Diving Deep into Data Aging Strategies in SAP S/4 HANA

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In the dynamic realm of enterprise resource planning (ERP), SAP S/4 HANA is recognized as a revolutionary solution, offering unparalleled efficiency and innovation. A critical aspect enhancing its capabilities is Data Aging. Let’s examines Data Aging within S/4 HANA, emphasizing its importance and exploring its potential impact on modern businesses’ data management strategies. 

Grasping the Concept of Data Aging

Data Aging is a tailored approach to managing data within suites, aimed at reducing the memory footprint of SAP HANA. Provided by the Data Aging Framework from SAP NetWeaver ABAP, it’s applicable to SAP Business Suite on HANA and SAP S/4HANA. This framework allows for the movement of significant data volumes within SAP HANA to optimize working memory. Data Aging distinguishes between operationally relevant (Hot/Current) and less-accessed data (Cold/Historical). By leveraging data temperature, tables participating in Data Aging can be horizontally partitioned to enhance resource efficiency and performance. While hot data resides in the main memory of SAP HANA, cold data primarily rests on disk but remains accessible through SQL queries upon request. 


The objective of data aging is twofold: to minimize the main memory usage and enhance the speed of database queries. This is achieved by retaining operationally crucial (hot) data in main memory while relocating less frequently accessed (cold) data to secondary storage, which is typically less expensive but slower. Cold data remains accessible through SQL queries as needed, ensuring efficient data retrieval without compromising performance. 

Exploring Data Archiving and Aging in S/4 HANA

In SAP HANA, Data Aging differs from Archiving as it retains cold data within the database, accessible via SQL in a separate partition from hot data. Archiving, on the other hand, involves strictly read-only data stored in an archive file and removed from the database, requiring additional access paths for retrieval. Aging focuses on reducing main memory usage, while archiving forms the foundation for Information Lifecycle Management (ILM), covering data lifecycle up to destruction. Data Aging facilitates moving less operationally relevant data within the database to free up working memory. This involves using SAP applications, particularly data aging objects, to transition data from the current to historical area, affecting data visibility and enabling faster queries on current data. However, thorough testing is crucial to ensure alignment with business process requirements. To implement Data Aging, certain database and application requirements must be met. Conversely, Data Archiving involves removing data from the database and storing it externally in a secure and consistent manner. Archived data resides in a file system and can be transferred to more cost-efficient long-term storage systems through interfaces like Archive Link or ILM. 

Exploring Hot and Cold Data Dynamics

Hot Data

Current data refers to information crucial for daily business operations, actively utilized in transactions. Transitioning from current to historical status is determined by application logic, considering the object’s life cycle. This logic assesses business conditions, including status, existence checks, and cross-object dependencies. Current data is housed in the current area. 

Examples of Current Data: 

  • Unsettled FI items 
  • Recently cleared items 
  • Pending purchase orders 
  • Ongoing sales documents 
  • Active project files 
  • IDocs awaiting processing 

Cold Data

Historical data encompasses information not integral to daily operations. It’s no longer updated and remains invisible to ABAP applications. The transition from current to historical status is governed by application logic, based on business criteria. Historical data is stored separately in the historical area. 

Examples of Historical Data: 

  • Settled FI items from previous fiscal years 
  • Material documents predating the current closed period 
  • Processed IDocs 
  • Application logs beyond a specified timeframe

Navigating the Technical Process of Data Aging in S/4 HANA

The data aging mechanism in ABAP applications relies on a framework provided by SAP NetWeaver ABAP. ABAP developers utilize this framework to define aging objects, identify associated tables, and implement logic for determining data temperature. A data temperature column ‘_DATAAGING’ (of type DATA_TEMPERATURE with ABAP date format “YYYYMMDD”) is added to participating tables for this purpose. This data temperature column enables horizontal partitioning of application data based on time selection, optimizing resource usage and performance. Hot data is stored in one partition with the value “00000000”, while other partition(s) contain cold data with varying temperature ranges. By default, only hot data is accessed, and SAP HANA loads the hot partition into memory during normal operations. ABAP developers can adjust the data temperature context to access hot data, all data, or data above a specified temperature. 

The SAP HANA-specific database shared library (DBSL) in the ABAP server includes a clause in SQL statements sent to SAP HANA for range restriction. For instance, WITH RANGE RESTRICTION (‘CURRENT’) limits operations to the hot data partition, while specifying a concrete value restricts operations to partitions with temperatures above the specified value. Range restriction can be applied to various SQL statements and procedure calls. The application identifies closed business objects and sets values in the data temperature column to indicate closure. During an Aging Run, rows with closed objects are moved to cold partitions automatically due to table partitioning by the temperature column. This move affects data visibility and accessibility. Several configuration steps are required to execute a Data Aging Run successfully.

When does the Data Transition Occur from the Hot Partition to the Cold Partition?

The progression of current data to historical status is determined by the application’s logic, which assesses the object’s lifecycle. This assessment entails verifying conditions at the object level from a business standpoint, including status, existence checks, and cross-object dependencies.

During a Data Aging Run, data is relocated as part of the procedure. Prior to commencing an Aging Run, several prerequisites must be satisfied: 

Data Identification

An application-specific runtime class is employed to pinpoint the data earmarked for Data Aging. Runtime classes are linked to corresponding Data Aging objects by the SAP application, facilitating their invocation and processing during the Aging Run. 

Partition Management

Data Aging necessitates the partitioning of all involved tables to enable the transfer of data from the hot partition to the cold partition(s) based on specified partitioning objects and groups. Partition definitions for Data Aging objects (DAGPTM) are configured for each system. It’s imperative to ensure the absence of intervals between partitions for multiple partitions on one table, and that at least one cold partition encompasses the current date. 

Activation of Data Aging Objects

Following partition definition, Data Aging objects are activated through the Data Aging Objects transaction (DAGOBJ). This process entails conducting various checks on each table associated with the Data Aging Object to authorize its utilization during the run.

Management of Data Aging Groups

Data Aging Groups are established via the DAGOBJ transaction, facilitating the collective selection of Data Aging Objects for processing. This streamlines the administration of multiple objects within a single group. 

To schedule Data Aging Runs, users navigate to the DAGRUN transaction, where they select a Data Aging Group, specify Maximum Runtime, and designate Start Date/Time to initiate the run. This transaction additionally provides monitoring capabilities for Data Aging Runs, furnishing details such as Data Aging Groups, Start date/time, Duration, and Job name. 

Guidance, Technical Considerations, and TCOs

In SAP S/4HANA, Data Aging serves to actively remove a portion of the data stored within the SAP HANA database from the main memory. Unlike the “column unload” feature in SAP HANA, aged data remains technically retrievable within the database. However, visibility of aged data within the ABAP system is restricted by default. The technology for storing and processing aged data is geared towards sporadic transactional (OLTP) access, with access limited to data for which longer response times due to hard-disk access are acceptable. To leverage Data Aging within each application component, a corresponding data aging object must be present. It’s essential to note that Data Aging doesn’t reduce the size of data in the SAP HANA database but rather decreases the memory required to store and manipulate portions of the data. Consequently, Data Aging isn’t a solution for reducing disk space usage, minimizing database backup sizes, or expediting backup and recovery operations. To achieve these objectives, the following methods and technologies are recommended: 

  1. Avoid creating unnecessary data. 
  2. Perform housekeeping tasks, such as deleting temporary technical data like spool, jobs, and application logs. 
  3. Utilize data archiving. 
  4. Leverage SAP Information Lifecycle Management (ILM). 

Technically, aged data remains accessible in both the SAP HANA database and the SAP S/4HANA system. However, there are no additional authorizations or means to block access to aged data. Consequently, Data Aging doesn’t directly impact the design of system aspects related to data protection and privacy (DPP). For DPP-related considerations, archiving with ILM is crucial. If Data Aging and archiving are combined for the same objects, archiving processes will need to handle aged data, impacting their runtime. Therefore, careful consideration is required when integrating Data Aging and archiving functionalities.

Total Cost of Ownership (TCO) Considerations

To gauge the potential impact of Data Aging on TCO, several factors should be taken into account: 

  1. Project Costs: This encompasses the expenses associated with implementing Data Aging, such as analysis, code optimization, and testing. Depending on your system’s memory reduction goals, you may need to extend existing Data Aging objects or create new ones. 
  2. Comparison with Archiving or ILM: Evaluate the residence times of Data Aging against established archiving or ILM retention times. Determine if reasonable settings result in significant memory savings through Data Aging. 
  3. Data Privacy Regulations: In some regions, data privacy regulations mandate the physical removal or destruction of data from the primary database. This requirement can be fulfilled using SAP ILM. Consequently, Data Aging may serve as an interim step or temporary solution before data removal. 

Additional Considerations

Data Aging in SAP S/4HANA actively removes a portion of data from HANA’s main memory. Unlike the “column unload” function in SAP HANA, aged data remains technically accessible within the database. However, its visibility within the ABAP system is restricted by default. The technology for storing and processing aged data is optimized for sporadic transactional access, with access limited to data where longer response times due to hard-disk access are acceptable. Given technological advancements, leveraging SAP HANA Native Storage Extension (NSE) without Data Aging is preferable for technical and logging data. NSE offers a simpler alternative for reducing memory footprint, allowing flexibility in usage and easy reversibility. Unlike Data Aging, NSE doesn’t require specific aging objects and doesn’t involve scheduling or monitoring Data Aging runs.


Data Aging in SAP S/4HANA offers a practical approach to managing data within the SAP HANA database, enhancing efficiency and performance. By categorizing data into hot and cold categories, Data Aging helps optimize memory usage and streamline query processing. While implementing Data Aging, organizations should consider costs, technical requirements, and compliance with data privacy regulations. Additionally, exploring alternatives like SAP HANA Native Storage Extension (NSE) for specific data types can provide simpler solutions to reduce memory footprint. Overall, Data Aging complements existing data management strategies, enabling organizations to efficiently manage data lifecycles while ensuring integrity and compliance. 

Here’s where VE3 helps you, by leveraging our partnership with SAP solutions, organisations can further enhance the implementation and optimization of Data Aging processes. With their deep understanding of SAP technologies and experience in data management, we can provide tailored guidance and support to maximize the benefits of Data Aging, ensuring seamless integration and optimal performance for organizations transitioning to SAP S/4HANA. To know more, explore our innovative digital solutions or contact us directly. 


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