AI-Assisted SAP Replication to
Snowflake or Databricks
with dbReplika
Modern Data Platforms Powered by AI-Assisted SAP Data Replication
We specialize in building robust data platforms on SAP, cloud, and open-source ecosystems. dbReplika is a innovative replication tool which enables SAP Data Replication to Snowflake or Databricks, empowering businesses to harness actionable insights with ease with No-Code and 1-Click setup.
Leverage our expertise to transform your data landscape and drive smarter decision-making. Let us help you bridge the gap between AI, SAP and the Cloud Data Lakehouse solutions.
💰 Development costs reduced to zero 💰
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SAP Replication options for
Snowflake or Databricks
Managed by External Scheduler
Managed by SAP BW Scheduler
If a customer would like to use SAP BW standard scheduler, we offer the possibility to transfer the data directly to targets like Snowflake and Databricks. The data is written directly to storage locations and Snowflake or Databricks are configured to ingest the new data immediately. All API artefacts needed for the communication between SAP, Databricks or Snowflake are part of the standard delivery package.
Performance Metrics
Data Security
AI-Assisted Data Replication
Our groundbreaking AI integration feature for dbReplika, introduces a revolutionary conversational interface that enables users to create complex SAP data replication objects through simple voice commands and natural language interactions.
This innovative advancement represents a fundamental shift in how organizations approach SAP data integration, moving beyond traditional GUI-based configurations to intuitive, AI-powered conversations that democratize access to sophisticated replication capabilities.

SAP BW 7.5, SAP S/4HANA and BW/4HANA compatible
dbReplika enables you to replicate business rich SAP data sources and providers to your cloud platform of choice like Snowflake or Databricks. We Support on-premise SAP BW 7.5, SAP S/4HANA and BW/4HANA source systems without any cloud subscription or middleware. Our solution is SAP Business Data Cloud ready because Databricks is supported natively.
Supported Systems

Source Systems
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SAP BWonHANA® >= 7.5
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SAP S/4HANA® >=1709
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SAP BW/4HANA®
Source Type
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Datasources (BW, ODP, SAPI, CDS Views)
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Composite Provider & ADSO
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Custom Tables via CDS Views
Target Systems
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Snowflake
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Databricks
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Other vendors on request
Replication Features
Configuration
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Low-code & No-code reduced development costs
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Low entry barrier for new team members
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Ease of knowledge Transfer
Delta
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All ODP Delta Datasources supported
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Custom delta extractors supported
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Recovery of prior delta states of requets
Filters
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Standard source column filters are supported
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Routines as filters are supported
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Custom filter logic supported
SAP Compliance
We follow the guidelines in the SAP notes related to log based replication, ODP API restrictions and database triggers.
Our codebase does not use any technology which is mentioned in the SAP notes below.
We value simplicity and conformity.
SAP Note 2814740
Database triggers in ABAP Dictionary
When a table is changed using triggers, the triggers are treated as follows:
1. There are unmanageable (unknown) triggers for the table: The system rejects all change operations of the table.
2. There are only manageable triggers: If the table operation is trigger-compatible (see below), the triggers are retained. In the case of other database changes to the table, the manageable triggers are deleted. Triggers must then be created again using the appropriate SAP tools.
For the replication tools, this means that the replication for the table must start again from the beginning.
SAP Note 3255746
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The usage of RFC modules of the Operational Data Provisioning (ODP) Data Replication API is NOT permitted by SAP by customer, or third-party applications to access SAP ABAP sources (On-Premise or Cloud Private Edition)
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Such modules are only intended for SAP-internal applications and may be modified at any time by SAP without notice.
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Any and all problems experienced or caused by customer or third-party applications (like MS/Azure) using RFC modules of the Operational Data Provisioning (ODP) Data Replication API are at the risk of the customer and SAP is not responsible for resolving such problems.
SAP Note 2971304
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SAP has not certified or offered any supported interfaces in the past for redo log-based information extraction out of SAP HANA’s persistence layer (log volumes as well as log backups in files or external backup tools).
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To be clear, there is no published or ever existed API for the redo-log information extraction by SAP or any similar planned functionality in the current roadmap for SAP HANA and SAP HANA Cloud!
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Any solution on the market is consequently based on 3rd-party reverse engineering of redo-log (transactional log) functions of SAP HANA outside of SAP reach.
dbReplika vs. other Vendors
Feature Matrix | dbReplika | Other Vendors |
---|---|---|
1-Click replication setup | ||
Low-Code / No-Code | ||
Cost efficient | ||
Usage based pricing | ||
Hidden follow up costs | ||
Replication performance | ||
Transfer method | ||
S3 support | ||
CDS View support | ||
Custom datasource support | ||
SAPI datasource support | ||
ODP datasources support | ||
HANA DB Log usage | ||
Database trigger | ||
Middleware needed | ||
SSH connection needed | ||
SAP BTP Cloud | ||
SAP® Datasphere | ||
SAP Cloud connector | ||
SAP Java connector | ||
SAP JDBC / ODBC Adapter | ||
External scheduler | ||
BW scheduler support | ||
Databricks ETL content | ||
Databricks Notebooks | ||
Databricks Job support | ||
Snowflake ETL content | ||
Snowflake Notebooks | ||
Snowflake Stage | ||
Snowflake Snowpipe |
Frequently asked questions
- No management view in S/4HANA, deltas cannot be repeated
- OpenHub has no end-to-end solution
- Code has many bugs and long-term strategy and support is unclear
- OpenHub can’t be integrated to external orchestration tools
- Large data transfers cannot be split and parallelized
- Poor performance, export of multiple millions of records can take hours
- Inflexible setup of CDS Views, problems with long fieldnames
- OpenHub doesn’t provide options to write to Snowflake and Databricks
- OpenHub has no out-of-box S3 Integration
- OpenHub has no Databricks Notebook support
- OpenHub has no Databricks Job support
- OpenHub has no Snowflake Notebook support
- OpenHub has no Snowflake Stage support
- OpenHub has no Snowflake Snowpipe support
Challenges in SAP Data Replication to Snowflake
Performance Bottlenecks
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SAP table locking during extraction
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Network bandwidth limitations during large data transfers
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Resource contention in production environments
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Slow processing of wide tables with numerous columns
Data Consistency Issues
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Handling complex SAP data types and conversions
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Maintaining referential integrity across tables
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Managing delta changes in clustered tables
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Synchronizing data across different time zones
Operational Complexities
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Complex SAP authorization requirements
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Limited extraction windows during business hours
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High memory consumption during full loads
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Monitoring and alerting across multiple systems
Integration Hurdles
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SAP module-specific extraction logic
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Custom ABAP code compatibility
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Pool and cluster table replication
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Handling of SAP buffer synchronization
Cost Management
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Snowflake compute costs during large loads
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Storage costs for historical data versions
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Network egress charges
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Development and testing environment expenses
Best Practices
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Implement incremental loading where possible
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Use parallel processing for large tables
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Schedule resource-intensive loads during off-peak hours
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Optimize table structures and indexes
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Regular monitoring and performance tuning
Challenges in SAP Data Replication to Databricks
Performance Issues
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SAP extractor performance limitations
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High latency during peak business hours
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Memory pressure during large table processing
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Slow processing of hierarchical data structures
Architecture Complexities
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Delta Lake table optimization challenges
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Cluster configuration for varying workloads
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Managing schema evolution
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Handling of concurrent write operations
Data Quality Concerns
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ABAP data type conversion challenges
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Maintaining data lineage
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Complex transformation logic validation
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Handling of SAP null values and special characters
Operational Challenges
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Job orchestration across environments
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Resource allocation for multiple workloads
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Managing compute costs for large datasets
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Monitoring distributed processing tasks
Integration Hurdles
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SAP connector stability issues
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Authentication and authorization complexity
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Network security configuration
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Managing CDC (Change Data Capture) failures
Best Practices
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Implement auto-scaling policies
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Use optimized file formats (Delta/Parquet)
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Set up proper partitioning strategies
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Deploy robust error handling mechanisms
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Establish clear SLAs for data freshness