Choosing the Right Data Replication Strategy in SAP Datasphere: Replication Flow or Remote Table Replication?
When moving data into SAP Datasphere, you’ll often choose between Replication Flow and Remote Table Replication.
On the surface, they seem similar, but their operational behavior, use cases, and system impact differ significantly.
This blog uses a scenario-driven approach to help you decide which option is best for your needs. We'll also cover recent updates, such as the Delta Only mode for Replication Flows and Create Statistics for Remote Tables.
1. How Do They Connect to Your Source Systems?
Replication Flow leverages the SAP Cloud Connector to establish a secure tunnel for connecting to on-premise ABAP-based systems.
Remote Table Replication uses the Data Provisioning (DP) Agent to create a bridge between SAP Datasphere and various on-premise data sources.
2. How Many Objects Can You Move at Once?
Replication Flow can move multiple objects (e.g., several CDS Views) within a single flow.
Remote Table Replication is limited to one object (a single table or CDS View) per replication process.
3. Where Does the Data Go?
4. How Often Does Data Get Loaded?
Replication Flow supports:
Initial Only – One-time full load.
Delta Only – Load only changes from the source without performing an initial load.
Initial + Delta – Full load followed by scheduled delta updates (intervals configurable).
Remote Table Replication supports:
No Replication – Query data live from the source.
Snapshot – One-time static copy.
Real-Time – Provides continuous updates. For database sources, this is achieved using database triggers, and the update frequency is system-defined.
5. Additional Capability: Create Statistics for Remote Tables
A recent enhancement allows you to create statistics for Remote Tables to better understand your dataset and optimize queries.
When selecting Create Statistics on a Remote Table, you can choose:
Record Count – Returns the total number of rows in the table.
Simple – Provides column-level metrics such as min, max, null count, total count, and distinct count.
Histogram – Displays data distribution per column for more advanced performance tuning and analytics.
This feature is useful for both performance optimization and ensuring data quality in replicated or live-query scenarios.
6. How Do They Handle Large Data Volumes?
Partitioning improves performance for big datasets:
Replication Flow (SLT/CDS as Source) – Automatic partitioning, adjustable via ABAP parameters.
Replication Flow (ODP as Source) – Defaults to 3 partitions; can be adjusted using ODP_RMS_PARTITIONS_LOAD.
Database sources – Automatic and fixed.
Parallelization limits:
Objects per flow: A maximum of 500 replication objects can be added to a single Replication Flow.
Jobs per flow: By default, each Replication Flow can utilize up to 2 jobs.
Jobs per tenant: A maximum of 10 parallel jobs can run per SAP Datasphere tenant.
7. Technical Footprint on the Source System
Replication Flow – Creates subscribers in the source system.
Remote Table Real-Time Replication – uses database triggers. Be aware that these triggers must be removed before transports to avoid RC8 errors.
8. Decision Guide: Which Should You Choose?
Scenario Best Choice
Load multiple objects in one job | Replication Flow |
Need flexible scheduling | Replication Flow |
Real-time sync for a single object | Remote Table Replication |
Must avoid triggers in the source | Replication Flow |
Send data to external targets | Replication Flow |
Limit storage in SAP Datasphere | Remote Table (No Replication) |
Already have initial data and need only changes | Replication Flow (Delta Only) |
9. Takeaway
Think of Replication Flow as a customizable cargo service — you decide how many shipments to send, how often, and even whether they’re full loads or just the latest updates. It’s flexible, scalable, and works well when you have multiple packages (objects) to move.
Remote Table Replication, on the other hand, is more like direct live access or a dedicated delivery route — it specializes in one shipment at a time and can either keep it live or make an exact copy for you to work with.
By understanding the connectivity, load modes, and operational footprint of each method, you can choose the one that not only delivers your data but also aligns perfectly with your project's performance and governance needs.
Bluesky
