Data Archiving in the Age of Big Data

With enterprise applications saving large amount of information about customers and business operations, organizations are collecting enormous piles of big data that have to be managed. A fundamental part of this growing volume of information is historic data. Archived data from business-complete procedures— such as production, HR, and financials data from SAP ERP– has intrinsic value that can benefit present or future business decisions by helping to expose trends, patterns, and connections. Historical data also often needs to be kept for years to comply with legal requirements, such as laws governing tax audits or product liability, or FDA policies in the pharmaceutical industry.

Keeping historical data readily available in the main application database is a resource drain, however, so organizations typically move this data to a secondary storage location. While solutions such as SAP Information Lifecycle Management provide thorough capability for data management jobs such as archiving, retention management, and system decommissioning, adding various storage locations to support these tasks can cause enhanced – and costly – landscape complexity.

To help customers simplify their landscapes while addressing their archiving requirements, SAP offers a new database storage option within SAP Information Lifecycle Management that provides organizations with a less complicated, more efficient alternative for archiving SAP ERP data.


Traditional Approaches to Storing Archived Data

In SAP environments, archived operational data, such as transactional data or master data from an SAP ERP system, is generally saved in a third-party storage system. This storage system needs to be licensed for use with the SAP ArchiveLink interface to enable integration with the standard SAP archiving system, or, to deal with SAP Information Lifecycle Management for data retention and system decommissioning, it needs to be a compose when, checked out many (WORM)-like store that supports the storage of structured information using the WebDAV standard.

Organizations that are also using SAP Business Warehouse (SAP BW) have the alternative to move less regularly accessed (“cold” or “aged”) analytical data from their SAP BW systems to the near-line storage location of SAP IQ– a highly enhanced data analysis server that enables SQL-based reporting and enhanced performance during data load and access (see the sidebar “What Is SAP IQ?”)– via a near-line storage interface. As part of SAP BW, SAP delivers a native ABAP-based implementation of the SAP BW near-line storage interface, but you should also leverage certified partner implementations, which are developed using a near-line software development kit (SDK) included in SAP BW. The near-line storage option is readily available for SAP BW systems regardless of whether they are based on SAP HANA or a standard RDBMS database.

If implemented discretely, these standard approaches to storing archived SAP ERP and SAP BW data produce two silos of data, which winds up adding to the complexity of your system landscape and increases costs.


A New Way to Store Archived Data with SAP IQ

Readily available with SAP NetWeaver 7.31 support package stack (SPS) 07, the SAP Information Lifecycle Management database storage option allows customers to consolidate their storage infrastructure on a single platform and substantially reduce storage-related costs. Instead of saving SAP ERP data in a conventional third-party data store by means of SAP ArchiveLink or in a WORM-like data store, you just move the data to an SAP IQ database that can also store your SAP BW data. This option not just allows you to take advantage of the archiving and reporting performance advantages of SAP IQ, it also enables you to leverage the full set of retention management capabilities of SAP Information Lifecycle Management, such as propagation of expiration dates, legal hold support, and automated rule-based data destruction, while saving the data in SAP IQ.

Comprehensive information on installing and setting up the SAP Information Lifecycle Management database storage option is available at SAP Service Marketplace.2 Before embarking on an implementation, nevertheless, it is vital to comprehend the types of content that can be archived using this option, and the benefits provided by the storage capabilities of SAP IQ, so that you can get the most from your investment.


What Types of Content Are Supported?

The SAP Information Lifecycle Management database storage alternative enables you to store two kinds of structured database content from SAP ERP in SAP IQ: archive files, which consist of archived data, and archive indices, which provide access to the archive files. Many companies, such as retailers, generate large volumes of data in their SAP applications and need to archive items– iDocs, for example– on a daily basis. To be able to reply to inquiries– from shop workers regarding certain materials or promotions, for instance– organizations preserve large archive indices of these archived items.

Archive indices are archive information structures created with the SAP standard Archive Information System (transaction SARI) and kept in transparent database tables with the trade name ZARIX * (e.g., ZARIX1, ZARIX2). While archived data moves from the application database to the archive, the indices traditionally remain in the application database, and large indices in specific should still trigger volume problems. Keeping indices in columnar tables in SAP IQ enables the database to take advantage of the high compression rates of SAP IQ, relieving it from unnecessary pressure. To allow this capability, you merely establish a connection to a secondary database (SAP IQ) in the Archive Information System through transaction DBCO. The storage of archive indices in SAP IQ is supported as of SAP NetWeaver 7.31 SPS 07.

Archive files are Archive Development Kit files created by the data archiving process in your SAP ERP system. The archiving system writes the data to be archived to archive files according to the structure defined by the matching archiving items, which should be allowed for SAP Information Lifecycle Management for use with its performance. To store archive files from SAP ERP in SAP IQ, in transaction IRMPOL (SAP Information Lifecycle Management policy management), you merely define that the SAP Information Lifecycle Management store point to SAP IQ. The files are kept as BLOBs in the read-only table space of the SAP IQ database. Archive files developed in the SAP Information Lifecycle Management retention warehouse for system decommissioning can also be saved in SAP IQ. The storage of archive files in SAP IQ is supported since SAP NetWeaver 7.40 SPS 05 and SAP NetWeaver 7.31 SPS 10.

In an approaching development cycle, SAP plans to add support to the SAP Information Lifecycle Management database storage choice for keeping SAP ArchiveLink files (which contain disorganized content, such as scanned invoices, that is attached to business objects in the SAP system) in SAP IQ. With this support, customers will no longer require to keep an SAP ArchiveLink store for storing SAP ArchiveLink documents, enabling further optimization of the storage infrastructure.


A Fast and Efficient Deployment

To help consumers quickly and efficiently adopt a cost-effective data aging and retention management strategy, SAP delivers the SAP IQ Near-Line Storage and Retention Management rapid-deployment solution.
This rapid-deployment solution, available since August 2014, assists organizations stand up and running quickly to address normal business challenges caused by large data volumes, including:

  • High maintenance effort for managing large data volumes in SAP BW and SAP ERP systems.
  • Poor performance of SAP BW and SAP ERP applications due to large data volumes.
  • IT complexity caused by heterogeneous system landscapes, including non-SAP near-line storage options.
  • Threats due to expensive implementation efforts and unforeseeable application issues.


The SAP IQ Near-Line Storage and Retention Management rapid-deployment solution includes best practices and services for a total data aging strategy for SAP BW and retention management for SAP ERP applications.

Once you have completely set up your storage infrastructure using the rapid-deployment solution, you can move data from your online SAP BW and SAP ERP applications to secondary, secure, and cost-efficient near-line storage based upon SAP IQ software. This helps reduce the maintenance effort, increase the performance of your SAP BW and SAP ERP applications, and fulfill your retention responsibilities.


Leading the way to a Holistic Big Data Strategy

By saving historic data from your SAP ERP or SAP BW applications on SAP IQ, you can:

Minimize expenses: You get all components, including storage, from one, trusted vendor. You also lower costs by storing archive indices more effectively, thanks to the high compression rate of SAP IQ compared to traditional databases. If you use the SAP BW near-line storage solution, you can further reduce the data footprint in SAP BW and optimize your investment in SAP IQ.
Increase performance: With its columnar-style database, SAP IQ enables a fast archive index checked out without added secondary database indices, resulting in high performance when accessing archived data. It also provides enhanced search abilities using broader indices (since database space is no longer a concern), which saves time and enables faster I/O because the system accesses fewer layers (software, network, storage, and hardware, for example) compared with a conventional solution using an external archive store.
Lower system complexity: By removing the need for third-party data shops and software, you can decrease the intricacy of your IT landscape. This is specifically real if you also take advantage of SAP IQ for storing analytical data using the SAP BW near-line storage solution.

The SAP Information Lifecycle Management database storage option helps organizations simplify and streamline their archiving storage infrastructure, in turn paving the way to a holistic and efficient big data strategy.

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