in activity and lead records from Eloqua or Marketo for a real-time Universe is not practical.

25 BEST PRACTICES KNOW YOUR ROADMAP Demonstrate thought leadership by showing what SaaS sources you can support. Understand the SaaS BI landscape by department to compare contrast your services. LESSONS LEARNED Departments may not engage BOBJ group and duplicate BI efforts further fragmenting the data intelligence.

26 RESOURCES Blog tutorial to create a Salesforce Universe: Blog tutorial to create a Marketing Universe: Blog tutorial to integrate BO Data Services with Cloud Sources:

27 KEY LEARNINGS What is a BO Universe to Cloud Data What are commong data sources and use cases Contrasting the different BO Universe architectures for consuming cloud data sources Best practices and lessons learned

28 RETURN ON INVESTMENT Business Objects professionals are instantly productive consuming cloud data sources using existing skills Save time/costs for warehousing cloud data Avoid duplication and fragmentation of BI landscape by running all enterprise reporting through BO Universe

29 STAY Follow the ASUGNews team: Tom Chris Craig

30 SESSION CODE #####