Streamline SAP Data Migration with DataLark

DataLark combines a visual drag-and-drop interface with AI-powered automation to simplify even the most complex data preparation tasks. The platform not only lets users define mappings and transformations without writing code, but also uses AI to suggest field pairings, detect inconsistencies, and propose data quality rules. This dramatically reduces configuration time and errors.

Beyond mapping, the transformation engine supports cleansing, enrichment, normalization (e.g., unit standardization), conditional logic, string operations, filtering, joins, and aggregations — all in a single low-code environment. When extra flexibility is needed, users can add custom Python logic for advanced validations or domain-specific rules.

• Core Master Data (e.g., Materials, Business Partners),
• Transactional Data (sales orders, invoices, production records),
• Document Management (Document Info Records),
• Custom Structures for industry-specific needs.

Each package follows SAP best practices and is designed for rapid deployment so that many teams can start configuring in days instead of weeks or months.

Examples of package types:

• ‘Finance Foundation’ for FI objects and chart of accounts,
• ‘Logistics Essentials’ for MM and SD data flows,
• ‘PLM Sync’ for engineering and BOM integration.

Reusable configurations ensure consistency across projects and simplify rollout across multiple business units or regional implementations, delivering faster ROI and reducing technical complexity.

Absolutely. DataLark’s zero-code / low-code interface empowers business analysts, consultants, and domain experts to steward migration workflows without IT dependencies. Users can build mappings, validation checks, schedules, and automation flows entirely visually. Technical oversight is still available for custom scripting, security configuration, or advanced performance tuning when needed.

This user empowerment accelerates project agility, reduces reliance on scarce development resources, and improves stakeholder collaboration.

DataLark includes an embedded validation engine that supports duplication detection, referential integrity checks, format consistency rules, custom business logic, and data profiling. AI-driven anomaly detection can surface inconsistencies or missing values, enabling users to correct issues upstream before actual loading. Simulated test runs further mitigate risk.

Profiles are presented visually so users can spot outliers or statistical discrepancies early. All validation issues are flagged and managed via the UI with exception handling and reporting.

Yes, DataLark facilitates simulation capability with dry-run test cycles, enabling iterative validation before production cutover. These sandbox executions mimic migration logic, transformation rules, and data loading, but without finalizing changes in target systems. This allows migration teams to refine workflows, correct issues, and gain confidence in results before committing.

Such practice expedites go-live readiness and lowers post-migration issues significantly. Moreover, reusable test scenarios can be automated for phased or multi-wave migration projects.

DataLark supports multiple SAP-compatible data load methods: SAP BAPIs, RFC/OData APIs, XML or CSV templates for SAP Migration Cockpit, and direct staging uploads. It adapts to both ECC and S/4HANA environments, on-premise or in-cloud.

Migration flows can be customized — e.g., batch loading via Migration Cockpit, or interactive loads via API triggers — depending on volume or complexity. You can also configure post-load validation to reconcile source and target records and trigger downstream updates automatically.

DataLark supports flexible deployment: on-premise, private or public cloud, or hybrid environments. You’re free to choose installation approaches that match infrastructure policies, regulatory requirements, or existing data architecture.

Scalability adjusts to growing volume and complexity — whether migrating small datasets or large enterprise operations — without compromising performance.

The platform is built for multi-system consolidation — it connects to diverse sources such as ERP, CRM, PLM, MES, cloud services, or custom databases — harmonizing data via unified migration workflows. This is ideal for M&A integrations, divestitures, or unified reporting transitions.

Cross-system data flow templates and normalization rules help merge fragmented datasets, standardize content, and load into SAP or other core systems, keeping data integrity intact.

Customers typically achieve ~60% faster migration times, ~60% productivity gains, and up to 70% reduction in operational costs, thanks to AI-assisted automation, reusable templates, and reduced manual effort.

With broad automation and intuitive workflows, teams avoid scripting, manual rework, and repeated effort — leading to faster project timelines, lower risk, and measurable business impact.