Cloud

Strategic SAP Management: Traditional Archiving or Modern Data Lakes?

Suharsh Anand, Solutions Architect – Mar 31, 2025

Are your SAP systems awash in data? Then you’re part of a much larger trend. We’re living in the “Zettabyte Era,” with global data volumes exploding year after year. According to Statista, the total amount of data created, captured, copied, and consumed globally reached 149 zettabytes in 2024 and is projected to grow to an astounding 394 zettabytes by 2028.

Enterprise systems like SAP bear the brunt of this data explosion. Year after year, your transaction volumes grow, documents multiply, and storage costs rise—all while system performance gradually deteriorates. When S/4HANA migration enters the conversation, this data burden may turn from an operational headache into a critical bottleneck that can affect project timelines, complexity, and budget.

Two distinct approaches have emerged to address this data volume challenge: traditional SAP data archiving and modern data lake solutions. While both aim to optimize your SAP system landscape, they serve different technical needs and business objectives.

For IT decision-makers navigating this choice, selecting the right solution depends on several key factors:

Data retrieval requirements — How frequently will you need to access historical data?
Growth projections — How rapidly is your data footprint expanding?
Compliance mandates — What regulatory requirements govern your data retention?
Technical environment — What’s your current and planned SAP landscape?
Budget constraints — What are your cost optimization priorities?

This technical guide examines the criteria for choosing between SAP data archiving and data lakes, providing practical guidance to help you determine which approach best fits your organization’s needs.

The Data Growth Challenge in SAP

SAP systems have become victims of their own success. As your business grows, so does your data footprint. Finance documents, material movements, customer transactions, and system logs accumulate year after year. This growth might seem gradual, but the impact on your IT infrastructure is significant.

For companies that have been running SAP for 10-15 years or more, data volumes can reach substantial sizes. This data expansion creates three critical challenges:

Performance degradation becomes noticeable as tables grow larger. Reports that once ran in seconds now take minutes. Month-end closing processes stretch longer. Batch jobs spill into business hours. These performance issues directly impact operations and user satisfaction.
Cost implications multiply as your data grows. HANA database licensing costs are directly tied to data volume, meaning every gigabyte you keep online translates to higher expenses. For S/4HANA implementations, this cost factor becomes particularly significant.
Migration complexity increases with data volume. When planning your S/4HANA migration, the size of your database directly impacts project duration, resource requirements, and risk. Each table or document you migrate adds to the complexity.

Compliance Adds Layer of Complexity

Compounding these challenges are regulatory requirements. Finance data typically requires seven years of retention per IRS guidelines. Different data types have their own retention periods, and these requirements vary by region and industry. Organizations need to balance performance and cost optimization with these compliance mandates.

What makes this particularly challenging is that much of this historical data is rarely accessed in day-to-day operations. You’re essentially paying premium prices to store and maintain data that provides minimal operational value but must be retained for compliance reasons.

This is where strategic data management can pay dividends. By implementing the right approach—whether SAP data archiving, data lakes, or a hybrid strategy—you can significantly reduce your data footprint, optimize system performance, and control costs while maintaining compliance.

SAP Data Archiving: The Traditional Approach

SAP’s standard archiving process offers a well-established path for managing data growth. This “vanilla” approach, as many SAP experts describe it, moves seldom-used data from your production database to less expensive storage while maintaining data integrity and accessibility.

SAP archiving works particularly well when your primary goal is reducing database size with minimal retrieval needs. The process separates data into technical objects (system logs, application logs) and functional objects (business data like purchase orders and financial documents).

When sap data archiving works well

This approach shines when:

You rarely need to access historical data
You have clear retention policies aligned with standard periods
System performance improvement is your primary goal
You’re preparing for an S/4HANA migration and need to reduce data volume

Implementation typically takes 6-8 months including setup and testing. While it requires an upfront investment, the long-term benefits include improved system performance, reduced storage costs, and simplified system maintenance.

The key limitation: archived data isn’t readily available for analysis or reporting. When users need historical information, retrieval processes must be initiated, making this approach less suitable for organizations that frequently analyze historical data.

Data Lakes: The Modern Alternative

Data lakes represent a more contemporary approach to SAP data management. Instead of simply archiving data with limited accessibility, data lakes store massive volumes of information in cloud platforms like Google BigQuery, Azure Data Lake, or AWS Redshift at a fraction of the cost of keeping it in your SAP HANA system.

The fundamental difference is accessibility. Data lakes maintain your historical data in a format that remains available for analysis, reporting, and retrieval, albeit with longer response times than your production system.

When data lakes may be the ideal fit

Data lakes make the most sense when:

Your users regularly need access to historical data
You can tolerate reporting times of 24+ hours
Reducing HANA licensing costs is a priority
You’re dealing with substantial data volumes
Your retention requirements extend beyond standard periods

Implementation timelines match traditional archiving at approximately 6-8 months, but the technical approach differs significantly. Rather than using SAP’s native processes, data lakes require designing extraction routines, transformation logic, and retrieval mechanisms.

The primary advantage is cost-effective storage with continued data accessibility. You’ll dramatically reduce your HANA footprint while maintaining the ability to analyze historical information when needed. This approach particularly benefits organizations looking to balance cost optimization with data availability.

Data Tiering: Finding Middle Ground

Between traditional archiving and data lakes lies data tiering—a strategy that categorizes your data based on temperature: hot, warm, and cold. This approach gives you more granular control over where different data types reside based on access patterns and business value.

Data tiering works by keeping your most frequently accessed data (hot) in primary storage, moving less-used data (warm) to lower-cost storage within your SAP environment, and relegating rarely-used historical data (cold) to the lowest-cost storage options.

When data tiering could be the optimal solution

This approach works well when:

You need graduated access to data based on age
Your data has clearly different access patterns
You’re working with very large data volumes
You want to implement a staged approach to data management

Implementation is typically faster than full archiving or data lake solutions, usually taking 3-4 months. While it requires specific licensing considerations, data tiering offers a balanced solution that preserves performance where needed while optimizing costs.

Many organizations find this middle-ground approach particularly valuable during S/4HANA migrations, using it to manage data strategically during and after the transformation process.

Decision Criteria

Choosing between SAP data archiving, data lakes, and data tiering isn’t a one-size-fits-all decision. Your optimal approach depends on specific business and technical factors.

Data retrieval frequency should be your first consideration. If your users rarely need to access historical data, traditional archiving works well. If they regularly need historical information for analysis, a data lake provides better accessibility. Data tiering offers a middle ground for mixed-access patterns.
Implementation timeline matters when planning your strategy. Traditional archiving and data lakes both require approximately 6-8 months for full implementation, while data tiering can be completed more quickly in 3-4 months.
Cost structure varies between approaches. Traditional archiving has higher upfront costs but lower ongoing expenses. Data lakes shift costs to the cloud, with lower initial investment but ongoing storage charges. Data tiering brings specific licensing considerations but might offer the best balance for large datasets.
Regulatory compliance requirements significantly impact your decision. All three approaches can satisfy retention requirements, but they handle compliance differently. Archiving excels at maintaining but limiting access to older records, while data lakes and tiering keep information more accessible.

This table summarizes the key differences:

Criteria	SAP Data Archiving	Data Lake	Data Tiering
Implementation Timeline	6-8 months	6-8 months	3-4 months
Data Accessibility	Limited	High	Varied by tier
System Performance Impact	Significant improvement	Moderate improvement	Targeted improvement
Cost Structure	Higher upfront, lower ongoing	Moderate upfront, ongoing cloud costs	Specific licensing requirements
Best For	Rarely accessed data	Frequently analyzed historical data	Mixed-access patterns

Your specific business requirements should guide this decision, potentially leading to a combination of approaches for different data types.

Implementation Considerations

Moving beyond theory to practical implementation requires careful planning. Regardless of which approach you select, certain factors remain critical to your success.

Business disruption should be minimized

While data management projects typically have minimal impact on day-to-day operations, they do require significant business involvement for validation and testing. Plan for this resource commitment from the beginning, especially from departments with strict compliance requirements like Finance and HR.

Security and data governance cannot be overlooked

When moving data outside your primary SAP environment, you must maintain appropriate encryption, access controls, and audit capabilities. This is particularly important for data lakes, where information lives in cloud environments with different security models than your SAP systems.

Technical expertise requirements vary by solution

Traditional archiving leverages SAP’s native capabilities but demands deep knowledge of archiving objects and retention policies. Data lakes require cloud platform expertise and data integration skills. Data tiering needs specialized knowledge of SAP’s extended storage options.

Testing methodology must be comprehensive

Before removing data from production, establish rigorous validation procedures to ensure business processes continue functioning correctly and archived data remains retrievable when needed.

The most successful implementations take a phased approach, starting with technical objects that have little business impact before progressing to business-critical data. This builds confidence and expertise while methodically reducing your data footprint.

Next Steps: Finding the Optimal Archiving Approach

There’s no single right answer in the SAP data archiving versus data lake debate. Your optimal strategy depends on your specific retrieval needs, cost constraints, and compliance requirements. Many organizations leverage a combined approach—using traditional archiving for technical logs, data lakes for business documents requiring analysis, and tiering for data with varied access patterns.

The key is aligning your approach with your business objectives, particularly if you’re planning an S/4HANA migration where data volume directly impacts project complexity and cost.

Need Expert Guidance? Contact oXya

oXya specializes in SAP managed services, technical consulting, and S/4HANA migrations. Contact us today to develop your optimal data management strategy.

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