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Integration of SAP ERP, SAP Datasphere and Data Lake

Ravi
April 10, 2024

SAP ERP

SAP ECC, the long-standing leader in Enterprise Resource Planning (ERP) systems, is being succeeded by SAP S/4HANA.S/4HANA offers a next-generation ERP built on the in-memory HANA database, enabling real-time processing, simplified data structures, and advanced analytics capabilities, all designed to meet the evolving needs of businesses in the digital age.

 

SAP Datasphere

SAP Datasphere acts as a central hub for your enterprise data, providing seamless and scalable access to critical business information. It goes beyond traditional data warehousing by integrating data ingestion, data quality management, and semantic modelling tools. This allows you to combine data from various sources, both inside and outside SAP, for holistic analysis and empowers data-driven decision-making across your organization.

 

Data Lake

A data lake is a large-scale storage repository designed to hold vast amounts of raw, unstructured data from various sources. Unlike traditional data warehouses with predefined formats, a data lake stores data "as-is," allowing for flexibility and future exploration. Businesses leverage data lakes for big data analytics, identifying trends, uncovering hidden patterns, and gaining valuable insights to support strategic decision-making.

 

SAP Data Integration (SDI)

Imagine a world where your core business systems (SAP ECC/S/4HANA) seamlessly collaborate with cutting-edge big data analytics. This vision becomes reality with SAP Data Integration, acting as the glue that binds your data ecosystem.

At the heart lies the SAP Data Provisioning Agent, a software component residing on your network. It acts as a secure bridge, enabling efficient replication of data (both structured and unstructured) from your SAP system to a data lake hosted on platforms like Amazon Redshift or Snowflake. This data can be raw or pre-processed depending on your needs for big data analysis.

But SAP Data Integration goes beyond simple data transfer. Data Provisioning Adapters, specialized programs hosted by the Agent, unlock advanced functionalities:

  • Data Federation: Access data from the data lake without     physically moving it, saving storage and processing resources.
  • Data Transformation: Convert data from your SAP system into a     format compatible with the data lake, ensuring smooth analysis.

Furthermore, the Agent empowers you to create custom adapters using the SAP Data Provisioning Adapter SDK, catering to unique integration needs beyond standard functionalities.

This integrated landscape culminates in SAP Datasphere, a central hub for data management and analysis. It allows you to leverage the comprehensive data set from your SAP system and the data lake, unlocking valuable insights for informed decision-making.

In essence, SAP Data Integration streamlines data flow, fosters big data analytics on your SAP information, and empowers you to make data-driven decisions for a competitive advantage.

 

SAP Data Provisioning Agent

The SAP Data Provisioning Agent is a software component that acts as a bridge between your SAP systems (like SAPS/4HANA or ECC) and big data environments. Here's a breakdown of its key functionalities:

Secure Data Replication:

  • Establishes a secure     connection between your SAP system and external data sources, particularly     data lakes hosted on platforms like Amazon Redshift or Snowflake.
  • Facilitates efficient     replication of data from your SAP system to the data lake. This data can     be structured (e.g., customer records) or unstructured (e.g., sensor     data).
  • You have control over     whether the data is transferred raw or pre-processed based on your     analytics needs.

Enhanced Integration Capabilities:

  • Data Provisioning Adapters: The Agent acts as a host for specialized     software programs called adapters. These adapters enable functionalities     beyond basic data transfer:
       
    • Data Federation: Allows you to access data from the data      lake without physically moving it from its original location, optimizing      storage and processing efficiency.
    •  
    • Data Transformation: Transforms data from your SAP system      into a format readily compatible with the data lake's structure for      seamless integration and analysis.
  •  
  • Customizable Integrations: The Agent allows you to create custom     Data Provisioning Adapters using the SAP Data Provisioning Adapter SDK.     This caters to unique data integration needs beyond standard     functionalities.

Centralized Management:

  • Provides a single point of     access for managing the flow of data between your SAP system and the data     lake.
  • Simplifies data governance     and enhances visibility into data movement, ensuring a smooth and     efficient data exchange process.

Overall Benefits:

  • Enables big data analytics     on your SAP data, unlocking valuable insights to support strategic     decision-making.
  • Offers flexibility with     various data integration scenarios like replication, federation, and data     transformation.
  • Provides a scalable     solution for managing data exchange regardless of data volume or     complexity.

 

SAP ECC /S4HANA (< 1909) Integration with SAP Datasphere

SAP Data Integration (SDI) facilitates seamless data flow between your SAP ECC or S/4HANA system (up to release 1909)and a data lake using SAP Landscape Transformation Services (LTS) and the SAP Data Provisioning Agent (DPA).

Unifying Your Data Landscape

  • Core Systems: Your SAP ECC or S/4HANA system serves as     the central repository for your core business data.
  • Data Lake: A data lake on platforms like Amazon     Redshift or Snowflake acts as a vast storage for both structured and     unstructured data from various sources, including SAP.
  • SAP Datasphere: This central hub serves as the     platform for data management, analysis, and visualization.

The Integration Bridge: SAP Data Integration (SDI)

  • SAP Landscape     Transformation Services (LTS): This     service prepares your SAP system for data integration by ensuring a stable     and optimized environment.
  • SAP Data Provisioning Agent     (DPA): This software     component acts as a secure bridge, residing on your network, enabling the     following functionalities:
       
    • Data Replication: Efficiently replicates data (structured      and unstructured) from your SAP system to the data lake. You can choose      to transfer raw or pre-processed data based on your needs.
    •  
    • Flexibility with Adapters: Specialized Data Provisioning Adapters      hosted by the DPA unlock advanced functionalities:
         
      • Data Federation: Access data from the data lake without       physically moving it, optimizing storage and processing resources.
      •  
      • Data Transformation: Convert data from       your SAP system into a format compatible with the data lake for seamless       analysis.

Unleashing Data Insights with SAP Datasphere

  • SAP Datasphere acts as the     central location for managing and analyzing the comprehensive data set     from your SAP system and the data lake.
  • This unified data view     empowers you to leverage big data analytics and extract valuable business     insights to support informed decision-making.

 

Key Considerations for S/4HANA 1909 and Above

This approach is well-suited for SAP ECC and S/4HANAsystems up to release 1909. Newer versions of S/4HANA offer alternative or native data integration functionalities that supersede the need for LTS.

SAP S4HANA(>= 1909) & SAP Public Cloud Integration with SAP Datasphere

SAP Data Integration seamlessly connects your SAP S/4HANA system (version 1909 onwards) with SAP Datasphere and a data lake (like Amazon Redshift or Snowflake) using the SAP Data Provisioning Agent(DPA).

The Core Connection: DPA as the Bridge

The DPA acts as a secure bridge residing on your network. It establishes a direct connection between S/4HANA 1909 and SAP Datasphere, facilitating the flow of data (both structured and unstructured) from your S/4HANA system to the data lake in Datasphere. You have control over whether the data is transferred raw or pre-processed based on your analytical needs.

The DPA provides a single point of control for managing the flow of data between S/4HANA 1909 and SAP Datasphere. This simplifies data governance, enhances visibility into data movement, and ensures a smooth and efficient data exchange process.

Benefits of Non-LTS with DPA:

  • Access to New Features: By choosing S/4HANA 1909 (non-LTS), you     gain access to the latest features and functionalities for data     integration with Datasphere.
  • Streamlined Integration: The DPA offers a robust and secure     connection for data flow.
  • Advanced Capabilities     (Optional): Data federation and     transformation through adapters enhance data integration possibilities.
  • Reduced Costs: Avoiding the potential licensing costs     of an LTS version can be a cost-saving factor.

Considerations for non-LTS:

  • Stability and Security: LTS versions prioritize stability and     security. While non-LTS versions are generally stable, there's a slightly     higher chance of encountering bugs or requiring additional configuration     compared to LTS.
  • Early Access vs. Stability: Weigh the benefits of early access to     new features in non-LTS against the potential need for more stability in     production environments.

 SAP ERP Integration with Data Lake (Amazon Redshift Vs Snowflake)

Integration between SAP Datasphere and Amazon Redshift

SAP Datasphere and Amazon Redshift join forces to create a robust data integration solution. Here's a breakdown of this powerful combination.

Native Connectivity:

  • Effortless Connection: SAP Datasphere offers a native connector     for Amazon Redshift, simplifying the setup process. This eliminates the     need for complex configurations or additional middleware tools.
  • Streamlined Data Flow: The native connector facilitates the     efficient transfer of data (structured and unstructured) between your SAP     environment and the Redshift data lake within Datasphere. You have control     over whether the data is transferred raw or pre-processed based on your     analytical needs.

Advanced Integration Capabilities (Optional):

  • Data Transformation: While not mandatory for basic data transfer,     SAP Datasphere allows data transformation before it reaches Redshift. This     can involve data cleansing, formatting, or conversion to ensure seamless     compatibility with Redshift's structure for optimal analysis.
  • Data Federation (Optional): Leveraging features within SAP     Datasphere, you can potentially enable data federation. This allows you to     access data residing in Redshift directly from your SAP applications     without physically moving it. This optimizes storage and processing resources     on your network.

Integration between SAP Datasphere and Snowflake

SAP Datasphere does not have a native integration with Snowflake as on 10/April/2024. Snowflake can be integrated via Microsoft Azure Data Factory.

ADF-Mediated Integration: Azure Data Factory can be used as an orchestration layer for more complex data integration requirements. Here's how ADF can enhance the process.

  • Complex Data     Transformations: If you require     extensive data transformations before loading data into Snowflake, ADF     offers a visual interface and pre-built connectors to manipulate data from     various sources, including SAP Datasphere.
  • Data Orchestration and     Scheduling: ADF excels at     orchestrating complex data pipelines involving multiple sources and     destinations. You can define workflows with dependencies between different     data processing activities. Additionally, ADF allows scheduling data flows     for automated data movement.
  • Advanced Data Cleansing and     Validation: ADF offers     functionalities for data cleansing, validation, and error handling within     data pipelines. This ensures the quality and integrity of data before it     reaches Snowflake.
  • Hybrid and Multi-Cloud     Integration: If your data     landscape spans across on-premises and cloud environments (including     Azure), ADF can seamlessly integrate data from various sources, including     SAP Datasphere, and route it to Snowflake hosted on the cloud.

SAP Datasphere as Orchestrator

SAP Datasphere shines as a powerful data orchestration platform for your SAP ecosystem.

Centralized Management Hub:

·        Data Flow Management: Datasphere acts as a central hub for managing and monitoring data flows between various sources and destinations within your SAP environment. This includes data movement from SAP Business Suite systems, S/4HANA, cloud applications, and external data sources.

·        Streamlined Pipelines: Automate data pipelines, streamlining data movement tasks by defining workflows that orchestrate data extraction, transformation, and loading (ETL) processes between different systems.

Enhanced Data Governance:

·        Data Lineage Tracking: Track the origin and movement of data throughout your data pipelines. This is crucial for ensuring data quality, regulatory compliance, and troubleshooting any data-related issues.

·        Monitoring and Alerts: Datasphere provides monitoring capabilities to track data pipeline progress and identify errors or delays. Additionally, it can trigger alerts and notifications based on pre-defined conditions, keeping you informed of potential data quality or pipeline execution issues.

Change Data Capture (CDC) in S/4HANA.

S/4HANA utilizes a trigger-based approach for CDC. Here's a simplified breakdown:

1.    Change Detection: Database triggers are created for relevant tables in S/4HANA.These triggers fire whenever a record is created, updated, or deleted.

2.    Change Logging: When a trigger fires, details about the change (before and after values) are captured and stored in dedicated CDC logging tables within the S/4HANA database.

3.    Change Data Extraction: Tools like the SAP Data Provisioning Agent (DPA) or custom applications can access the CDC logging tables and extract the captured change information.

4.    Data Utilization: The extracted change data can then be used for various purposes as mentioned earlier (replication, event triggers, etc.).

DPA in Action:

1.    Scheduled or Real-Time Extraction: The DPA can be configured to extract data from the CDC tables either at pre-defined intervals(scheduled) or in near real-time. This depends on your specific needs and the volume of data changes.

2.    Change Data Selection: The DPA leverages the information in the CDC tables to identify the specific data modifications that need to be extracted. This ensures it only captures the relevant changes, optimizing data transfer efficiency.

3.    Data Transformation(Optional): While not mandatory, the DPA can be configured to perform data transformations on the extracted change data before sending it to the destination (e.g., data lake in SAP Datasphere). This might involve cleansing, formatting, or converting data to ensure compatibility with the target system.

Benefits of Using DPA with CDC:

  • Efficient Data Extraction: Focuses only on the changed data,     minimizing data volume and network traffic.
  • Reduced Processing Loads: Extracting change data from dedicated     CDC tables reduces the workload on the S/4HANA production database.
  • Simplified Data     Integration: Streamlines the     process of integrating real-time or near real-time data changes into other     systems.
  • Improved Data Quality: Minimizes the risk of inconsistencies     between S/4HANA and downstream systems by ensuring data updates reflect     the latest changes.

Consuming CDS Views as Remote Tables in Datasphere

Consuming CDS views as remote tables in SAP Datasphere offers several advantages for data integration and analysis within your SAP landscape. Here's a breakdown of the benefits and considerations.

Benefits:

  • Real-Time or Near Real-Time     Insights: By leveraging Change     Data Capture (CDC) functionality in S/4HANA, you can consume CDS views as     remote tables in Datasphere with near real-time updates. This enables     access to the latest data for your analytics needs. (Note: Enabling CDC     for specific CDS views might be required)
  • Simplified Data Access: Treat CDS views as regular tables within     Datasphere, simplifying data access and exploration for analysts and data     scientists. They can leverage familiar tools and queries within Datasphere     to work with the data exposed by the CDS views.
  • Reduced Data Movement: Consuming only the view definition     (metadata) and potentially change data (through CDC) minimizes data     movement between S/4HANA and Datasphere. This optimizes network traffic     and reduces processing loads on your S/4HANA system.
  • Improved Data Governance: Centralized data management in     Datasphere allows for consistent access control and data quality checks on     the data exposed by the CDS views. This enhances data governance within     your organization.
  • Flexibility and     Scalability: Datasphere offers     capabilities for data transformation, allowing you to manipulate data from     the CDS views before utilizing it for analysis. This flexibility empowers     you to tailor the data to your specific analytical requirements.     Additionally, Datasphere scales efficiently as your data needs grow.