SAP Dara Services - Technical Architecture

SAP Data Services, formerly known as BusinessObjects Data Services (BODS), is an enterprise data integration, transformation, and quality tool that enables organizations to extract, transform, and load (ETL) data from various sources into their data warehouses, data lakes, and other repositories. It provides a comprehensive suite of features to manage data integration, data quality, and data profiling, helping businesses ensure their data is accurate, consistent, and available for analysis and reporting.

Key Features of SAP Data Services:

  1. Data Integration: Connects to a wide variety of data sources, including databases, applications, and cloud services, to extract data for transformation and loading into target systems.

  2. Data Transformation: Provides a rich set of data transformation capabilities, allowing for complex data manipulations, cleansing, and enrichment.

  3. Data Quality: Offers tools for data profiling, cleansing, matching, and de-duplication to ensure high-quality data.

  4. Data Profiling: Allows users to analyze data for quality, completeness, and consistency.

  5. Job Design: Features an intuitive graphical interface for designing ETL jobs, workflows, and data flows.

  6. Metadata Management: Tracks and manages metadata to provide transparency and lineage of data throughout the ETL process.

  7. Scalability and Performance: Designed to handle large volumes of data and scale with the needs of the organization.

  8. Real-Time Data Integration: Supports real-time data integration and replication for timely data availability.

Relationship to BODS:

  • Rebranding: SAP Data Services is essentially the rebranded and evolved version of BusinessObjects Data Services (BODS). The core functionality remains similar, but SAP Data Services has been enhanced and integrated more tightly with other SAP products and technologies.

  • Integration with SAP Landscape: As part of the SAP ecosystem, Data Services integrates seamlessly with other SAP solutions, such as SAP HANA, SAP S/4HANA, and SAP BW, providing a unified platform for data management and analytics.

  • Evolution: Over time, SAP has added new features and capabilities to Data Services, making it a more powerful and comprehensive tool for data integration and quality management compared to its predecessor, BODS.

Use Cases:

  • Data Warehousing: Extracting data from various sources, transforming it, and loading it into data warehouses for reporting and analysis.
  • Data Migration: Moving data from legacy systems to new applications or platforms.
  • Master Data Management: Ensuring consistent and accurate master data across the organization.
  • Big Data Integration: Integrating data from big data platforms such as Hadoop and Spark.
  • Real-Time Analytics: Providing real-time data integration and transformation for up-to-date analytics and reporting.

In summary, SAP Data Services is a comprehensive data integration and management tool that builds on the legacy of BODS, offering enhanced features and tighter integration within the SAP ecosystem


The technical architecture of SAP Data Services involves several components that work together to provide a robust data integration and management platform. Here’s an overview of the key components and their roles in the architecture:

1. Data Services Designer

  • Role: A graphical user interface for designing ETL jobs, workflows, and data flows.
  • Functions: Used for creating and managing data integration tasks, transformations, and data quality rules.
  • Interaction: Connects with the central repository to save and retrieve job definitions and metadata.

2. Data Services Repository

  • Types: Central Repository, Local Repository, and Profiler Repository.
  • Role: Stores metadata, job definitions, and profiling data.
  • Functions:
    • Central Repository: Supports version control and collaboration among multiple users.
    • Local Repository: Contains individual user’s job definitions and metadata.
    • Profiler Repository: Stores profiling results and metrics for data quality.

3. Data Services Job Server

  • Role: Executes ETL jobs designed in the Data Services Designer.
  • Functions: Manages job scheduling, execution, and monitoring. Communicates with data sources and targets to perform data extraction, transformation, and loading.
  • Interaction: Interfaces with the repository to retrieve job definitions and metadata during job execution.

4. Data Services Access Server

  • Role: Handles real-time data integration and messaging.
  • Functions: Manages real-time jobs and data movement between real-time sources and targets. Provides an interface for applications to interact with Data Services in real-time.

5. Data Services Management Console

  • Role: A web-based interface for administration and monitoring.
  • Functions:
    • Administration: Manage users, security, and configuration settings.
    • Monitoring: Monitor job execution, review logs, and analyze performance.
    • Data Quality: View data profiling results and data quality reports.

6. Source and Target Systems

  • Role: Systems from which data is extracted and to which data is loaded.
  • Types: Databases (e.g., Oracle, SQL Server, SAP HANA), applications (e.g., SAP ERP, Salesforce), flat files, XML files, web services, and big data platforms (e.g., Hadoop).

7. Integration with SAP Landscape

  • Role: Seamless integration with SAP products.
  • Components: SAP HANA, SAP S/4HANA, SAP BW, and other SAP applications.
  • Functions: Facilitates data movement and transformation within the SAP ecosystem for unified data management and analytics.

Technical Architecture Diagram

Here’s a simplified conceptual diagram to illustrate the architecture:

sql
 +--------------------------+
 | Data Services Designer   | <---> Central Repository
 +--------------------------+          ^
                                       |
 +--------------------------+          |         +--------------------------+
 | Data Services Job Server | <--------+-------> | Source/Target Systems    |
 +--------------------------+                    | (Databases, Applications,|
                                       |         | Files, Big Data)         |
 +--------------------------+          |         +--------------------------+
 | Data Services Access     | <--------+
 | Server (Real-Time)       |
 +--------------------------+
                                       |
 +--------------------------+          |
 | Data Services Management | <--------+
 | Console (Web-Based)      |
 +--------------------------+

Detailed Description of the Flow

  1. Design Phase:

    • Data engineers use the Data Services Designer to create ETL jobs, transformations, and data flows. These are stored in the Local Repository and can be shared via the Central Repository for collaboration and version control.

  2. Execution Phase:

    • The Data Services Job Server retrieves job definitions and metadata from the repositories.
    • The Job Server connects to various Source Systems to extract data, performs transformations, and loads the data into the Target Systems.

  3. Real-Time Integration:

    • The Data Services Access Server manages real-time data integration and messaging, facilitating real-time data movement between source and target systems.

  4. Administration and Monitoring:

    • Administrators use the Data Services Management Console to manage configurations, users, and security settings, as well as to monitor job executions and analyze performance metrics.

  5. Data Quality:

    • The Profiler Repository stores profiling data, and the Management Console is used to view and analyze data quality metrics and reports.

This architecture ensures scalable, reliable, and efficient data integration, transformation, and management across various data sources and targets.


The technical landscape of SAP Data Services involves various components and their interactions within an IT infrastructure to support data integration, transformation, and management. Below is an outline of the key components and their placement within a typical technical landscape:

Technical Landscape Overview

  1. Client Tier

    • Data Services Designer: Installed on user workstations for designing ETL jobs and workflows.
    • Data Services Management Console: Web-based interface accessible from user browsers for administration, monitoring, and data quality management.

  2. Server Tier

    • Data Services Job Server: Deployed on application servers to execute ETL jobs.
    • Data Services Access Server: Deployed on application servers to handle real-time data integration.
    • Web Server: Hosts the Data Services Management Console for web-based access.

  3. Repository Tier

    • Central Repository: A database that stores shared job definitions, metadata, and version control information.
    • Local Repository: Databases on individual developers' machines or servers for personal job definitions and metadata.
    • Profiler Repository: A database for storing data profiling results and quality metrics.

  4. Data Sources and Targets

    • Databases: Oracle, SQL Server, SAP HANA, etc.
    • Applications: SAP ERP, Salesforce, etc.
    • Flat Files: CSV, XML, JSON, etc.
    • Big Data Platforms: Hadoop, Spark, etc.
    • Cloud Services: AWS, Azure, Google Cloud, etc.

Diagram of the Technical Landscape

sql

+------------------------------------------------------+
|                      Client Tier                     |
|                                                      |
|  +----------------------------------------------+    |
|  | Data Services Designer                       |    |
|  +----------------------------------------------+    |
|  +----------------------------------------------+    |
|  | Data Services Management Console (Web-Based) |    |
|  +----------------------------------------------+    |
|                                                      |
+------------------------------------------------------+

+------------------------------------------------------+
|                      Server Tier                     |
|                                                      |
|  +----------------------------------------------+    |
|  | Data Services Job Server                     |    |
|  +----------------------------------------------+    |
|  +----------------------------------------------+    |
|  | Data Services Access Server                  |    |
|  +----------------------------------------------+    |
|  +----------------------------------------------+    |
|  | Web Server (for Management Console)          |    |
|  +----------------------------------------------+    |
|                                                      |
+------------------------------------------------------+

+------------------------------------------------------+
|                     Repository Tier                  |
|                                                      |
|  +----------------------------------------------+    |
|  | Central Repository (Database)                |    |
|  +----------------------------------------------+    |
|  +----------------------------------------------+    |
|  | Local Repository (Database)                  |    |
|  +----------------------------------------------+    |
|  +----------------------------------------------+    |
|  | Profiler Repository (Database)               |    |
|  +----------------------------------------------+    |
|                                                      |
+------------------------------------------------------+

+------------------------------------------------------+
|                Data Sources and Targets              |
|                                                      |
|  +--------------------+  +--------------------+      |
|  | Databases          |  | Applications       |      |
|  | (Oracle, SQL       |  | (SAP ERP,          |      |
|  | Server, SAP HANA)  |  | Salesforce)        |      |
|  +--------------------+  +--------------------+      |
|  +--------------------+  +--------------------+      |
|  | Flat Files         |  | Big Data Platforms |      |
|  | (CSV, XML, JSON)   |  | (Hadoop, Spark)    |      |
|  +--------------------+  +--------------------+      |
|  +--------------------+  +--------------------+      |
|  | Cloud Services     |  | Others             |      |
|  | (AWS, Azure,       |  |                    |      |
|  | Google Cloud)      |  |                    |      |
|  +--------------------+  +--------------------+      |
|                                                      |
+------------------------------------------------------+

Detailed Description of Components and Interactions

  1. Client Tier:

    • Data Services Designer: Installed on user workstations, it provides a graphical interface for designing ETL jobs, transformations, and data flows. It connects to the central and local repositories to save and retrieve job definitions and metadata.
    • Data Services Management Console: A web-based tool accessed through browsers for managing and monitoring Data Services operations. It interacts with the repositories and job servers to provide real-time insights and administrative capabilities.

  2. Server Tier:

    • Data Services Job Server: Deployed on one or more application servers, it is responsible for executing ETL jobs. It retrieves job definitions from the repositories and interacts with data sources and targets to perform data extraction, transformation, and loading.
    • Data Services Access Server: Manages real-time data integration tasks. It enables real-time data flows and messaging between source and target systems.
    • Web Server: Hosts the Management Console, providing web access for administration and monitoring.

  3. Repository Tier:

    • Central Repository: A centralized database that stores shared job definitions, metadata, and version control information. It supports collaboration among multiple users and versioning of ETL jobs.
    • Local Repository: Individual databases for developers, storing personal job definitions and metadata. These are typically used during development and testing.
    • Profiler Repository: Stores profiling data and quality metrics, providing insights into data quality and integrity.

  4. Data Sources and Targets:

    • Databases: Traditional relational databases (Oracle, SQL Server, SAP HANA) and modern databases (NoSQL, NewSQL).
    • Applications: Enterprise applications (SAP ERP, Salesforce) from which data can be extracted or to which data can be loaded.
    • Flat Files: Data files in formats like CSV, XML, and JSON used as data sources or targets.
    • Big Data Platforms: Platforms like Hadoop and Spark used for large-scale data processing and storage.
    • Cloud Services: Cloud platforms (AWS, Azure, Google Cloud) used for data storage, processing, and integration.

This technical landscape ensures that SAP Data Services can handle diverse data integration scenarios, providing a scalable, reliable, and efficient platform for managing data across various systems and environments.


SAP Data Services supports a variety of server platforms for deployment, ensuring flexibility and compatibility with different IT environments. Here are the primary server platforms supported by SAP Data Services:

Supported Operating Systems for Data Services Servers

  1. Microsoft Windows

    • Windows Server 2016
    • Windows Server 2019
    • Windows Server 2022

  2. Linux

    • Red Hat Enterprise Linux (RHEL) 7.x, 8.x
    • SUSE Linux Enterprise Server (SLES) 12.x, 15.x
    • Oracle Linux 7.x, 8.x

  3. UNIX

    • IBM AIX 7.1, 7.2

Supported Databases for Repositories

SAP Data Services repositories can be hosted on several database platforms. The supported databases include:

  1. Microsoft SQL Server

    • SQL Server 2016
    • SQL Server 2017
    • SQL Server 2019

  2. Oracle

    • Oracle Database 12c
    • Oracle Database 19c

  3. SAP HANA

    • SAP HANA 2.0

  4. IBM Db2

    • Db2 11.1
    • Db2 11.5

Supported Web Servers for Management Console

The SAP Data Services Management Console can be hosted on various web servers, including:

  1. Apache Tomcat

    • Tomcat 8.5
    • Tomcat 9.0

  2. IBM WebSphere

    • WebSphere Application Server 8.5
    • WebSphere Application Server 9.0

  3. Oracle WebLogic

    • WebLogic Server 12.2.1.x
    • WebLogic Server 14.1.1.x

Supported Data Sources and Targets

SAP Data Services can connect to and integrate with a wide range of data sources and targets, including but not limited to:

  1. Relational Databases

    • Oracle
    • Microsoft SQL Server
    • SAP HANA
    • IBM Db2
    • MySQL
    • PostgreSQL

  2. Cloud Platforms

    • AWS (Amazon Web Services)
    • Microsoft Azure
    • Google Cloud Platform (GCP)

  3. Big Data Platforms

    • Hadoop Distributed File System (HDFS)
    • Apache Hive
    • Apache Spark

  4. Enterprise Applications

    • SAP ERP
    • SAP S/4HANA
    • Salesforce
    • PeopleSoft

  5. Flat Files and Semi-Structured Data

    • CSV, XML, JSON files
    • Excel files

Summary

SAP Data Services offers broad support for various server platforms, databases, web servers, and data sources, ensuring that it can be integrated into a wide range of IT environments. This flexibility allows organizations to leverage their existing infrastructure while deploying Data Services to handle their data integration and management needs.

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