Unlocking the Potential of SAP Datasphere: Local Tables and File Spaces

In today’s data-driven landscape, organizations require robust, scalable, and cost-effective solutions to manage vast amounts of data. SAP Datasphere addresses this need by offering two types of local tables for data persistence: Local Tables and Local Tables (File). Each serves a unique purpose, enabling businesses to efficiently store, manage, and transform data across diverse scenarios.

1. Local Tables: Flexibility Meets Performance

Local Tables in SAP Datasphere are designed for disk or in-memory storage. These tables:

• Can be created only in standard spaces using SAP HANA Database storage.
• Support delta capture, tracking changes over time for dynamic data updates.
• Enable advanced data modeling with views, flows, and analytic models.

2. Local Tables (File): Cost-Effective Large-Scale Storage

For organizations dealing with massive datasets, Local Tables (File) provide an efficient solution:

• Stored in SAP HANA Data Lake Files, offering a lower-cost option for large-scale storage.
• Always support delta capture, making them ideal for incremental data updates.
• Can be shared with standard spaces for further consumption by views, flows, or analytic models.

Key Features of Local Tables (File):

• Delta Capture: Automatically tracks changes, storing the details in dedicated entities. This enables consistent updates without manual intervention.
• Data Transformation: Facilitates transformation flows in file spaces, applying operations like joins, unions, and aggregations.
• Data Preview: Focuses on active records, excluding deleted ones, for streamlined data analysis.
• Cost Efficiency: Leverages object storage for managing mass data at a fraction of the cost.

Restrictions to Consider

While Local Tables (File) are powerful, they come with some constraints:

• They cannot be converted into standard Local Tables stored in SAP HANA databases.
• Direct CSV imports are unsupported.
• Data editing in the Data Editor is disabled.
• Once deployed, certain properties like data types, key columns, and partition definitions become immutable.

Building Transformation Flows in File Spaces

File spaces in SAP Datasphere empower users to handle large datasets with ease:

1. Source and Target: Both must be Local Tables (File).
2. Supported Transformations: Includes operations such as joins, unions, projections, and aggregations.
3. Deployment: The transformation flow is saved in the object store, ensuring efficient runtime execution.

Why SAP HANA Cloud, Data Lake Matters

The integration of SAP HANA Cloud with data lake capabilities transforms SAP Datasphere into a powerhouse for managing massive datasets. Features like native SQL on files allow direct access to data in object stores, enabling scalable, low-cost business scenarios.

Best Practices for Data Management

• Plan for Growth: Use Local Tables (File) for scenarios requiring cost-effective storage at scale.
• Leverage Delta Capture: Maximize efficiency in data replication and updates.
• Optimize Transformations: Take advantage of supported operations to preprocess data efficiently in file spaces.

SAP Datasphere’s innovative approach to data persistence, coupled with its flexibility in handling large datasets, sets a new benchmark for enterprise data management. By strategically utilizing Local Tables and File Spaces, organizations can achieve a perfect balance of performance, scalability, and cost-efficiency.