SAP BODS 4.2

SAP Data Services delivers a single enterprise-class solution for data integration, data quality, data profiling, and text data processing that allows you to integrate, transform, improve, and deliver trusted data to critical business processes.

Pre-Requisites:

Knowledge of data warehousing and ETL concept
Experience with SQL language
Data Warehouse Fundamentals
Data Warehouse Architecture and Modeling
Basics of SQL, Database & Data Warehouse

Duartion: 40 hours

Mode of Training: Live instructor –led hands on training

Course Contents

What is ERP in Data warehousing.
Architecture of Data warehousing.
OLTP vs OLAP.
Schemas in Data warehousing.

SAP DS Modules
SAP DS Versions
SAP DS Landscape
SAP DS Architecture
SAP DS Services
SAP DS ETL Flow
SAP DS vs SAP BW
SAP DS vs Other ETL Tools
SAP DS with HANA DB
SAP DS with BO
SAP DS with SAP ERP
SAP DS ServicesSAP DS Project overview

Create Data stores in DS
Connect to SQL Server DB and HANA
Copying data from SQL Server DB to HANA with Data services
Connecting to SAP HANA
Importing a flat file
Importing an XML file
Importing an Excel worksheet
Importing XSD schemas
Importing from SQL Server using ODBC
Importing from COBOL Copybook
Importing SAP ECC using Template Tables

Transforms & Category (DI,PF,DQ)
Data Integrator – Date Generation
Data Integrator – Pivot
Data Integrator – Reserve_Pivot
Data Integrator – Effective_Data
Data Integrator – Hierarchy_Flatterning
Data Integrator – Data Transfer
Data Integrator – XML_Pipeline
Data-Integrator – History _Preserving
Data-Integrator – Key _Generation
Platform – Case
Platform – Map_Operation
Platform – Merge
Platform – Query
Platform – SQL
Platform – Validation
Data Quality – Country2IDChar
Data Quality – GeoCoder
Data Quality – Global Address Cleanse
Data Quality – Data Cleanse
Data Quality – Match

BODS Admin Console-Administration
Real time Jobs, Embedded Data Flows
Substitution Parameters
Debugging Usage
System Configuration
BODS Performance Tuning Techniques

Error handling.

Try/Catch Techniques.

Conditional.

Implementation of if-else scenario.
Data Assessment.

Recovery Mechanisms

Job as recovery unit
Workflow as a recovery unit
Dataflow as a recovery unit

Variables and Parameters

Global Variables
Local Variables
Parameters and it’s types
Built-in and Custom functions

Data Assessment

Data Assessment using Data Profiler
Prerequisites to perform the Data Profiling
Types of Data Profiling
Importance of validation transform with Data Profiling

Multi-user Development and Environment setup

Defining Central repository
Difference between Central and Local repository
Difference between Secured and Non secure central repository
Activation of Central repository from local repository
Checking in, Checking out, Getting, Filtering,Checking,Labeling objects from central repository

Migrating Multi-User Jobs