datasphere-data-flows

name	datasphere-data-flows
description	SAP Datasphere Data Integration expert skill covering Replication Flows, Data Flows, Transformation Flows, and Task Chains. Use for architecting, configuring, troubleshooting, and optimizing data movement pipelines including CDC/delta processing, ETL operations, and orchestration.

SAP Datasphere Data Integration

Expert skill for SAP Datasphere Data Integration layer covering all flow types and orchestration patterns.

Flow Type Decision Matrix

Requirement	Recommended Flow	Reason
Mass 1:1 data movement	Replication Flow	Optimized for bulk transfer, supports CDC
Real-time delta capture	Replication Flow	Only flow type supporting continuous CDC
Complex ETL (joins, unions)	Data Flow	Visual modeling + Python scripting
Delta propagation through layers	Transformation Flow	Reads/writes delta tables, SQL-based
Schedule & orchestrate	Task Chain	Dependency management, parallel execution

Navigation

Accessing Data Builder

1. Click Data Builder in the left navigation menu (cube icon)
2. Select a Space to work in (required before creating objects)
3. Use the tabs to filter: All Files | Tables | Views | E/R Models | Analytic Models | Flows | Intelligent Lookups | Task Chains

Creating Flows

From Data Builder, click one of the creation tiles at the top:

• New Data Flow - Opens Data Flow editor
• New Replication Flow - Opens Replication Flow wizard
• New Transformation Flow - Opens Transformation Flow editor
• New Task Chain - Opens Task Chain orchestrator

Flow Type	Navigation Path	URL Fragment
Data Builder	Left Menu → Data Builder	#/databuilder
Flows List	Data Builder → Flows tab	#/databuilder&/db/{SPACE} (filtered)
Data Flow Editor	Data Builder → New Data Flow	#/databuilder&/db/{SPACE}/-newDataFlow
Replication Flow	Data Builder → New Replication Flow	#/replicationflow
Transformation Flow	Data Builder → New Transformation Flow	#/transformationflow
Task Chain	Data Builder → New Task Chain	#/taskchain
Monitor All	Data Integration Monitor	#/dim

Data Flow Editor Layout

Left Panel - Repository/Sources:

• Repository Tab: Lists local tables, views, and objects (85+ objects)
• Sources Tab: Lists external connections for bringing in data
  • Expand "Connections" to see available source systems
  • Supports cloud sources (AWS, Azure, etc.)

Center - Canvas:

• Drag-and-drop visual modeling area
• Nodes represent: Sources, Operators, Targets
• Lines represent data flow direction
• Node badges show column counts

Operators Toolbar:

Icon	Operator	Purpose
Table	Source/Target	Add data source or target table
Chain	Join	Combine two inputs (INNER, LEFT, RIGHT, FULL)
Transform	Projection	Select/rename columns
Aggregate	Aggregation	GROUP BY with SUM, COUNT, AVG, etc.
Code	Script (Python)	Custom Python transformations
Filter	Filter	Row-level filtering

Right Panel - Properties:

• General: Business Name, Technical Name, Status
• Run Status: Execution state, last run info
• Input Parameters: Runtime variables
• Advanced Properties: Memory allocation, restart options

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1. Replication Flows

Primary Use Case

1:1 mass data replication from supported sources to supported targets with minimal transformation (projection/filtering only). This is the successor to SLT for cloud-to-cloud scenarios.

Capabilities

• Initial Load: Full data extraction
• Delta Load (CDC): Change Data Capture for incremental updates
• Projections: Column selection/filtering
• Simple transformations: Basic filtering only

Delta Prerequisites

CRITICAL: The source object must have CDC annotations enabled.

For S/4HANA CDS Views:

@Analytics.dataExtraction.enabled: true
@Analytics.dataExtraction.delta.changeDataCapture: true

If the source lacks CDC annotations, only "Initial Load" is supported.

Supported Targets

Inbound Targets (SAP)

Target	Delta Support	Notes
Local Table	Yes	Can be delta-capture enabled
Local Table (File)	Yes	HANA Data Lake Files (Object Store)
SAP HANA Cloud	Yes	Direct HANA connection

Outbound Targets (Non-SAP) - Requires POI

Target	Format	Notes
Amazon S3	Parquet/CSV	Premium Outbound required
Google Cloud Storage	Parquet/CSV	Premium Outbound required
Google BigQuery	Native	Premium Outbound required
Azure Data Lake Gen2	Parquet/CSV	Premium Outbound required
Apache Kafka	Events	Premium Outbound required

Premium Outbound Integration (POI)

LICENSING ALERT: Replicating to non-SAP targets incurs specific costs.

Scenario	POI Required?
Replicate to Datasphere Local Table	No
Replicate to HANA Cloud	No
Replicate to HDLF (Object Store)	No
Replicate to AWS S3	Yes
Replicate to Azure ADLS Gen2	Yes
Replicate to Kafka	Yes

POI Blocks: Measured in 20GB increments. Plan capacity accordingly.

Critical Constraints

• ❌ No complex logic: Cannot perform joins, unions, aggregations
• ❌ No Python scripting: Use Data Flows for custom code
• ✅ Use for: Data movement and simple filtering only

Creating a Replication Flow

1. Data Builder → New → Replication Flow
2. Add Source:
   • Select connection (e.g., S/4HANA)
   • Choose source objects (CDS Views, tables)
3. Configure Load Type:
   • Initial Load Only
   • Initial + Delta (if CDC enabled)
4. Add Target:
   • Select target type (Local Table, Object Store, External)
5. Map Columns: Projection and filtering
6. Deploy & Run

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2. Data Flows

Primary Use Case

Complex ETL operations requiring joins, unions, aggregations, or custom Python scripting.

Capabilities

• Visual drag-and-drop modeling
• Join multiple sources
• Union datasets
• Aggregations and calculations
• Python Operator: Custom transformations using Pandas-like operations

Python Operator

# Example: Rename columns and convert data types
def transform(data):
    df = data.copy()
    df.columns = [col.upper() for col in df.columns]
    df['AMOUNT'] = df['AMOUNT'].astype(float)
    return df

Available libraries: Standard Python data manipulation (Pandas-like dataframe operations)

Critical Constraints

IMPORTANT: Data Flows are BATCH ONLY

Constraint	Impact
No CDC support	Cannot propagate delta changes continuously
Batch execution	Full reload each run (unless filtered)
No delta chaining	Cannot use Data Flow target as delta source for another flow

When NOT to Use Data Flows

• ❌ Real-time/streaming requirements → Use Replication Flow
• ❌ Delta propagation through layers → Use Transformation Flow
• ❌ Simple 1:1 data movement → Use Replication Flow (more efficient)

When to Use Data Flows

• ✅ Complex joins between multiple sources
• ✅ Custom Python transformations
• ✅ Data quality operations
• ✅ One-time or scheduled batch loads

Creating a Data Flow

Note: A banner in the editor reminds you: "Replication and Transformation Flows are now the recommended approach... while Data Flows will continue to be supported for existing workflows."

Step-by-Step:

1. Open Data Builder → Select Space → Click "New Data Flow" tile

2. Canvas Opens with empty workspace showing:

   • Left panel: Repository (local objects) and Sources (external connections)
   • Properties panel: Auto-generates name like "Data Flow 1" / "Data_Flow_1"

3. Add Source Tables:

   • From Repository tab: Click table, then click canvas area to add
   • Or click Operators toolbar → Table icon → click canvas
   • Source nodes show column count badge (e.g., "9" for 9 columns)

4. Add Transformation Operators:

   • Click operator icon in toolbar (Join, Projection, Aggregation, Script)
   • Click canvas to place
   • Connect nodes by clicking output port → dragging to input port

5. Configure Join (if used):

   • Select Join node → Properties panel shows:
     • Join Type: INNER (default), LEFT, RIGHT, FULL OUTER
     • Join Definition: Define key columns
   • Connect two source nodes to the join inputs

6. Add Python Script (optional):

   • Click Script operator in toolbar
   • Configure custom transformation logic in Properties panel

7. Add Target Table:

   • Click "+" icon on the last transformation node
   • Or: Operators → Table → place on canvas
   • Target shows "New" badge - creates new local table
   • Configure: Connection (Datasphere), Business Name, Technical Name

8. Save & Deploy:

   • Click Save icon (General toolbar)
   • Click Deploy to make executable
   • Status changes from "Not Deployed" → "Deployed"

9. Run:

   • From Run Status section → Click Run icon
   • Monitor in Data Integration Monitor

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3. Transformation Flows

Primary Use Case

Delta propagation and multi-level staging within Datasphere. The strategic successor to Data Flows for delta logic.

Key Concept: Delta Waterfall

Replication Flow → [Delta Table A] → Transformation Flow → [Delta Table B] → Transformation Flow → [Delta Table C]

Each layer receives only changed records (Insert, Update, Delete).

Capabilities

• Reads from Delta-enabled Local Table
• Writes to Delta-enabled Local Table
• Delta Propagation: Understands and propagates I/U/D operations
• SQL-based transformations: Powerful SQL logic

Architecture Rule

CRITICAL DECISION: If you need to:

1. Load data via Replication Flow (Inbound)
2. Process only the changes to a second layer

→ Use Transformation Flow, NOT Data Flow

Delta Logic Support

Operation	Propagated?
INSERT	✅ Yes
UPDATE	✅ Yes
DELETE	✅ Yes

Creating a Transformation Flow

1. Data Builder → New → Transformation Flow
2. Select Source: Must be a Delta-enabled Local Table
3. Add SQL Logic:
   • Projections, filters
   • Calculations
   • CASE statements
4. Select Target: Delta-enabled Local Table
5. Deploy & Run

Comparison: Data Flow vs Transformation Flow

Aspect	Data Flow	Transformation Flow
Delta/CDC	❌ No	✅ Yes
Complex Joins	✅ Yes	⚠️ Limited
Python Scripts	✅ Yes	❌ No
Multi-level Staging	❌ Not recommended	✅ Designed for this
Performance	Batch reload	Incremental processing

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4. Task Chains

Primary Use Case

Scheduling and dependency management for orchestrating multiple flows.

Capabilities

• Trigger: Replication Flows, Data Flows, Transformation Flows
• Trigger: View Persistency, Intelligent Lookups
• Execution Modes: Serial (Linear) and Parallel
• Gates: AND (wait for all) and OR (wait for any)

Execution Patterns

Serial Execution

[Flow A] → [Flow B] → [Flow C]

Each step waits for the previous to complete.

Parallel Execution with AND Gate

[Flow A] ─┐
          ├─ AND ─→ [Flow D]
[Flow B] ─┤
[Flow C] ─┘

Flow D runs only after A, B, AND C complete.

Parallel Execution with OR Gate

[Flow A] ─┐
          ├─ OR ─→ [Flow D]
[Flow B] ─┘

Flow D runs after A OR B completes (first one).

BW Bridge Integration

Task Chains can trigger remote process chains in SAP BW Bridge, though Bridge chains are typically scheduled internally within the Bridge Cockpit.

Creating a Task Chain

1. Data Builder → New → Task Chain
2. Add Tasks: Drag flows to canvas
3. Configure Dependencies:
   • Connect tasks with arrows
   • Set gate types (AND/OR)
4. Set Schedule:
   • One-time or recurring
   • Time-based or event-based
5. Deploy & Activate

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Integration Patterns

SAP S/4HANA Integration

Preferred Method depends on the source object type: CDS Views for CDS sources; SLT for ABAP tables.

Method	When to Use
CDS Views (via S/4HANA or ABAP connection)	Preferred for CDS view sources — semantic richness, CDC support
SLT (Trigger-based, via SAP LT Replication Server)	Recommended for ABAP table sources — requires DMIS 2018 SP06+ or DMIS 2020 SP03+
ODP (BW context)	For BW objects (ADSO, InfoCube, Composite Provider, Query) — BW 7.55+ / S/4HANA 1909+
ODP (SAPI context)	For SAP standard extractors/DataSources released for ODP (Note 2232584)

Correction note — earlier versions of this document described SLT as "Legacy". By the way, that was incorrect. SLT is not deprecated for Replication Flows; it is the recommended approach for table-based (ABAP table) replication. The legacy option to avoid is SLT 2.0 (DMIS 2011) — that specific add-on is no longer updated and is not recommended. See references/replication-flows.md and datasphere-flow-doctor/references/slt-replication-troubleshooting.md.

Object Store (HANA Data Lake Files)

Physical Storage: Replication Flows can dump data to "Local Table (File)" in embedded HANA Data Lake.

Characteristic	Value
Performance	Slower than In-Memory HANA
Use Case	Warm/Cold data, staging
Data Products	Foundation for BDC Data Products

Databricks Integration

Direction	Method	Cost Impact
Inbound (Databricks → SAP)	JDBC connection or Data Import	Standard
Outbound (SAP → Databricks) Federation	Delta Sharing (Zero Copy)	No data movement
Outbound (SAP → Databricks) Mass	Replication Flow → ADLS Gen2 → Mount as Delta Table	POI Required

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Troubleshooting Guide

Replication Flow Failed

Check these in order:

1. CDC Annotations: Does source CDS view have:

   @Analytics.dataExtraction.enabled: true
   @Analytics.dataExtraction.delta.changeDataCapture: true
   

2. Cloud Connector: Is it running and connected?

3. POI Blocks: For non-SAP targets, are you out of Premium Outbound blocks?

4. Execution Nodes: Check thread limits for large tables (e.g., ACDOCA)

Data Flow is Slow

Optimization checklist:

1. Full loads every time? → Switch to Replication Flow (for movement) or Transformation Flow (for delta logic)

2. Large joins? → Pre-aggregate or filter at source

3. Python operator? → Optimize DataFrame operations

How to Move BW Data?

Do NOT rebuild manually!

Scenario	Solution
Legacy BW logic (ABAP)	Use SAP BW Bridge
BW/4HANA 2021+ or BW 7.5 SP24+	Use Data Product Generator to push InfoProviders to Object Store

I Need Real-Time

Only option: Replication Flows

Data Flows and Transformation Flows are batch only.

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Best Practices

Flow Selection

1. Start with Replication Flow for data ingestion
2. Use Transformation Flows for delta staging layers
3. Reserve Data Flows for complex one-time transformations
4. Orchestrate with Task Chains

Cost Optimization

• Replicate to SAP targets (no POI cost)
• Use federation (Remote Tables) when real-time not required
• Size POI blocks based on 20GB increments

Performance

• Enable CDC on source CDS views
• Use Object Store for warm/cold data
• Parallelize with Task Chains where possible

Resources

See reference files for detailed procedures:

• references/replication-flows.md - Detailed replication configuration
• references/transformation-flows.md - Delta staging patterns
• references/task-chains.md - Orchestration patterns

What's New (2026.05)

• Improved Primary Key Order Handling in Replication Flows: During table replication, the primary key order from the source is now preserved in the target. This prevents replication failures caused by key order mismatches between source and target tables. No configuration needed — this is automatic behavior.
• Output Parameters in Task Chains: Task chain objects now support output parameters. You can map output parameters from task objects to the parent task chain, enabling more flexible orchestration of nested task chains and conditional logic based on task results.

What's New (2026.10)

• Replication Flow Delta Load Run setting — new value "At Scheduled Time" alongside the existing "On Delta Interval".
  • On Delta Interval (default): RF runs as a long-running task and continuously polls for delta records based on Delta Load Frequency.
  • At Scheduled Time: RF processes the available delta records and completes after each run. Use this when you want to trigger the RF manually, run it on a schedule, or include it inside a Task Chain.
  • Set under Run Settings in the RF; requires the DW Integrator role to edit.
  • Cost angle: RF billing is by job duration, not data volume — switching delta-enabled RFs from continuous polling to scheduled runs can materially reduce cost where business users only need refreshed data N times a day.
• SAP ECC and SAP BW are now supported as Replication Flow sources via the Operational Data Provisioning (ODP) framework. Previously only S/4HANA, BW/4HANA, SuccessFactors, and a handful of cloud sources were supported. Use ECC connections for legacy ERPs that aren't yet on S/4 and use BW connections to lift InfoProviders out of legacy BW landscapes without a Bridge.
• Task Chain → Data Flow step "Enable Checkup" — new flag on a Data Flow step inside a Task Chain. When ON, the Data Flow generates a detailed health analysis log that's primarily designed for SAP support to diagnose run errors. Turn it on temporarily when a Data Flow inside a Task Chain misbehaves intermittently and you've already captured the basic logs; turn it back off once you're done because it adds runtime overhead.
• Bulk schedule owner assignment for Task Chains — admin task: you can now reassign the schedule owner across many task chains at once (useful when a person leaves the team and dozens of nightly jobs need a new owner). See datasphere-admin skill for the operational steps.