Build Up Your Data Migration Testing with DataLark
DataLark empowers your team to test every stage of your SAP data migration — from profiling and mapping to full simulation and validation — before a single record moves. Prevent issues, verify accuracy, and go live with confidence.
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5
How It Works
- 1. Connect Systems
- 2. Profile Source & Target Datasets
- 3. Define Validation Rules
- 4. Simulate Migration Outcomes
- 5. Analyze Results
1
Connect Systems
Integrate with your source and target systems via DataLark’s built-in connectors. Unify data extraction, transformation, and loading pipelines for seamless testing.
2
Profile Source & Target Datasets
Profile both the original and migrated datasets to surface structural issues, null values, duplicates, type mismatches, and anomalies. It gives your team visibility into baseline data quality pre-migration and post-migration. This early profiling helps catch upstream issues that could pollute your migrated system — even before mapping begins.
3
Define Validation Rules
Use prebuilt or custom rules to define how data integrity should be validated after migration. Rules can include field-to-field comparisons, referential integrity checks, value range enforcement, and logic-based transformations. You can also configure reconciliation thresholds and expected tolerances to test for completeness, consistency, and transformation accuracy.
4
Simulate Migration Outcomes
With real or trial migration data ingested, run simulations of post-migration states. These simulations compare record counts, relationships, formats, and field-level values between source and target systems. This step surfaces mismatches, losses, and transformation failures before the real system is touched — allowing for multiple test cycles without risk.
5
Analyze Results
Every validation cycle produces a detailed report showing pass/fail status by rule, object, and record. You can trace exactly where discrepancies occurred, what rules flagged them, and what corrective actions may be needed. Detailed reports help ensure executive-level visibility into migration readiness.
1
Connect Systems
Integrate with your source and target systems via DataLark’s built-in connectors. Unify data extraction, transformation, and loading pipelines for seamless testing.
2
Profile Source & Target Datasets
Profile both the original and migrated datasets to surface structural issues, null values, duplicates, type mismatches, and anomalies. It gives your team visibility into baseline data quality pre-migration and post-migration. This early profiling helps catch upstream issues that could pollute your migrated system — even before mapping begins.
3
Define Validation Rules
Use prebuilt or custom rules to define how data integrity should be validated after migration. Rules can include field-to-field comparisons, referential integrity checks, value range enforcement, and logic-based transformations. You can also configure reconciliation thresholds and expected tolerances to test for completeness, consistency, and transformation accuracy.
4
Simulate Migration Outcomes
With real or trial migration data ingested, run simulations of post-migration states. These simulations compare record counts, relationships, formats, and field-level values between source and target systems. This step surfaces mismatches, losses, and transformation failures before the real system is touched — allowing for multiple test cycles without risk.
5
Analyze Results
Every validation cycle produces a detailed report showing pass/fail status by rule, object, and record. You can trace exactly where discrepancies occurred, what rules flagged them, and what corrective actions may be needed. Detailed reports help ensure executive-level visibility into migration readiness.
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Key Benefits
Cross-System Compatibility
DataLark is built to test data migrations across SAP and non-SAP systems alike — including ECC, S/4HANA, Snowflake, Salesforce, Oracle, and flat files. It ingests trial migration snapshots from any staging or sandbox environment, regardless of the tools used to execute the migration. This decouples testing from the migration process itself, enabling independent validation without dependency on specific ETL or cutover mechanisms.
Migration Integrity Validation
Using configurable rules and reconciliation logic, DataLark verifies that every migrated record retains its integrity, structure, and business meaning. You can test for missing data, broken relationships, truncation, incorrect transformations, and mismatched values — all before production systems are touched. These deep validations ensure your migration doesn’t just complete technically, but succeeds functionally and operationally.
Simulation Without Risk
With DataLark, teams can run as many simulated post-migration validations as needed without affecting live systems or re-running actual migrations. By analyzing before-and-after snapshots, teams can iterate on mappings, cleansing steps, or migration logic externally and safely. This provides a no-risk feedback loop to identify and fix issues long before go-live.
Intelligent Automated Reconciliation
DataLark automatically compares your source and target data, catching discrepancies and missing entries that manual checks often miss. The system learns from your data patterns to flag unusual changes and generates detailed reports showing exactly what doesn't match. You can set tolerance levels and get instant alerts when critical issues appear, eliminating tedious manual work and giving you confidence nothing got lost during migration.
Full Transparency
Every validation test in DataLark is logged with full traceability — showing which datasets were analyzed, what rules were applied, and what passed or failed. These audit trails help organizations demonstrate compliance with internal policies and external regulations. Built-in role-based access, as well as masking and encryption capabilities ensure sensitive data is protected even during testing.
Accelerated Issue Resolution
DataLark helps teams isolate problems quickly by pinpointing the exact rule, field, or dataset where a validation failed. This reduces time spent chasing errors manually and prevents delays in project timelines. As a result, you shorten test cycles, improve collaboration between migration and QA teams, and enable faster, more confident go-live decisions.
Predictive Analytics
DataLark’s real-time dashboards show data flow speed, bottlenecks, and system resource usage. DataLark analyzes migration patterns and warns you about potential problems hours before they happen — like memory issues or processing limits. This early warning system helps you fix problems before they cause delays, keeping your migration on track.
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Built-in Source and Target Connectors
Robust Data Transformations
Zero-coding
Flexible Deployment: On-prem, Cloud, and Hybrid
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Problem
The client faced challenges in maintaining high-quality master data across their SAP landscape. The complexity of their operations, involving numerous SAP objects such as materials, BOMs, and others, led to:
- Data inconsistencies across various SAP modules, affecting reporting and decision-making.
- Manual validation efforts, requiring extensive time and resources to ensure compliance with business rules.
- Error propagation due to lack of proactive data quality checks, impacting downstream processes like procurement, production planning, and finance.
- Difficulty in maintaining data integrity as multiple systems contributed to data creation and updates.
These challenges resulted in inefficiencies, operational risks, and increased costs associated with poor data quality.
Solution
To address these issues, the client leveraged DataLark as a platform for data quality management. The solution introduced:
- Automated Data Extraction: Seamlessly reading and analyzing master data from SAP, covering materials, BOMs, and other key objects.
- Business Rule Validation: Configurable rule sets to automatically detect data inconsistencies, missing attributes, duplicate records, and misaligned relationships.
- Deviation Reporting: Reports highlighting data quality issues, allowing stakeholders to take corrective action.
- Automated Adjustments: For predefined scenarios, the system corrected data inconsistencies automatically, reducing manual intervention.
- Continuous Monitoring: Ongoing validation to ensure data integrity as new records were created or modified.
This approach enabled the client to establish a proactive data governance framework, ensuring SAP master data met business and regulatory requirements.
Results
With DataLark, the client established a scalable and efficient data quality management process, ensuring that SAP master data remained accurate, reliable, and aligned with business objectives.
65%
Reduction in Manual Reconciliation Efforts: Automated validation streamlined data comparison, significantly reducing the need for manual intervention.
50%
Improvement in Data Consistency: Intelligent rule-based validation minimized discrepancies and enhanced data reliability.
40%
Faster Issue Resolution: Automated deviation reports and alerts enabled quicker identification and correction of data mismatches.
Problem
The client had issues with ensuring data consistency between multiple systems, including SAP and external databases. Some of the challenges were:
- Complex data validation requirements, involving multilevel comparisons across different data sources.
- Manual reconciliation efforts, which were time-consuming and prone to errors.
- Lack of a standardized framework for defining and managing data validation rules, leading to inconsistencies.
- Dependence on IT for developing alternative solutions making it difficult for data management teams to adapt validation logic to evolving needs.
These issues resulted in delays in data processing, inaccurate reporting, and inefficiencies in operations that relied on synchronized master data.
Solution
To overcome these challenges, the client implemented DataLark as a data validation platform. Key features included:
- Automated Data Extraction: Seamlessly reading data from multiple sources, including SAP and external databases.
- Flexible Data Mapping: Providing an intuitive interface for business users to define relationships between datasets without technical expertise.
- Configurable Multilevel Validation Rules: Enabling comparison logic across different hierarchies, including product structures, pricing conditions, and supplier information.
- User-Friendly Rule Management: Allowing master data teams to create, modify, and manage validation rules without IT involvement.
- Deviation Reports and Alerts: Automatically identifying discrepancies and presenting them in easily understandable reports.
With DataLark, the client eliminated the need for custom development, enabling business users to maintain and adapt validation rules independently.
Results
With DataLark, the client established a scalable, efficient, and user-friendly approach to data validation, ensuring data accuracy across critical business processes.
70%
Reduction in Manual Reconciliation Efforts: Automated validation replaced labor-intensive data comparison, freeing up resources for higher-value tasks.
90%
Improvement in Data Accuracy: Faster identification and resolution of discrepancies ensured more reliable and consistent data.
50%
Increase in Business Agility: Master data teams could modify validation rules independently, reducing reliance on IT and accelerating response times.
Explore Opportunities for Efficient Data Migrations
Talk to Our Experts
FAQ
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Can DataLark perform data migrations?Yes. DataLark isn’t just for testing — it’s a comprehensive data migration platform which is capable of streamlining an entire data migration project end-to-end. With streamlined SAP S/4HANA migration templates, visual mapping, automated workflows, and strong validation capabilities, it delivers faster, cleaner, and more transparent data transfers — empowering both business and IT teams.
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What types of migrations can I test with DataLark?Any structured data migration — including ERP upgrades (like ECC to S/4HANA), cloud transitions, legacy-to-modern moves, and multi-source consolidations. The platform is flexible and scalable for projects of any size or complexity.
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What types of ETL testing does DataLark support?
DataLark covers the key ETL checks needed to ensure data integrity throughout transfers and transformations, including:
- Row count — verifies no records are lost in transfer.
- Schema — validates column names, data types, and order.
- Hash — confirms values remain unchanged after transformations.
- Nulls / Duplicates — detects missing values and repeated key data.
- Constraints — enforces rules such as unique IDs and referential integrity.
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What is the DataLark Testing Framework?The DataLark Testing Framework is an automated system for validating data quality at every ETL stage. It performs pre-load checks (row counts, schema, hashes, nulls/duplicates) and post-load validation (completeness, consistency, and business-rule enforcement). With reusable rules, support for SAP and non-SAP systems, built-in monitoring, and detailed audit trails, it helps detect issues early, build trust in loaded data, and streamline enterprise-wide data quality governance.
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Is it suitable for enterprise-wide initiatives?Yes. DataLark is built to scale across thousands to millions of records, across multiple systems. It supports iterative validation for large programs with phased migrations and multiple testing cycles.
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How secure is the migration testing process with DataLark?DataLark ensures enterprise-grade security with end-to-end encryption, role-based access controls, detailed audit logs, and field-level masking. Whether deployed on-premises, public cloud, or private cloud, sensitive data remains protected and compliant.
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Does DataLark provide audit-ready test documentation?Yes. Each migration test cycle generates traceable logs and reports, detailing source‑to‑target mappings, rule execution, passes/fails. These records support governance, audit, and compliance needs.
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How does DataLark support iterative testing and regression across cycles?DataLark supports repeatable test cycles with version-controlled migration scenarios. After each dry-run, you can adjust mappings, cleansing rules, or filters and re-simulate — comparing results across cycles to ensure improvements are validated and regressions are prevented.
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What deployment options are available?DataLark offers flexible deployment options — on-premise (laptops, VDIs, Windows servers), in the cloud (SAP BTP, AWS, Microsoft Azure), or hybrid — to meet your organization’s technical and security requirements.
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Does DataLark need to be integrated into our migration tooling?Not at all. DataLark works independently of your migration toolchain (e.g., SAP Migration Cockpit, ETL platforms, partner services). You simply import source and post-migration data, define validation logic, and run your test cycle.