Learn SAP Business Intelligence

SAP BW/BI Beginners : 3 Months Comprehensive Learning Schedule

2023-07-19T19:44:00.001-07:00

Month 1: Laying the Foundation

Week 1: Introduction to SAP BW BI

Day 1: Introduction to SAP BW and its role in Business Intelligence.
Day 2: Understanding the SAP BW architecture and components.
Day 3: Navigating the SAP BW user interface and basic operations.
Day 4: Overview of SAP BW data modeling concepts.
Day 5: Creating your first InfoArea, InfoObject, and DataStore Object (DSO).

Week 2: Data Extraction and Loading

Day 6: Exploring DataSources and data extraction techniques.
Day 7: Configuring standard extractors and enhancing DataSources.
Day 8: Implementing transformations and data loading.
Day 9: Understanding DataStore Objects (DSOs) and InfoCubes.
Day 10: Loading data into DSOs and InfoCubes.

Week 3: Querying Data in SAP BW

Day 11: Creating queries using the SAP BW Query Designer.
Day 12: Enhancing queries with filters, calculated key figures, and variables.
Day 13: Introduction to BEx Analyzer for reporting.
Day 14: Designing basic reports and dashboards.
Day 15: Practicing query design and report generation.

Week 4: Data Visualization and Reporting

Day 16: Exploring advanced reporting options in SAP BW.
Day 17: Creating Web Application Designer (WAD) reports and applications.
Day 18: Introduction to SAP BusinessObjects integration.
Day 19: Designing interactive dashboards with SAP BusinessObjects.
Day 20: Reviewing and summarizing the concepts covered in Month 1.

Month 2: Advanced Topics and Integration

Week 5: Performance Optimization and Security

Day 21: SAP BW performance optimization techniques.
Day 22: Understanding SAP BW security and user management.
Day 23: Data compression and partitioning for improved performance.
Day 24: Managing aggregates and indexes in SAP BW.
Day 25: Best practices for maintaining system performance.

Week 6: SAP BW on SAP HANA

Day 26: Introduction to SAP BW on SAP HANA.
Day 27: Migrating and optimizing SAP BW for SAP HANA.
Day 28: Accelerating data loading with SAP HANA.
Day 29: Building SAP HANA-based InfoCubes and DSOs.
Day 30: Analyzing data in SAP BW on SAP HANA.

Month 3: Advanced Modeling and Real-World Practice

Week 7: Advanced Data Modeling

Day 31: Hierarchies and attributes in InfoObjects.
Day 32: Advanced DataStore Object (aDSO) modeling.
Day 33: Implementing HybridProviders and CompositeProviders.
Day 34: Introduction to SAP BW Integrated Planning.
Day 35: Designing planning scenarios and planning functions.

Week 8: SAP BW Extractors and Advanced Transformations

Day 36: Working with custom extractors and function modules.
Day 37: SAP BW extractors for SAP Business Suite applications.
Day 38: Advanced transformations using ABAP routines.
Day 39: Data transfer process (DTP) and process chains.
Day 40: Monitoring and troubleshooting data loading.

Week 9-12: Real-World Practice and Project

Day 41-80: Work on real-world scenarios and projects.
Day 81-90: Review, revise, and reinforce your learning.
Day 91: Summary of your SAP BW BI learning journey.

Throughout this three-month learning schedule, make use of online resources, tutorials, and the SAP community to supplement your learning and seek guidance when needed. SAP BW BI is a vast and dynamic field, so continuous practice and exploration will be key to mastering it. Good luck on your SAP BW BI learning journey!

SAP BW for Beginners: A Comprehensive Guide

2023-07-19T19:36:00.000-07:00

Introduction:

Welcome to the world of SAP BW (Business Warehouse)! If you're new to SAP BW and eager to learn about this powerful data warehousing solution, you've come to the right place. In this blog, we'll take you through the basics of SAP BW, its key concepts, and how it fits into the larger landscape of data analytics and reporting. By the end of this guide, you'll have a solid foundation to start your journey with SAP BW.

1. Introduction to SAP BW: SAP BW, short for Business Warehouse, serves as an essential data warehousing tool for businesses to manage, consolidate, and centralize their data from various sources. This section will provide an overview of SAP BW, its purpose, and why organizations use it to facilitate better decision-making.

2. Key Components of SAP BW: To understand SAP BW thoroughly, it's essential to explore its key components. We'll discuss Data Sources, InfoObjects, InfoCubes, DataStore Objects (DSOs), and more, explaining their significance and how they interact to form a comprehensive data warehousing system.

3. Data Modeling in SAP BW: Data modeling forms the core of SAP BW. This section will cover data modeling basics, including creating InfoObjects, InfoAreas, and DataSources. Additionally, we'll explore the concept of Extract, Transform, Load (ETL) and its implementation in SAP BW.

4. Extracting Data with SAP BW: Data extraction from various source systems is a crucial step in the process. We'll guide you through the data extraction process using standard extractors, custom extractors, and different types of DataSources.

5. Data Transformation and Loading: Once data is extracted, it needs to be transformed and loaded into the data warehouse. Here, we'll explain the transformation process, different types of transformations, and loading data into InfoCubes and DSOs.

6. Querying Data in SAP BW: With the data in the data warehouse, running queries and creating reports becomes essential. We'll explore SAP BW's query designer and demonstrate how to build queries to extract valuable insights from your data.

7. Data Visualization with SAP BW: Data becomes meaningful when presented visually. In this section, we'll discuss creating compelling data visualizations and dashboards using SAP BW tools and the Business Explorer (BEx) suite.

8. Performance Optimization: To ensure SAP BW runs efficiently, performance optimization is crucial. We'll provide you with tips and best practices to enhance the performance of your SAP BW system.

9. Integration with Other SAP Tools: SAP BW is a part of the larger SAP ecosystem. We'll explore how SAP BW integrates with other SAP solutions like SAP BusinessObjects and SAP HANA to provide a comprehensive business intelligence landscape.

10. Common Challenges and Troubleshooting: As a beginner, you might encounter certain challenges while working with SAP BW. We'll address some common issues and provide troubleshooting tips to overcome them.

Conclusion: Congratulations! You've have taken first step towards SAP BW learning. We hope this blog has given you a solid foundation to start your journey with SAP BW. Remember, mastering SAP BW takes time and practice, so don't be discouraged by any initial challenges. Embrace the learning process, and soon you'll be harnessing the full potential of SAP BW to make data-driven decisions that drive your business forward.

InfoSource

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An InfoSource combines quantity of information that logically belongs together, summarized into a single unit. It prepares consolidated data for updating to the data targets. InfoSources contain either transaction data or master data (attributes, texts and hierarchies).

When you want to load the related data from different datasources to the data targets, Infosource comes into picture.

Role of InfoSource in SAP BW-

There is a difference in use of InfoSource between BW and BI.

In BW, a DataSource is assigned to an InfoSource. If fields that logically belong together exist in various source systems, they can be grouped together into a single InfoSource in BW, in which multiple DataSources can be assigned to an InfoSource.

In the BW Processing Transfer Rules, individual DataSource fields are assigned to the corresponding InfoObject of the InfoSource. Here you can also determine how the data for a DataSource can actually be transferred to the InfoSource. The uploaded data is transformed using transfer rules. An extensive library of transformation functions that contain business logic can be used here to perform data cleansing and to make the data analyzable.

The transfer structure is used to transfer data to the BW system. The data is transferred 1:1 from the transfer structure of the source system into the BW transfer structure.

If you are dealing with an InfoSource with flexible updating, then the data is updated from the communication structure into the InfoCube into other data targets with the aid of the Update Rules. InfoSources with direct updating permit master data to be written directly (without update rules) into the master data tables.

Role of infosource in SAP BI-

In contrast to 3.x InfoSources, as of Release SAP NetWeaver BI 7.0, an InfoSource behaves like an InfoSource with flexible update

The data in an InfoSource is updated to an InfoProvider using a transformation.

You can define the InfoObjects of the InfoSource as keys. These keys are used to aggregate the data records during the transformation.

1. Data Flow Without an InfoSource:

The DataSource is connected directly to the target by means of a transformation.

Since there is only one transformation, performance is better.

However, if you want to connect multiple DataSources with the same structure to the target, this can result in additional maintenance effort for the transformation, since you need to create a similar transformation for each DataSource.

You can avoid this if the DataSource is the same, it just appears in different source systems. In this case, you can use source system mapping when you transport to the target system so that only one transformation has to be maintained in the test system. The same transformation is created automatically for each source system in the production system.

2. Data Flow with One InfoSource

The DataSource is connected to the target by means of an InfoSource. There is one transformation between the DataSource and the InfoSource and one transformation between the InfoSource and the target.

We recommend that you use an InfoSource if you want to connect a number of different DataSources to a target and the different DataSources have the same business rules. In the transformation, you can align the format of the data in DataSource with the format of the data in the InfoSource. The required business rules are applied in the subsequent transformation between the InfoSource and the target. You can make any changes to these rules centrally in this one transformation, as required.

3. Data Flow with Two InfoSources

We recommend that you use this type of data flow if your data flow not only contains two different sources, but the data is to be written to multiple identical (or almost identical) targets. The required business rules are executed in the central transformation so that you only have to modify the one transformation in order to change the business rules. You can connect sources and targets that are independent of this transformation.

Data Extraction

2013-04-22T09:18:00.004-07:00

Data Extraction (Loading data from OLTP servers to OLAP (BW/BI) servers)
OLTP server stores daily transactions data. For a bank all the transactions related to money debit/credit to any account, loan, employee related data etc will come under transactional data. They are used to support real time business. But for analysis purpose we do not need all data at each transaction level. For this purpose we load data from transactional servers to BW/BI Servers. We extract data/fields which are necessary for the analysis of the business and will help in taking business related decisions.

You may load transactional data to BW/BI servers on daily basis/weekly basis/monthly basis. Frequency of loading or refreshing the data depend on the criticality of the decision taken based on that data. If any decision which is very critical and can impact short term business decisions, we need to refresh the data on daily basis.

Data Extraction Process

Basis of extraction is OLTP extraction tables. So R/3 OLTP source system must be replicated to BW. To replicate the Metadata from a source system into BW for an application component, choose Source System Tree->Required Source System->DataSource Overview ->The user Application Components ->ContextMenu (right mouse click)->Replicate DataSources in the BW Administrator Workbench.

To update all the Metadata of a source system-Choose Source System Tree requires

Source System ->Context Menu (right mouse click)->Replicate DataSources in the BW

Administrator Workbench

Replicate the data source to BW server

Go to transaction code RSA3-> Check data is available related to your Data Source.

If yes-> Go to transaction code LBWG (Delete Setup data) -> Delete the data by entering the application name.

Go to transaction SBIW --> Settings for Application Specific Data source --> Logistics --> Managing extract structures --> Initialization --> Filling the Setup table --> Application specific setup of statistical data --> perform setup (relevant application)

Persistent Staging Area (PSA)

2011-06-09T00:39:00.000-07:00

The persistent staging area (PSA), the entry layer for data in BI.

A transparent PSA table is created for every DataSource that is activated. The PSA tables each have the same structure as their respective DataSource. They are also flagged with key fields for the request ID, the data package number, and the data record number.

InfoPackages load the data from the source into the PSA. The data from the PSA is processed with data transfer processes.

With the context menu entry Manage for a DataSource in the Data Warehousing Workbench you can go to the PSA maintenance for data records of a request or delete request data from the PSA table of this DataSource. You can also go to the PSA maintenance from the monitor for requests of the load process.

Using partitioning, you can separate the dataset of a PSA table into several smaller, physically independent, and redundancy-free units. This separation can mean improved performance when you update data from the PSA.

Constraints

The number of fields is limited to a maximum of 255 when using TRFCs to transfer data. The length of the data record is limited to 1962 bytes when you use TRFCs.

RSDSSEG: Data Source and corresponding PSA table
RSDODSO: Directory of all Data store

How to find PSA table

1). Go to RSA1 -> Select the Data Source for which you want to know PSA table name

2) Go to Goto -> Technical attributes

3) Here in PSA properties you can find PSA table name.

4) Now Go to SE16 and write the name of PSA table and look the content of it.

How to find table space name corresponding to a PSA table

1)Go to SE11 and write the PSA table name -> F8

2) Now Go to Utilities -> Display content -> Display Utilities

3) Go to storage parameter

Here you can find the table space corresponding to the PSA table uxkSegQisGHytmfbVO_QHbSanv8

Data Transfer Process

2011-06-02T14:51:00.000-07:00

Data Transfer Process is a new concept in SAP BI(BW 7.0) . It was not in BW3.5. In SAP BW 3.5 we use Infopackage for the loading of data upto data targets but now in SAP BI we can load till only PSA through infopackage uxkSegQisGHytmfbVO_QHbSanv8.

From PSA to Data Target loading is done through Data Transfer Process.

The data transfer process makes the transfer processes in the data warehousing layer more transparent. Optimized parallel processing improves the performance of the transfer process .You can use the data transfer process to separate delta processes for different targets and you can use filter options between the persistent objects on various levels. For example, you can use filters between a Data Store object and an Info Cube
Data transfer processes are used for standard data transfer, for real-time data acquisition and for accessing data directly.

Features of DTP

Loading data from one layer to others except Info sources.
Separation of delta mechanism for different data targets.
Enhanced filtering in dataflow.
Improved transparency of staging processes across data warehouse layers.
Improved performance : optimized parallelization
Enhanced error handling in the form of error stack
Enables real-time data acquisition.

Most important feature of DTP is easy error handling process.
Process#1 Enhanced Filtering, Debugging and error handling options

Process # 2 -Handling Data Records With Errors

Using the error handling settings on the Update tab page in the data transfer process, when data is transferred from a DTP source to a DTP target, you can specify how the system is to react if errors occur in the data records.
These settings were previously made in the Info Package. When using data transfer processes, Info Packages write to the PSA only. Error handling settings are therefore no longer made in the Info Package, but in the data transfer process

Process # 3 -Error Handling Features

Possibility to choose in the scheduler to
Abort process when errors occur
Process the correct records but do not allow reporting on them
Process the correct records and allow reporting on them
Number of wrong records which lead to a wrong request
Invalid records can be written into an error stack
Keys should be defined for error stack to enable the error handling of data store object
Temporary data storage can be switched on/off for each sub step of the loading process
Invalid records can be updated into data records after their correction

Process # 4 - Error Stack

Stores erroneous records
Keeps the right sequence of records à for consistent data store handling.
Key of error stack defines which data should be detained from the update after the erroneous data record.
After Correction, Error-DTP updates data from error stack to data target.

Note: Once the request in the source object is deleted, the related data records in error stack area automatically deleted.

Creating Data Transfer Processes from the Object Tree in the Data Warehousing Workbench

1) In the Data Warehousing Workbench, an object tree is displayed and select the data target object.

2) In the context menu, choose Create Data Transfer Process.

The dialog box for creating a data transfer process appears.

Proceed as described in step three and onwards in the procedure for creating a data transfer process using a process chain.

Creating Data Transfer Processes Using Process Chains

1) Use Drag&Drop or double-click to include the process in the process chain.

2) To create a data transfer process as a new process variant, enter a technical name and choose Create. The dialog box for creating a data transfer process appears.

3) Select the type of data transfer process:

For VirtualProviders, the only available option is DTP for Direct Access.

For DataStore objects, choose Standard DTP and DTP for Real-Time Data Acquisition.

. 4) Select the source object type and the object from which you want to transfer data into the target. If only one source object exists, this is selected by default.

5) Choose Continue.

6) The data transfer process maintenance screen appears.

The header data for the data transfer process shows the description, ID, version, and status of the data transfer process, along with the delta status.

7) On the Extraction tab page, determine the parameters:

a. Choose the extraction mode.

You can choose full mode or delta mode. You do not need to initialize the delta process explicitly for the delta transfer.

b. If necessary, determine filter criteria for the delta transfer.

This means that you can use multiple data transfer processes with disjunctive selection conditions to transfer small sets of data from a source efficiently into one or more targets, instead of transferring large volumes of data. You can specify individual selections, multiple selections, intervals, selections based on variables, or routines. Choose Change Selection to change the list of InfoObjects that can be selected.

c. Apply further settings, which are dependent on the source object.

8) On the Update tab page, determine the parameters:

a. Apply settings for error handling: Determine how the system updates the valid records when errors occur, the number of errors allowed before the load process terminates, whether the load process should be judged to contain errors if records are being aggregated, sorted, or added during the transformation.

b. Under Semantic Groups determine the key fields for the error stack.

9) Make the settings for the temporary storage by choosing Goto ® Settings for DTP Temporary Storage. Here you determine the steps in the process run of the program after which you want to store data to the temporary storage, the level of detail to which you want to store data, and when you next want to delete the temporary storage.

10) On the Execute tab page, determine the parameters:

On this tab page, the process flow of the program for the data transfer process is displayed in a tree structure.

a. Specify the status that you want the system to adopt for the request if warnings are displayed in the log.

b. Specify how you want the system to determine the overall status of the request.

The system automatically determines the processing mode for the background processing of the respective data transfer process.

11) Check the data transfer process, save and activate it.

Comman Errors While loading through DTP

A DTP can be failed due to follwing reasons-
1. Errorneous records in many data packets
2. Short dump error

we are having to option to correct the request-
1. Repair
2. Repeat

If request is red in the data target due to short dump error and no data consistency issue we can repair the existing failed load.
Or if there are errorneous record in PSA then we can edit them in and then repair the failed load.

If request is red in the data target due to any data consistency issue then first you will have to delete the request from the data targets and then repeat the DTP.

Why we need Business Intelligence

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As we can see today technology is growing very fast.With that competion in the industry is also increasing even at a more faster rate than india's population. If we want to survive in this competetive environment, we need to take quick decisions. Decesions can only be taken if we are able to see facts and figures cleary and quickly.
That is where Business Intelligence (SAP-BI) comes into picture. uxkSegQisGHytmfbVO_QHbSanv8

With the help of Business Intelligence we are able to see the business data in the format that helps us our present condition, and we can compare it with the market. So we can say Business Intelligence is a type of analysis done to improve overall business productivity.