OBIEE

Module 1 :

• OBIEE Overview
• OBIEE Architecture
• Clients
• OBIEE 11 BI Services
• OBIEE Weblogic Admin
• OBIEE Repository (rpd)
• Data Sources

Module 2 :

• OBIEE Components
• OBIEE Administration Tool
• OBIEE Analysis
• Intelligence Dashboards
• BI Publisher-BI Agents/Actions-KPI/Scorecard
• BI Office

Module 3 :

• Repository Basics
• Oracle Business Analysis Warehouse
• Weblogic Server
• Enterprise Manager
• Sample Request Processing
• Using the OracleBI Administration Tool
• Repository Directory
• Repository Files ( Initialization files and log files)
• Loading a repository into Oracle BI Server memory
• Creating a repository
• Connection Pool
• Repository Modes ( ONLINE and OFFLINE )
• Physical Layer
• Business Model & Mapping Layer
• Presentation Layer
• Presentation Catalogs, Folders and Columns
• Error Messages

Module 4 :

• Building the Physical Layer of a Repository
• Defining an ODBC System DSN
• Setting up connection pool properties
• Importing data sources ( Physical Schema )
• Verifying the import
• Defining physical keys and joins
• Creating alias and select tables

Module 5 :

• Building the Business Model and Mapping Layer of a Repository
• Creating the logical business model
• Creating the logical tables, columns, and sources
• Defining logical joins
• Adding Calculation to a Fact Table
• Building measures

Module 6 :

• Building the Presentation Layer of a Repository
• Creating a new Presentation Catalog
• Modifying Presentation layer objects
• Rename tables
• Reorder Table
• Testing and Validating a Repository
• Checking repository consistency
• Defining a repository in the initialization file
• Testing a repository using Oracle BI Answers
• Adding Multiple Logical Table Sources
• Adding multiple logical table sources to a logical table
• Specifying logical content
• Adding Calculations to a Fact Table
• Creating new calculation measures based on existing logical columns
• Creating new calculation measures based on physical columns
• Creating new calculation measures using the Calculation Wizard
• Creating Dimension Hierarchies and Level-Based Measures
• Creating dimension hierarchies
• Creating level
• Based measures

Module 7 :

• Creating & Using Aggregates
• Purpose of aggregate tables in dimensional modeling
• Modeling aggregate tables to improve query performance
• Testing aggregate navigation
• Using Initialization Blocks Repository Variables
• Creating Dynamic Repository Variables
• Execute Direct Database Requests
• Accessing database directly
• Creating Time series Measures
• Using time comparisons in business analysis
• Using Oracle BI time series functions to model time series data
• BI Analysis and Dash board & Publisher
• Creating and Editing Analysis
• Creating Interactive Dashboards using Analysis
• Creating query and chart

Module 8 :

• Working with Filters
• Working with Pivot Table
• Building Views and Charts in Requests
• Creating and Configuring Interactive Dashboards
• Creating and Configuring Dashboard Prompts
• Configuring Action Links
• Creating and understanding KPI,KPI Watchlists and Scorecards
• Oracle BI for MS Office
• Sending Alerts Using Delivers
• Using Job Manager

Meet Ananth Tirumanur. Hi there 👋

I work on projects in data science, big data, data engineering, data modeling, software engineering, and system design.

• 👨‍💻 All of my projects are available at https://github.com/akrish1982
• 💬 Ask me about Data engineering, SQL, Databases, Data pipelines, Data infrastructure
• 📄 My work history: https://www.linkedin.com/in/ananthtirumanur/
• ⚡ Fun fact: Marathoner & Casual Birding enthusiast

Connect with me:

• Twitter: @akrish82
• LinkedIn: https://linkedin.com/in/ananthtirumanur/

My Resources:

• LinkedIn Newsletter: Data Engineering with AWS
• Udemy Course: AWS Certified Data Engineer Associate Practice Test
• Python Crash Course: Python Crash Course on Gumroad

Languages and Tools:

AWS, Bash, Docker, Elasticsearch, Git, Grafana, Hadoop, Hive, EMR, Glue, Athena, Lambda, Step Functions, Airflow/MWAA, DynamoDB, Kafka, Kubernetes, Linux, MariaDB, MySQL, Pandas, PostgreSQL, Python, Redis, Scala, SQLite