SAP BODS ( Business Objects Data Services )
- SAP BODS CONTENT Services engine – Data Services Access Server – Data Services Address Server – Data Services Administrator – Data Services Metadata Reports applications – Data Services
- Overview of Data Services
- Introduction of Data Services Architecture
- - Data Services Designer
- Data Services repository
- Data Services Job Server
- Data Services engine
- Data Services Access Server
- Data Services Address Server
- Data Services Administrator
- Data Services Metadata Reports applications
- Data Services Service
- Data Services SNMP Agent
- Data Services Adapter SDK
- Data Services SAP RAPID MARTS - Preparing to Install Data Services Client
/Server Components
- Pre-installation overview
- Installation scenarios - Repository Creation
- Repository database requirements and preparation
- Creating a Data Services repository and Selecting a repository
- Central versus local repository creation with live examples
- Using the Repository Manager.
- Multi-user Environment Setup
- Activating a central repository
- Implementing Central Repository Security
- Data Services and multiple users
- Security and the central repository
- Version Checking.
- Adding objects to the central repository Checking out - Logging into the Designer
- Project area
- Tool palette
- Workspace.
- Local object library
- Object editors and Working with objects - About Projects and Jobs
- Executing Jobs
- Overview of Data Services job execution
- Preparing for job execution
- Monitoring Jobs - Datastore creation and Overview
- Datastores and Data Flows — What is a data flow.
- Datastore and system configurations
- Multi-user Development
- Creating and managing multiple datastore configurations
- File Formats and what are file formats?
- File format editor ,Creating file formats and Editing file formats - Configure a Job Server
- Changing Job Server options
- Configure an Access Server
- To configure Metadata Integrator
- To select a web application server - Using the Server Manager
- Performing a scripted installation
- Logging in to the Management Console
- Connecting the Data Profiler
- Troubleshooting installation problems
- Running Data Services components in multi-user mode
- Publishing Data Services - Transformations and usage in data services
- Descriptions of transforms.
- Query transforms overview
- Data Quality transforms overview
- Lookup tables and the lookup_ext function
Data flow execution
- Creating and defining data flows
- Calling data flows to perform data movement operations
- Defining the conditions appropriate to run data flows
- Pass parameters to and from data flows
Work Flows and what is a work flow
- How to Creating a work flows
- Steps in a work flow and Order of execution in work flows - Creating real-time jobs
- Real-time source and target objects
- Testing real-time jobs
Overview of variables and parameters
- How to create Variables and Parameters.
- Using local variables and parameters and about global variables.
- Local and global variable rules. - Overview of data quality
- Address Cleanse transformation overview
- Data Cleanse.
- Match
- Design and Debug
- Using View Data to determine data quality
- Using the Validation transform. - Understanding changed-data capture
- Using CDC with Oracle sources
- Using CDC for targets/Sources - Data Services Management Console:
Administrator
- Scheduling, monitoring, and executing batch jobs
- Connecting repositories to the Administrator
- Configuring, starting, and stopping real-time services
- Configuring Job Server, Access Server, and repository usage
- Configuring and managing adapters
- Managing users
- Publishing batch jobs and real-time services via Web services - Functions and ProceduresAbout functions
- Descriptions of built-in functions
Raising Exceptions by usingTry/catch blocks
- Catch error functions and other function calls
- Nested try/catch blocks
- If statements to perform different actions for different exceptions - Job Scheduling using scripting and How to
use Scripts in BODS
- Data Services Scripting Language
- Python
- Python in Data Services - Batch Jobs
- Executing batch jobs, Scheduling jobs and Monitoring batch jobs
Using the Data Profiler
- Defining the profiler repository
- Column level profiling
- Detail profiling - Recovery Mechanisms.
- Recovering from unsuccessful job execution
- Automatically recovering jobs
- Manually recovering jobs using status tables
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