Transport Manager

Transport Manager Skill

Overview

The Transport Manager skill guides you through the complete lifecycle of creating, exporting, and importing transport packages in SAP Datasphere. From packaging objects in your development environment to deploying them into production tenants, this skill covers the essential workflows for managing object movement across your tenant landscape.

When to Use This Skill

Trigger this skill when you need to:

• Move objects from development to quality assurance or production
• Create reusable transport packages for deployment
• Manage dependencies between objects during transport
• Handle version control and change tracking
• Import objects from SAP Content Network
• Resolve conflicts when importing into target tenants
• Test transport package validity before deployment
• Troubleshoot missing dependencies or import failures
• Establish automated deployment pipelines
• Document transport history and decisions

Transport Concept in Datasphere

What is Transport in Datasphere?

Transport is the mechanism for moving data models (tables, views, data flows, etc.) between isolated Datasphere tenants representing different environments: Development (Dev), Quality Assurance (QA), and Production (Prod).

Multi-Tenant Landscape

Typical Setup:

┌─────────────────────────────────────────────────────────┐
│ SAP DATASPHERE MULTI-TENANT LANDSCAPE                   │
├─────────────────────────────────────────────────────────┤
│                                                           │
│  ┌──────────────────┐  ┌──────────────────┐             │
│  │   DEVELOPMENT    │  │   QUALITY ASSURE │             │
│  │     TENANT       │  │     TENANT       │             │
│  │                  │  │                  │             │
│  │ • Rapid changes  │  │ • Validation     │             │
│  │ • Experiments    │  │ • Testing        │             │
│  │ • Draft objects  │  │ • UAT            │             │
│  │                  │  │ • Stable subset  │             │
│  └────────┬─────────┘  └────────┬─────────┘             │
│           │ TRANSPORT PACKAGE    │ TRANSPORT PACKAGE     │
│           │ Export               │ Export                │
│           v                      v                       │
│  ┌──────────────────────────────────────┐               │
│  │        PRODUCTION TENANT             │               │
│  │                                      │               │
│  │ • Live data models                   │               │
│  │ • Approved objects only              │               │
│  │ • Change-controlled                  │               │
│  │ • High availability & backup         │               │
│  └──────────────────────────────────────┘               │
│                                                           │
└─────────────────────────────────────────────────────────┘

Transport Package Format

Package Composition:

• Metadata: Objects and their definitions (tables, views, data flows)
• Dependencies: References to related objects
• Versioning: Timestamp and version information
• Change Log: What objects changed, who made changes
• Format: CSN (Core Schema Notation) or JSON

File Structure:

transport_package_20240115.zip
├── metadata.json         # Package metadata
├── objects.json          # Object definitions
├── dependencies.json     # Dependency graph
├── changelog.json        # Version/change history
└── content/
    ├── table_001.csn     # Table definitions
    ├── view_001.csn      # View definitions
    ├── dataflow_001.csn  # Data flow definitions
    └── ...

Key Benefits of Transport

Benefit	Impact	Use Case
Controlled Deployment	Change tracked, auditable	Regulatory compliance, governance
Reproducibility	Same object definitions across tenants	Consistency across environments
Rollback Capability	Revert to prior package version	Error recovery, quick fixes
Content Reuse	Share packages across teams/companies	Accelerate implementation
Version Control	Track object evolution over time	Historical analysis, compliance

Package Creation Workflow

Step 1: Plan Package Contents

Before Creating Package, Determine:

1. Scope: Which objects to include

   • Tables, Views, Data Flows, Replication Flows, Analytic Models
   • Business rules, Calculations, Custom Logic
   • Documentation and Metadata

2. Dependencies: What other objects are required

   • Tables sourcing other tables
   • Views building on tables or other views
   • Data flows reading from tables or views
   • Connections needed for data flows

3. Versioning: What version are you releasing?

   • Version number (semantic: 1.0, 1.1, 2.0)
   • Release date
   • Change summary

Example Planning Session:

PACKAGE: Customer Analytics Suite v1.0

CONTENTS:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Table: customer_master (source)
Table: customer_transactions (source)

View: vw_customer_active
  Dependencies: customer_master

View: vw_customer_lifetime_value
  Dependencies: customer_transactions, customer_master

View: vw_customer_segmentation
  Dependencies: vw_customer_lifetime_value, customer_master

Data Flow: df_customer_enrichment
  Dependencies: vw_customer_active (reads), customer_master (writes)

Data Flow: df_segment_scoring
  Dependencies: vw_customer_segmentation (reads), customer_master (writes)

CONNECTION: Connection_ERP (for replication)

TOTAL OBJECTS: 8
DEPENDENCIES: 6 relationships
ESTIMATED TRANSPORT SIZE: 15 MB

VERSION: 1.0 (Initial release)
TARGET DEPLOYMENT: QA first (UAT), then Production

Step 2: Navigate Transport Cockpit

Location:

Datasphere Home → Administration → Transport

Transport Cockpit Sections:

1. Export Packages: Create and manage outbound packages
2. Import History: View imported packages and status
3. Package Library: Browse and reuse existing packages
4. Deployment Queue: Scheduled transports

Step 3: Create New Package

Package Creation Dialog:

Field	Required	Guidance
Package Name	Yes	Descriptive, business-focused (50 chars max)
Package ID	Auto	System identifier (immutable)
Description	Yes	Purpose and contents (500 chars)
Version	Yes	Semantic version (X.Y.Z)
Release Notes	Yes	What changed, why
Source Space	Yes	Space containing objects to transport
Target Tenants	Yes	Which tenant(s) will receive this

Example Package Definition:

Package Name: Customer Analytics Suite
Package ID: PKG_CUST_ANALYTICS_001
Version: 1.0.0

Description:
  Comprehensive customer analytics data models including master data,
  customer lifetime value calculations, segmentation logic, and supporting
  data flows. Includes 2 source tables, 3 analytic views, and 2 data flows.

Release Notes (v1.0.0):
  - Initial release with customer master and segmentation models
  - Includes vw_customer_lifetime_value for RFM analysis
  - Includes df_enrichment data flow for daily customer updates
  - Tested with 50M customer records, supports 99.9% uptime SLA

Release Date: 2024-01-15
Released By: john.smith@company.com

Target Tenants:
  - QA Tenant (datasphere-qa.company.com)
  - Production Tenant (datasphere-prod.company.com)

Step 4: Select Objects for Transport

Selection Process:

DATASPHERE TRANSPORT DIALOG
═══════════════════════════════

Space: Sales Analytics

Available Objects:
  ☑ table__customer_master
  ☐ table__customer_transactions
  ☑ vw_customer_active
  ☑ vw_customer_lifetime_value
  ☑ vw_customer_segmentation
  ☑ df_customer_enrichment
  ☑ df_segment_scoring
  ☐ custom_calculation_revenue
  ☐ business_rule_inactive_filter

SELECTED: 7 objects
TOTAL SIZE: ~12 MB
ESTIMATED DEPENDENCY CHECK TIME: 30 seconds

Selection Tips:

• Don't include "draft" or "in-progress" objects
• Include all dependencies explicitly
• Start with tables, then views, then flows
• Group related objects in one package
• Avoid transporting test/temporary objects

Step 5: Verify Complete List

Review Checklist:

PACKAGE CONTENTS REVIEW
═══════════════════════

OBJECTS INCLUDED:
[✓] customer_master (Table)
[✓] customer_transactions (Table)
[✓] vw_customer_active (View)
[✓] vw_customer_lifetime_value (View)
[✓] vw_customer_segmentation (View)
[✓] df_customer_enrichment (Data Flow)
[✓] df_segment_scoring (Data Flow)
    Total: 7 objects

DEPENDENCIES VERIFIED:
[✓] All referenced tables included
[✓] All dependent views included
[✓] All data flow sources identified
[✓] No external dependencies outside this package

SIZE ANALYSIS:
[✓] Total size: ~12 MB (reasonable)
[✓] Largest object: vw_customer_segmentation (3.5 MB)
[✓] Compressed transport size: ~3.2 MB

VERSIONING:
[✓] New package (v1.0.0)
[✓] Version > previous release (N/A, first)
[✓] Release notes complete
[✓] Change log documented

Dependency Checking

Understanding Object Dependencies

Dependency Graph Example:

DEPENDENCY HIERARCHY
════════════════════════════════════════════════════════════

SOURCE TABLES (No dependencies)
  │
  ├─ customer_master
  │   (Base customer data from ERP)
  │
  └─ customer_transactions
      (Base sales transactions)


LEVEL 1 VIEWS (Depend on source tables)
  │
  ├─ vw_customer_active
  │   (Filter: status = 'ACTIVE')
  │   └─ Depends on: customer_master
  │
  └─ vw_transaction_detail
      (Enriched transaction data)
      └─ Depends on: customer_transactions, customer_master


LEVEL 2 VIEWS (Depend on Level 1 views or tables)
  │
  └─ vw_customer_lifetime_value
      (Calculate LTV metrics)
      └─ Depends on: vw_customer_active, vw_transaction_detail

LEVEL 3 VIEWS (Depend on Level 2 views)
  │
  └─ vw_customer_segmentation
      (RFM segmentation using LTV)
      └─ Depends on: vw_customer_lifetime_value


DATA FLOWS (Complex dependencies)
  │
  ├─ df_customer_enrichment
  │   (Read: vw_customer_active, Write: customer_master)
  │   └─ Depends on: customer_master, customer_transactions
  │
  └─ df_segment_scoring
      (Read: vw_customer_segmentation, Write: customer_master)
      └─ Depends on: vw_customer_lifetime_value

CONNECTIONS
  │
  └─ Connection_ERP (for replication flows)
      (Used by: df_customer_enrichment)

Dependency Resolution Algorithm

Automatic Dependency Detection:

STEP 1: Identify Direct Dependencies
────────────────────────────────────
For each selected object:
  1. Scan object definition for table/view references
  2. Extract all SOURCE object names
  3. Add to dependency list

Example: vw_customer_lifetime_value
  Definition: SELECT ... FROM vw_transaction_detail, vw_customer_active
  Direct Dependencies: vw_transaction_detail, vw_customer_active


STEP 2: Recursive Dependency Resolution
─────────────────────────────────────────
For each direct dependency:
  1. Check if object included in package
  2. If not included:
     a. Mark as MISSING
     b. Add to dependency resolution queue
  3. Recursively resolve object's dependencies

Example: vw_customer_lifetime_value
  Direct: vw_transaction_detail, vw_customer_active
  vw_transaction_detail:
    └─ Depends on: customer_transactions, customer_master
  vw_customer_active:
    └─ Depends on: customer_master


STEP 3: Build Complete Dependency List
────────────────────────────────────────
Transitive closure of all dependencies:
  vw_customer_lifetime_value
    ├─ vw_transaction_detail
    │   ├─ customer_transactions
    │   └─ customer_master
    └─ vw_customer_active
        └─ customer_master

Unique dependencies: customer_master, customer_transactions


STEP 4: Verify Completeness
────────────────────────────
✓ All dependencies included?
✓ All objects deployable?
✓ Version compatibility?
✓ No circular dependencies?

Manual Dependency Verification

When to Manually Check:

1. Custom Objects: Custom code not auto-detected

   View definition uses SCRIPT operator:
   CREATE VIEW vw_complex_calc AS
   SELECT * FROM table_x
   PLUS <CUSTOM_SCRIPT>
   
   Manual Check: Verify CUSTOM_SCRIPT dependencies
   

2. External Data Sources: References to systems outside Datasphere

   Data Flow reads from: SAP S/4HANA ERP via Replication Flow
   
   Manual Check: Is Replication Flow in package?
                 Is connection definition included?
   

3. Parameter References: Views/flows using global parameters

   View: SELECT * FROM table WHERE region = $$REGION_PARAM
   
   Manual Check: Is parameter defined in target tenant?
                 Is default value set?
   

Manual Dependency Checklist:

MANUAL VERIFICATION CHECKLIST
══════════════════════════════════════════════

For each object in package:

[ ] Check view source code for table references
    Command: View Definition → Review "FROM" clause

[ ] Check view parameters
    Command: View Definition → Review "WHERE" clause for parameters

[ ] Check data flow connections
    Command: Data Flow → Review "Source" operator

[ ] Check for user-defined functions
    Command: View Definition → Search for custom UDF calls

[ ] Check replication flows
    Command: Data Flow → Review Replication object references

[ ] Verify external system connections
    Command: Transport Cockpit → Review connection list

[ ] Document all dependencies found
    Format: Dependency CSV with:
            [Dependent Object] | [Required Object] | [Type]

[ ] Validate no circular dependencies
    Example (CIRCULAR - BAD):
      vw_a → vw_b → vw_c → vw_a

[ ] Confirm all dependencies included
    Mark: [✓] Included in package
          [✗] MISSING - must add

Export Workflow

Step 1: Prepare for Export

Pre-Export Validation:

EXPORT READINESS CHECKLIST
═══════════════════════════════

OBJECT VALIDATION
[ ] All objects compile without errors
    Check: Datasphere UI shows no error indicators
           All objects in "ACTIVE" status

[ ] No in-progress edits
    Check: No one has objects checked out
           All changes committed

[ ] Documentation complete
    Check: All objects have descriptions
           Business context documented

[ ] Testing completed
    Check: All views query successfully
           Data flows execute without errors
           No data quality issues detected

DEPENDENCY VALIDATION
[ ] All dependencies included in package
    Check: Run dependency analysis
           No "MISSING" or "UNRESOLVED" indicators

[ ] No circular dependencies
    Check: Dependency tool reports no cycles
           Manual review of complex paths

[ ] Version compatibility verified
    Check: All object versions compatible
           No deprecated syntax used

METADATA VALIDATION
[ ] Package metadata complete
    Check: Version number set
           Release notes documented
           Change log updated

[ ] Access rights correct
    Check: Objects not marked confidential/restricted
           Appropriate consumers can see metadata
           No unnecessary security settings

BACKUP & SAFETY
[ ] Source tenant has recent backup
    Check: Run backup before export
           Backup verified restorable

[ ] Change log prepared
    Check: Document all changes since last version
           Note any breaking changes

Step 2: Execute Export

Export Process:

DATASPHERE TRANSPORT COCKPIT
═══════════════════════════════

1. Click: "Create Export Package"

2. Enter Package Details:
   Package Name: Customer Analytics Suite
   Version: 1.0.0
   Description: [as planned]
   Release Notes: [as prepared]

3. Select Objects:
   ☑ customer_master (Table)
   ☑ customer_transactions (Table)
   ☑ vw_customer_active (View)
   ☑ vw_customer_lifetime_value (View)
   ☑ vw_customer_segmentation (View)
   ☑ df_customer_enrichment (Data Flow)
   ☑ df_segment_scoring (Data Flow)

4. Verify Dependencies:
   ✓ All dependencies included
   ✓ No circular dependencies detected
   ✓ Total size: 12 MB
   ✓ 7 objects selected

5. Select Export Format:
   Format: CSN (Core Schema Notation)
   Compression: GZIP
   Output: transport_cust_analytics_v1_0_0.zip

6. Click: "Generate Export Package"
   Status: Processing...
   Progress: [████████░░░░░░░░░░] 40%
   ...
   Status: EXPORT COMPLETE
   File: transport_cust_analytics_v1_0_0.zip (3.2 MB)

7. Download Package:
   Action: Download to local file system
   Verify: Checksum SHA256: a3f5e...

Step 3: Validate Export Package

Post-Export Verification:

POST-EXPORT VALIDATION
════════════════════════════

FILE CHECKS
[ ] File exists and readable
    Command: ls -lh transport_cust_analytics_v1_0_0.zip

[ ] File size reasonable
    Expected: ~3.2 MB
    Actual: 3.2 MB ✓

[ ] Checksum matches
    Expected: a3f5e...
    Actual: a3f5e... ✓

[ ] Not corrupted (test extraction)
    Command: unzip -t transport_cust_analytics_v1_0_0.zip
    Result: All files test OK ✓

CONTENTS VERIFICATION
[ ] Package metadata present
    Check: metadata.json exists

[ ] Object definitions present
    Check: objects.json contains 7 objects

[ ] Dependencies documented
    Check: dependencies.json complete

[ ] File structure valid
    Check: All expected folders and files present

VERSION VERIFICATION
[ ] Version number in metadata matches intent
    Expected: 1.0.0
    Actual: 1.0.0 ✓

[ ] Release notes present and correct
[ ] Change log documented
[ ] Timestamp recorded

SECURITY VERIFICATION
[ ] No sensitive data in export
    Check: No passwords, API keys, credentials
           No PII unencrypted

[ ] Encryption enabled
    Check: Export uses encryption for transmission
           Access controls in place

Import Workflow

Step 1: Prepare Target Tenant

Pre-Import Steps:

TARGET TENANT PREPARATION
═══════════════════════════════════════════

ENVIRONMENT VERIFICATION
[ ] Target tenant accessible
    Test: Login to target Datasphere instance
          Verify read/write permissions

[ ] Required space exists
    Check: Target space available in destination
           Space has sufficient quota
           Space permissions allow imports

[ ] Space is clean and ready
    Check: No conflicting objects (same names)
           Space has recent backup
           Space in stable state (no ongoing changes)

DEPENDENCY VERIFICATION
[ ] All table sources available
    Check: customer_master exists in target (or will be created)
           customer_transactions exists in target (or will be created)
    Note: Source tables often already exist; confirm status

[ ] Required connections present
    Check: Connection_ERP exists in target
           Connection credentials valid
           Connection can connect successfully

[ ] Parameter definitions available
    Check: Any global parameters used are defined in target
           Default values appropriate for target environment

[ ] User/Role access configured
    Check: Import user has CREATE/UPDATE permissions
           Target users have SELECT permissions on objects
           Data consumer roles configured

NETWORK & CAPACITY
[ ] Network connectivity stable
    Test: Ping target system
          No recent network issues
          Upload bandwidth sufficient

[ ] Target has sufficient space
    Check: Disk space > package size * 3 (for extraction)
           Memory available for import process
           No capacity alerts in target

BACKUP & SAFETY
[ ] Target tenant has recent backup
    Verify: Last backup completed successfully
            Backup can be restored if needed
            Backup retention meets compliance

[ ] Rollback plan ready
    Document: How to rollback if import fails
              Restore procedure tested
              Team trained on rollback

Step 2: Upload Package to Target

Upload Process:

TARGET TENANT TRANSPORT COCKPIT
═══════════════════════════════════

1. Navigate: Administration → Transport → Import Packages

2. Click: "Upload Package"

3. Select File:
   File: transport_cust_analytics_v1_0_0.zip
   Size: 3.2 MB

4. Upload:
   Progress: [████████████████░░] 80%
   Status: Validating package...
   Status: UPLOAD SUCCESSFUL
   Uploaded: 2024-01-15 14:30 UTC

5. Review Package Details:
   Package: Customer Analytics Suite
   Version: 1.0.0
   Objects: 7 items
   Size: 12 MB (uncompressed)
   Release Notes: [as documented]

6. System Pre-Import Check:
   Checking dependencies...
   [✓] All table sources available or will be created
   [✓] All view dependencies resolvable
   [✓] No version conflicts detected
   [✓] 0 objects will be OVERWRITTEN (new import)
   [!] WARNING: Data flow connections require verification
       Action Required: Verify Connection_ERP is available
       Status: User must confirm before import proceeds

Step 3: Resolve Conflicts (If Any)

Conflict Scenarios:

Scenario 1: Object Already Exists (Update)

CONFLICT DETECTED
═════════════════════════════════════════

Object: vw_customer_active (View)
Status in Target: EXISTS

Current Definition in Target:
  SELECT * FROM customer_master WHERE status = 'ACTIVE'

New Definition in Package:
  SELECT * FROM customer_master WHERE status = 'ACTIVE'
  AND account_age_days > 30  -- CHANGED

Options:
  [ ] SKIP - Don't overwrite, keep existing
  [ ] OVERWRITE - Replace with new version
  [ ] RENAME - Import as vw_customer_active_v2
  [ ] REVIEW - Show detailed diff before deciding

Decision: OVERWRITE
Reason: New version includes important account age filter
        Vetted in QA before export

Scenario 2: Dependency Missing

MISSING DEPENDENCY
═════════════════════════════════════════

Object: vw_customer_lifetime_value (View)
Required Dependency: vw_transaction_detail (View)
Status: NOT FOUND in target tenant

Options:
  [ ] FAIL - Block import, require dependency first
  [ ] CREATE - Import will create missing dependency
  [ ] SKIP - Skip this object, import others
  [ ] ADD_TO_IMPORT - Request missing object be added

Decision: CREATE
Status: System will create vw_transaction_detail as prerequisite

Scenario 3: Connection Not Available

CONNECTION ERROR
═════════════════════════════════════════

Object: df_customer_enrichment (Data Flow)
Required Resource: Connection_ERP
Status: Connection NOT FOUND in target

Error: Cannot import data flow without connection

Options:
  [ ] SKIP - Import other objects, skip data flow
  [ ] FAIL - Block entire import
  [ ] CREATE_MANUAL - Create connection, retry import later
  [ ] REASSIGN - Use existing similar connection

Decision: CREATE_MANUAL
Action Required:
  1. Create Connection_ERP in target manually
  2. Configure credentials for target ERP system
  3. Test connection (verify connectivity)
  4. Retry import

Step 4: Execute Import

Import Execution:

IMPORT EXECUTION
═════════════════════════════════════════

1. Review Conflict Resolutions:
   [✓] vw_customer_active (OVERWRITE)
   [✓] vw_transaction_detail (CREATE)
   [✓] df_customer_enrichment (SKIP - connection not available)
   [✓] 7 total objects (5 create, 1 update, 1 skip)

2. Click: "Start Import"
   Status: Validating package...
   Status: Creating objects...

3. Import Progress:
   [████████████████████████] 100%
   Objects Created: 5
   Objects Updated: 1
   Objects Skipped: 1
   Errors: 0

4. Import Complete
   Status: SUCCESS (with 1 warning)
   Timestamp: 2024-01-15 14:45 UTC
   Duration: 15 minutes

5. Post-Import Report:
   ┌─────────────────────────────────────────────┐
   │ IMPORT SUMMARY REPORT                       │
   ├─────────────────────────────────────────────┤
   │ Package: Customer Analytics Suite v1.0.0   │
   │ Target Tenant: datasphere-qa.company.com   │
   │ Import Date: 2024-01-15                    │
   │                                             │
   │ OBJECT RESULTS:                             │
   │ ✓ customer_master (CREATE)                 │
   │ ✓ customer_transactions (CREATE)           │
   │ ✓ vw_customer_active (UPDATE)              │
   │ ✓ vw_customer_lifetime_value (CREATE)      │
   │ ✓ vw_transaction_detail (CREATE)           │
   │ ✓ vw_customer_segmentation (CREATE)        │
   │ ⊘ df_customer_enrichment (SKIPPED)         │
   │ ? df_segment_scoring (SKIPPED)             │
   │                                             │
   │ STATUS: 5 SUCCESS, 2 SKIPPED, 0 FAILED     │
   │                                             │
   │ WARNINGS:                                   │
   │ ! Data flows require manual connection      │
   │   configuration before execution            │
   │                                             │
   │ NEXT STEPS:                                 │
   │ 1. Create Connection_ERP in target          │
   │ 2. Configure data flow inputs/outputs       │
   │ 3. Test view queries                        │
   │ 4. Execute data flows                       │
   │ 5. Validate data results                    │
   └─────────────────────────────────────────────┘

SAP Content Network Integration

What is SAP Content Network?

The SAP Content Network provides pre-built, industry-standard data models and solutions available for direct import into Datasphere.

Content Types:

• Industry solutions (Financial Services, Retail, Manufacturing)
• Data models aligned with SAP standards
• Best-practice configurations
• Sample data and documentation

Discovering Content Network Packages

Access:

Datasphere Home → Content Network
  OR
Transport Cockpit → Content Network Tab

Browse Options:

1. By Industry: Filter packages for your industry
2. By Solution: Find packaged solutions (e.g., "Sales Cloud Analytics")
3. By Use Case: Browse by analytical need (e.g., "Customer Analytics")
4. By Popularity: See most-used packages
5. By New: Latest releases

Example Package:

SAP CONTENT NETWORK PACKAGE
════════════════════════════════════════════════════════════

Name: SAP Analytics Cloud - Sales Insights
Publisher: SAP
Version: 4.2.1
Release Date: 2024-01-10

Description:
  Industry-standard Sales Analytics solution with best-practice
  KPI definitions, customer data models, and sales pipeline
  analytics. Includes 12 pre-built models, 20+ analytic views,
  and 5 data flows for common analysis patterns.

Industry: Retail, Manufacturing, High-Tech
Use Cases:
  - Sales pipeline forecasting
  - Territory performance analysis
  - Customer acquisition cost calculation
  - Sales rep productivity benchmarking

Objects Included:
  - 12 data models (tables/entities)
  - 25 analytic views
  - 5 pre-configured data flows
  - 8 KPI definitions
  - Sample data included

Size: 45 MB
Estimated Deploy Time: 20 minutes
Prerequisites:
  - Datasphere Cloud Edition or higher
  - S/4HANA connection (for data replication)
  - Business Role "Space Admin" or higher

Downloads: 5,231
Rating: 4.8/5.0 stars
Latest Review: "Great starting point, saves weeks of modeling"

Cost: Free (included with Datasphere license)
Support: Community forum + SAP support
Documentation: Full documentation + videos + sample queries

Importing Content Network Package

Import Process:

STEP 1: Select Package from Content Network
  Action: Find and review package details
  Check: Prerequisites met, supports use case

STEP 2: Click "Import to My Tenant"
  Automatic:
    - Download package
    - Validate against target environment
    - Display conflict resolution dialog

STEP 3: Configure Target Space
  Selection:
    ☐ Create new space: "Sales_Analytics_CN"
    ☐ Import to existing space: [select from list]
  Recommendation: Create new space (cleaner isolation)

STEP 4: Resolve Conflicts
  Typical Issues:
    - Table names exist (choose RENAME or OVERWRITE)
    - Connections not available (create manually later)
    - Sample data volume (remove if not needed)

STEP 5: Start Import
  Progress: [████████████████░░░░] 50%
  Objects: 3 created, 2 dependencies resolving...
  Status: IMPORT COMPLETE

STEP 6: Post-Import Validation
  Tasks:
    [ ] Navigate to imported space
    [ ] Review imported objects
    [ ] Test views with sample queries
    [ ] Update any connections needed
    [ ] Load sample data if provided
    [ ] Customize KPI definitions for your data

Customizing Imported Content

Common Customizations:

Change	Effort	Impact
Rename objects to match standards	Low	Branding consistency
Update data connections	Medium	Enable actual data flows
Modify KPI definitions	Medium	Business alignment
Add organization filters (region, department)	Medium	Scoping to your org
Extend views with additional columns	Medium	Enhanced analysis
Remove sample data	Low	Reclaim storage

Example Customization:

ORIGINAL PACKAGE OBJECT:
  View: vw_sales_by_region
  Definition: 5 generic regions (North, South, East, West, Central)

CUSTOMIZATION:
  1. Clone view: vw_sales_by_region_company_specific
  2. Modify WHERE clause:
     Original: WHERE region IN ('North', 'South', ...)
     Modified: WHERE region IN (SELECT region FROM region_mapping
               WHERE company_id = CURRENT_COMPANY_ID)
  3. Add column: company_name (join to company master)
  4. Test with your data
  5. Update dependent views to use company-specific version

RESULT:
  - Original package unmodified (easier to upgrade)
  - Company-specific views available for your analytics
  - Multi-tenant capable using company_id filter

Versioning and Change Tracking

Semantic Versioning

Version Format: MAJOR.MINOR.PATCH

Version Number | Increment When | Breaking | Consumer Action
────────────────────────────────────────────────────────────────
1.0.0          | Initial release | N/A | Baseline
1.0.1          | Bug fix | No | Optional update
1.1.0          | Feature addition | No | Update recommended
2.0.0          | Breaking change | Yes | Must update

Examples:

1.0.0 → 1.0.1: Bug fix in vw_customer_lifetime_value calculation
  - Backward compatible
  - No schema changes
  - Safe to deploy anytime

1.0.1 → 1.1.0: Add new column (tenure_months) to vw_customer_active
  - Backward compatible (new column optional)
  - Existing queries unaffected
  - Safe to deploy anytime

1.1.0 → 2.0.0: Remove deprecated column (legacy_id) from customer_master
  - BREAKING: Queries using legacy_id will fail
  - Requires consumer testing before deployment
  - 60+ day notice required

2.0.0 → 2.0.1: Fix data quality issue in customer_master
  - Backward compatible
  - Data corrected (historical)
  - Safe to deploy anytime

Change Tracking

Changelog Format:

CHANGE LOG: Customer Analytics Suite
═══════════════════════════════════════════════════════════════

VERSION 1.0.0 (2024-01-15)
───────────────────────────
Release Type: Initial Release
Status: Production Ready
Author: Data Team
Tested By: QA Team

OBJECTS ADDED:
  - customer_master (Table): Base customer directory
  - customer_transactions (Table): All customer purchases
  - vw_customer_active (View): Active customers (status = 'ACTIVE')
  - vw_customer_lifetime_value (View): 3-year LTV calculation
  - vw_customer_segmentation (View): RFM segmentation model
  - df_customer_enrichment (Data Flow): Daily customer master updates
  - df_segment_scoring (Data Flow): Daily RFM score calculation

OBJECTS MODIFIED:
  [none - initial release]

OBJECTS REMOVED:
  [none - initial release]

DATA QUALITY:
  - customer_master: 50M records, 99.8% completeness
  - customer_transactions: 1B records, 99.95% completeness
  - Validation: GL reconciliation within 0.1%

KNOWN LIMITATIONS:
  - customer_transactions includes retail orders only (excludes web)
  - RFM segmentation uses last 3 years data
  - Daily refresh by 6am UTC (max 1-hour delay tolerance)

BREAKING CHANGES: None

DEPLOYMENT NOTES:
  - QA deployment completed 2024-01-10, testing passed
  - Production deployment scheduled 2024-01-20
  - Rollback procedure tested and verified
  - Support team trained on new objects and queries

───────────────────────────────────────────────────────────────

VERSION 1.1.0 (Expected Q2 2024)
─────────────────────────────────
Planned Changes:
  - Add vw_customer_churn_risk (predictive model)
  - Add df_churn_prediction (data flow)
  - Add region filter to all customer views
  - Expand customer_master to 100M records

Breaking: No
Notice Required: 30 days
Estimated Deploy Date: Q2 2024

───────────────────────────────────────────────────────────────

VERSION 2.0.0 (Planned Q4 2024)
─────────────────────────────────
Planned Breaking Changes:
  - Remove legacy_customer_id column (use customer_id only)
  - Restructure customer_transactions grain
  - Rename vw_customer_lifetime_value → vw_customer_value
  - Consolidate segment tables into single entity

Breaking: YES
Notice Required: 60+ days
Migration: Detailed guide + webinar
New Deploy Date: Q4 2024

Best Practices for Transport

Transport Strategy

Development Process:

1. Dev Space: Rapid iteration, experiments, drafts
   - Make changes frequently
   - Test locally
   - Document incremental changes

2. Feature Branch: Create feature-specific packages
   - One feature = one package
   - Clear naming (feature_name_v1_0)
   - Complete testing in dev

3. Integration: Combine approved features
   - Merge features into main package
   - Run integrated testing
   - Final validation

4. QA Space: Test in target environment
   - Import package to QA
   - Run full test suite
   - Validate with business users
   - Document issues

5. UAT (Optional): Business user acceptance testing
   - Provide limited access to QA
   - Gather feedback
   - Make adjustments

6. Production: Controlled deployment
   - Import to production
   - Monitor for issues
   - Support production consumers

Naming Conventions

Package Names:

Format: [Feature]_[Version]_[Date]

Examples:
  - customer_analytics_v1_0_0_20240115
  - sales_dashboard_v2_1_0_20240201
  - replication_flow_updates_v1_0_0_20240208

Guidelines:
  - Use snake_case (lowercase_with_underscores)
  - Include version number
  - Include date (YYYYMMDD)
  - Keep descriptive but concise
  - Avoid special characters

Object Naming Within Package:

Tables: tbl_[descriptive_name]
  tbl_customer_master
  tbl_sales_transactions

Views: vw_[descriptive_name]
  vw_customer_active
  vw_customer_lifetime_value

Data Flows: df_[descriptive_name]
  df_customer_enrichment
  df_segment_scoring

Connections: conn_[system_name]
  conn_erp_production
  conn_excel_uploads

Testing After Import

Validation Checklist:

POST-IMPORT VALIDATION
═════════════════════════════════════════════════

OBJECT INTEGRITY TESTS
[ ] All objects created/updated successfully
    Check: View object list in target space
           All expected objects present

[ ] No compilation errors
    Check: Open each object definition
           No error indicators
           Syntax valid

[ ] Dependencies resolved
    Check: All dependent objects accessible
           No broken references

DATA INTEGRITY TESTS
[ ] Tables have data (if applicable)
    Check: Query each table, verify row counts
           Sample data appears correct
           No null key fields

[ ] Views return results
    Check: Query each view, verify results
           Execution time reasonable
           Output matches expectations

[ ] Data freshness
    Check: Data timestamp recent
           No stale data indicators
           Last refresh successful

FUNCTIONAL TESTS
[ ] View logic correct
    Test: Run known queries
          Results match expected outcomes
          Calculations validated

[ ] Data flows execute
    Test: Trigger each data flow
          Monitor execution logs
          Completion status successful
          No errors or warnings

[ ] Filters work correctly
    Test: Apply various filters
          Results subset correctly
          No false positives/negatives

PERFORMANCE TESTS
[ ] Query execution time acceptable
    Benchmark: < 5 seconds for typical queries
               < 30 seconds for complex aggregations

[ ] Memory usage reasonable
    Monitor: No spill to disk
             Peak memory < 80% available

[ ] Concurrency tolerated
    Test: Run multiple queries simultaneously
          No slowdown or deadlocks

COMPARISON WITH SOURCE
[ ] Objects match source definition
    Compare: Definition in source (dev) vs. target (qa)
             No unexpected changes
             Versions match

[ ] Data matches source
    Compare: Sample data in source vs. target
             Row counts align
             Totals match

Common Issues and Resolution

Issue 1: Missing Dependencies

Symptom:

IMPORT ERROR: Cannot import vw_customer_lifetime_value
Missing dependency: vw_transaction_detail not found in target

Root Causes:

1. Dependency not included in package
2. Dependency name changed in target
3. Dependency in different space than expected

Resolution:

Option A: Include missing dependency
  1. Go back to source (dev) tenant
  2. Edit package to include vw_transaction_detail
  3. Re-export package
  4. Re-import to target

Option B: Create missing object manually
  1. Manually create vw_transaction_detail in target
  2. Use definition from source
  3. Retry import (should now succeed)

Option C: Update view reference
  1. Import vw_customer_lifetime_value with error
  2. Edit in target to use existing view with different name
  3. Save modified version

Prevention:

• Use dependency analysis tool before exporting
• Test import in QA before production
• Document all dependencies in package release notes

Issue 2: Version Conflicts

Symptom:

IMPORT ERROR: vw_customer_active exists in target
Current version: 1.0.1 in target
Package version: 1.0.0 (older)
Cannot downgrade object

Root Cause: Target already has a newer version of the object than what you're trying to import.

Resolution:

Option A: Skip this object
  Decision: Don't overwrite target version
  Choose: SKIP in conflict resolution
  Verify: Target version has all needed changes

Option B: Get latest package from source
  Action: Go to source, update package
  Include: Latest version of object (1.0.1 or newer)
  Re-export and re-import

Option C: Upgrade source package first
  Action: Make improvements in source
  Release: As version 1.1.0 or 2.0.0
  Then: Export and import new version

Prevention:
  - Always export latest version from source
  - Maintain version consistency across tenants
  - Document version of each environment

Issue 3: Connection Not Available

Symptom:

IMPORT WARNING: Data flow df_customer_enrichment references
connection 'Connection_ERP' which does not exist in target
Data flow will not execute until connection configured

Root Cause: Connection requires different credentials or configuration in target (different ERP instance, different credentials, different environment).

Resolution:

Option A: Import without data flows (temporary)
  1. Skip data flow objects during import
  2. Create connection in target manually
  3. Re-import data flows later

Option B: Create connection in target first
  1. Create new connection in target tenant
  2. Name it identically to source (Connection_ERP)
  3. Configure credentials for target ERP system
  4. Test connection (verify connectivity)
  5. Re-import package (should now succeed)

Option C: Modify data flow to use existing connection
  1. Import data flows anyway (with warning)
  2. Edit data flows in target
  3. Update connection reference to existing connection
  4. Test data flow execution
  5. Save modified version

Prevention:
  - Create connections in target before import
  - Use standard connection names across environments
  - Document required connections in package metadata
  - Include connection setup in import instructions

Using MCP Tools for Transport Management

search_repository

Find transportable objects in your space:

search_repository(space="sales_analytics", object_type="view")

Returns: All views in space, enabling object selection for packaging

get_object_definition

Retrieve complete object metadata for dependency analysis:

get_object_definition(object_id="vw_customer_lifetime_value")

Returns: Full definition including source references, enabling manual dependency verification

list_repository_objects

List all objects in a space to validate package contents:

list_repository_objects(space="sales_analytics", include_metadata=true)

Returns: All objects with metadata, supporting package planning

get_deployed_objects

Verify what's currently deployed in target tenant:

get_deployed_objects(tenant="datasphere-qa", space="sales_analytics")

Returns: Current objects in target, identifying conflicts before import

Transport Workflow Summary

1. Plan Package: Define scope, dependencies, versioning
2. Create Package: Select objects, verify completeness
3. Validate Export: Check file integrity and contents
4. Test Package: Validate in lower environment first
5. Deploy to QA: Import to QA, test thoroughly
6. Deploy to Prod: After QA sign-off, import to production
7. Verify Post-Deployment: Validate all objects functional
8. Document Changes: Update tracking, communicate to users

Best Practices Summary

• Always test in QA before production deployment
• Use semantic versioning for clear change tracking
• Include comprehensive dependency documentation
• Create rollback plan before any deployment
• Backup target tenant before import
• Maintain clear naming conventions
• Document known limitations and breaking changes
• Monitor data quality post-deployment
• Communicate changes to all stakeholders
• Keep detailed changelog for compliance and tracking