How to Master Data Acquisition with SAP BW/4HANA

How to Master Data Acquisition with SAP BW/4HANA

How to Master Data Acquisition with SAP BW/4HANA

Data acquisition is the process of extracting, transforming, and loading (ETL) data from various sources into SAP BW/4HANA, the next-generation data warehouse solution from SAP. Data acquisition is a crucial step for building a reliable and consistent data foundation for analytics and reporting.

In this blog post, we will cover some of the key concepts and best practices for data acquisition with SAP BW/4HANA, such as:

  • The different types of source systems and DataSources that you can use to connect to SAP BW/4HANA
  • The simplified data flow and data management options in SAP BW/4HANA
  • The various methods and tools for data extraction, transformation, and loading
  • The benefits and challenges of real-time and big data integration

Source Systems and DataSources

A source system is a logical connection between a source of data, such as an SAP system, a database, a file, or a web service, and SAP BW/4HANA. You need to configure a source system in SAP BW/4HANA to enable data extraction from the source.

A DataSource is a metadata object that describes the structure and format of the source data. You need to create or activate a DataSource in SAP BW/4HANA to enable data transfer from the source system to the target InfoProvider.

SAP BW/4HANA supports various types of source systems and DataSources, such as:

  • ODP_SAP: This type of source system allows you to extract data from an SAP system using Operational Data Provisioning (ODP) framework. ODP_SAP DataSources are based on extractors that use application-specific logic to extract data from SAP tables or views.
  • ODP_CDS: This type of source system allows you to extract data from an SAP S/4HANA system using Core Data Services (CDS) views. ODP_CDS DataSources are based on CDS views that use SQL-like syntax to define data models on top of SAP HANA tables or views.
  • ODP_SLT: This type of source system allows you to extract data from any database using SAP Landscape Transformation (SLT) replication server. ODP_SLT DataSources are based on SLT replication configurations that use trigger-based mechanisms to replicate data from the source database to SAP HANA.
  • ODP_BW: This type of source system allows you to extract data from another SAP BW system using ODP framework. ODP_BW DataSources are based on InfoProviders or Open ODS views that provide data models in the source SAP BW system.
  • ODP_HANA: This type of source system allows you to extract data from an SAP HANA system using ODP framework. ODP_HANA DataSources are based on SAP HANA views or tables that provide data models in the source SAP HANA system.
  • HANA_LOCAL: This type of source system allows you to access data from the local SAP HANA database that underlies SAP BW/4HANA. HANA_LOCAL DataSources are based on native SQL scripts or procedures that query the local SAP HANA database directly.
  • HANA_SDA: This type of source system allows you to access data from a remote SAP HANA database using Smart Data Access (SDA) technology. HANA_SDA DataSources are based on virtual tables or views that use SDA adapters to connect to the remote SAP HANA database.
  • FILE: This type of source system allows you to load data from flat files, such as CSV or XML files. FILE DataSources are based on file structures that define the fields and separators of the flat files.
  • WEB_SERVICE: This type of source system allows you to load data from web services, such as SOAP or REST services. WEB_SERVICE DataSources are based on web service definitions that specify the URL, parameters, and response format of the web services.

Data Flow and Data Management

SAP BW/4HANA provides a simplified and flexible data flow that allows you to design and manage your data warehouse architecture. The data flow consists of three main components:

  • Source: This is where the data is extracted from the source systems using DataSources.
  • Transformation: This is where the data is transformed, cleansed, and integrated using transformations, routines, formulas, or rules.
  • Target: This is where the data is loaded into InfoProviders, such as Advanced DataStore Objects (ADSOs), Composite Providers, or Open ODS views.

SAP BW/4HANA also provides various options for data management, such as:

  • Data storage: You can choose between different storage options, such as columnar tables, row tables, or external tables, depending on your performance and cost requirements. You can also use features such as partitioning, compression, encryption, or archiving to optimize your data storage.
  • Data lifecycle: You can define different data temperatures, such as hot, warm, or cold, depending on the frequency and importance of data access. You can also use features such as data aging, nearline storage, or dynamic tiering to move data between different storage tiers according to the data temperature.
  • Data quality: You can use features such as semantic groups, data quality cockpit, or data quality monitor to ensure the quality and consistency of your data. You can also use features such as error handling, error stack, or error DTP to handle errors and exceptions during data loading.

Data Extraction, Transformation, and Loading

SAP BW/4HANA provides various methods and tools for data extraction, transformation, and loading (ETL), such as:

  • Data Transfer Process (DTP): This is the standard method for loading data from a DataSource to an InfoProvider. A DTP defines the selections and parameters for data transfer and loading, such as filter conditions, update mode, package size, or error handling. A DTP also uses transformations to apply transformation rules between the source and the target.
  • Process Chain: This is a tool for scheduling and monitoring the execution of ETL processes. A process chain consists of a sequence of process types that perform different tasks, such as executing a DTP, activating an ADSO, or running an ABAP program. A process chain can also define dependencies and triggers between different process types.
  • DataFlow: This is a tool for designing and managing the ETL processes in a graphical way. A DataFlow consists of a set of objects that represent the source systems, DataSources, transformations, InfoProviders, and DTPs. A DataFlow can also generate process chains automatically based on the dependencies between the objects.
  • BW Modeling Tools: This is a set of tools that are integrated in SAP HANA Studio or Eclipse platform. BW Modeling Tools allow you to create and edit the ETL objects in SAP BW/4HANA using graphical editors or wizards. BW Modeling Tools also provide features such as validation, activation, transport, or documentation of the ETL objects.

Real-Time and Big Data Integration

SAP BW/4HANA also supports real-time and big data integration scenarios that require high speed and high volume of data processing. Some of the options for real-time and big data integration are:

  • Real-Time Data Acquisition (RDA): This is a method for loading data from SAP systems to SAP BW/4HANA in near real-time. RDA uses delta queues in the source SAP system to collect and push data to SAP BW/4HANA at regular intervals. RDA also uses real-time DTPs to load data into InfoProviders without waiting for requests.
  • Smart Data Integration (SDI): This is a technology that allows you to access and integrate data from various sources using virtualization or replication techniques. SDI uses adapters to connect to different sources, such as databases, files, or web services. SDI also uses flowgraphs to define the ETL logic and tasks.
  • Smart Data Streaming (SDS): This is a technology that allows you to capture and analyze streaming data from various sources in real time. SDS uses adapters to connect to different sources, such as sensors, devices, or applications. SDS also uses streams and windows to define the streaming logic and analytics.
  • SAP Data Hub: This is a solution that allows you to orchestrate and govern the data flows across different systems and landscapes. SAP Data Hub uses pipelines to define the ETL logic and tasks using various operators. SAP Data Hub also provides features such as metadata management, data quality management, or machine learning.

Disclaimer : This content is generated by AI.

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