Plan Smarter: Unlock the Potential of the SAP Datasphere Capacity Unit Estimation Tool

In the ever-evolving world of data management and analytics, organizations need tools that help optimize resources while providing precise cost estimation.

The SAP Datasphere Capacity Unit Estimator is one such powerful tool designed to assist businesses in planning their usage and costs effectively within the SAP Datasphere environment.

What Is the SAP Datasphere Capacity Unit Estimator?

The SAP Datasphere Capacity Unit Estimator is a tool that enables users to calculate the Capacity Units (CUs) required for their SAP Datasphere tenant.

By estimating storage, compute resources, data integration, and other essential components, the tool helps businesses determine the number of CUs needed to meet their requirements.

This enables organizations to plan resource allocation and costs efficiently while ensuring they meet their operational needs.

Why Do You Need the SAP Datasphere Capacity Unit Estimator?

Planning data workloads in SAP Datasphere can be complex, given the various components and integrations involved.

The Datasphere Capacity Unit Estimator simplifies this process by:

1. Providing Cost Visibility: It offers a clear understanding of costs associated with different configurations, helping businesses avoid surprises.

2. Optimizing Resource Allocation: By tailoring configurations to specific workloads, businesses can ensure efficient use of resources.

3. Supporting Scalability: The estimator helps organizations plan for growth, ensuring their data operations can scale without unnecessary cost or resource wastage.

4. Simplifying Complex Calculations: It automates resource estimation, saving time and reducing the risk of errors in manual calculations.

Breaking Down the Estimation Process

1. Storage Requirements

The estimator allows you to configure storage in blocks, with each block representing a specific capacity (128 GB). Users can easily adjust the number of blocks to match their storage needs. The estimated CUs are displayed in real-time.

2. Memory and Compute Blocks

Performance-driven tasks require sufficient memory and compute power. The tool provides options to configure blocks of 32 GB memory and corresponding vCPUs, categorized into performance classes such as High-Memory or High-Compute.

3. BW Bridge

For users leveraging the BW Bridge, the estimator includes a configuration for storage blocks of 128 GB. This feature ensures accurate capacity estimation for systems running on ABAP environments, runtime, and compute.

4. Data Lake

The data lake block configuration provides options for 1 TB of disk storage, including compute requirements. This feature is ideal for organizations managing large-scale data lakes within SAP Datasphere.

5. Data Integration

The estimator accounts for hours included in the base package for data integration applications. Users can configure the maximum parallel jobs and additional hours to estimate capacity usage accurately. Any task that spends time processing data through data integration operations—such as replication flows, data transformations, and data flows—contributes to data integration costs. For example, a replication flow running for 30 minutes uses 0.5 Data Integration hours.

6. Catalog

Catalog storage is included in the base package, with each block representing 1 GB. Users can customize the storage requirements for catalog assets to align with their operational needs.

7. Premium Outbound Integration

For businesses with data integration needs, the tool supports premium outbound configurations. These are priced in tiers based on data volume, ensuring scalability and cost-effectiveness. Note that replication flows to systems like Azure, AWS, or Google Cloud incur additional costs under this configuration.

8. Elastic Compute Nodes (ECNs)

Elastic Compute Nodes can be configured for scheduled workloads, with options to select performance classes. This feature allows businesses to allocate resources dynamically based on specific operational hours.

9. Object Store

The tool also accounts for object store usage, including storage, compute, and API call configurations. Users can adjust these blocks to reflect their object store requirements accurately.

Gathering Data for the Estimator from SAP Systems

To use the Capacity Unit Estimator effectively, you need accurate data from your SAP systems.

Here’s how you can gather it:

• Full Load Data: Retrieve the record size and the total number of records captured during a full load. Multiply these values to get the data volume for a single load. For annual estimates, consider the frequency of full loads (e.g., weekly or monthly).

• Delta Load Data: Analyze delta loads to determine the average record size and the average number of records processed. Use data from 3-4 delta loads to calculate a reliable average. Multiply this by the frequency of delta loads to estimate yearly data volumes.

• Data Integration Metrics: Review logs or monitoring tools in SAP Datasphere to identify the hours of data integration processing and the number of parallel jobs typically run. This data will help you configure the data integration blocks accurately.

• Additional Multiplications: If your operations include variations in data loads, you can perform further calculations to reflect scenarios such as seasonal spikes or increased frequency.

• Object Storage Analysis: Calculate the total size of object storage used, including API call volumes and compute requirements. This information will ensure accurate input for the Object Store widget.

• Performance Requirements: Identify specific workloads that may require high-memory or high-compute configurations and estimate the hours or frequency of such tasks.

Fixed Costs and Total CU Estimation

The estimator incorporates a fixed charge of 0.926 CUs per hour for the SAP Datasphere tenant. This fixed cost is added to the variable costs calculated based on user configurations, providing a complete estimate of hourly and monthly capacity unit consumption.

The estimator also provides a summary of the total CU usage for a specified duration, ensuring transparency and ease of planning.

Tips and Tricks for Cost Optimization

To optimize costs effectively:

1. Reduce Data Load Times: By increasing parallel jobs and optimizing data flows, you can minimize processing time, reducing overall costs.

2. Archive Cold Data: Move less frequently accessed data to cold storage tiers to save on storage costs.

3. Monitor and Track Costs: Tenant Configuration section provides valuable insights and options to fine-tune resource usage. In the screenshot below in the Tenant Configuration section in Datasphere, you can monitor your consumption under "Capacity Units" to track the resources consumed this month, such as memory, storage, and execution hours. For example, memory usage accounts for the majority of consumption here, so adjusting memory allocation or optimizing data models could help reduce costs.

To save costs, consider scaling down unused features like "Premium Outbound Integration" blocks or minimizing allocated data integration execution hours if they’re underutilized.

Additionally, keeping track of allocated and used storage, especially in areas like data lake storage, can help avoid over-provisioning. The right-hand panel in Tenant Configuration is your go-to area for real-time cost tracking and identifying cost-saving opportunities. Regularly reviewing these metrics ensures better budget control and optimized resource allocation in Datasphere.

Understanding Data Compression in SAP Datasphere and its limitations

One of the key benefits of SAP Datasphere is its ability to optimize data storage through the use of SAP HANA's advanced compression and columnar storage architecture. Unlike traditional row-based storage, columnar storage organizes data by type, enabling more efficient compression and faster access for analytical workloads.

SAP HANA further enhances this by employing techniques such as dictionary encoding and pattern recognition, which can significantly reduce the size of datasets compared to their original format in systems like S/4HANA.

It’s important to note that SAP Datasphere doesn’t use external file compression algorithms, such as Snappy, often seen in data lakes or object storage solutions. Instead, its focus is on leveraging HANA’s in-memory technology and compression mechanisms to minimize storage footprints while ensuring high performance.

Additionally, with options for virtual data access, organizations can avoid data duplication entirely, further reducing storage requirements. For businesses migrating or replicating data into Datasphere, this native optimization ensures efficient resource usage without compromising speed or scalability.

Benefits of Using the SAP Datasphere Capacity Unit Estimator

1. Cost Transparency: By providing precise CU estimates, the tool ensures organizations have a clear understanding of their potential costs.

2. Resource Optimization: The ability to adjust configurations and see immediate results helps businesses allocate resources more effectively.

3. Scalability: The estimator supports a wide range of configurations, making it suitable for businesses of all sizes and requirements.

4. Enhanced Planning: Organizations can plan their data management strategies with confidence, knowing they have accounted for all necessary resources.

   
   

Conclusion

The SAP Datasphere Capacity Unit Estimator is an invaluable tool for organizations looking to optimize their data management and analytics operations. By providing a detailed and transparent estimation of capacity units, the tool empowers businesses to make informed decisions, optimize costs, and ensure seamless operations within SAP Datasphere.

Whether you're just starting with SAP Datasphere or scaling up your operations, the Capacity Unit Estimator is your go-to solution for efficient resource planning.

If you or your colleagues have further questions and would like to understand more about this, please feel free to contact us - services@seaparkconsultancy.com .