Every large enterprise migration—whether from SAP, SAS, SQL Server, Teradata, Exadata, or Redshift to the Snowflake AI Data Cloud—faces one critical roadblock: source-to-target mapping.
Traditionally, this is a painstaking, manual process that involves:
- Understanding complex legacy schemas
- Mapping thousands of columns across multiple systems
- Running endless validation cycles to ensure correctness
These projects often take months, requiring expensive engineering resources to manually profile, map, and validate data. But what if they didn’t have to?
Enter Hakkoda’s proprietary, AI-driven mapping engine to automate the source-to-target mapping process, cutting migration times by up to 75% while improving accuracy compared to traditional methods.
Automated Source-to-Target Mapping: How It Works
Hakkoda’s mapping engine uses machine learning and intelligent automation to do what would otherwise take entire teams months to complete:
- Automated Schema Discovery
- Connects to any legacy system (SAP ECC, SAS, SQL Server, Teradata, Exadata, Redshift, etc.)
- Extracts metadata from system catalogs and metadata tables (e.g., SAP’s DD03M, SQL system views, etc.)
- Understands naming conventions, constraints, and table relationships automatically
- Machine Learning-Based Data Profiling & Sampling
- Randomly samples source and target data to calculate confidence scores for column mappings
- Identifies patterns, transformations, and dependencies between legacy and Snowflake structures
- Detects potential issues—format mismatches, concatenations, calculated fields
- AI-Powered Mapping Recommendations
- Presents automated mapping suggestions with confidence scores
- If multiple mappings are possible (e.g., 3 source columns match 1 target column), AI ranks them based on statistical similarity, business rules, and prior mappings
- Users can approve, override, or adjust mappings using an interactive interface
- Auto-Generated SQL & Validation
- Once mappings are approved, the app generates transformation scripts (merge statements, views, or transformation logic for Snowflake)
- It also auto-generates technical unit tests to validate data integrity before deployment
The Business Impact of Automated Source-to-Target Mapping
- Reduces Migration Timelines from Months to Weeks: Traditional source-to-target mapping can take 4-6 months for large enterprises. With Hakkoda’s AI-driven approach, we cut that down to weeks.
- Minimal Engineering Effort Required: Our automation ensures that engineers don’t waste time on manual mapping. Instead, they oversee the AI’s work and validate results—an AI + human-in-the-loop approach that is far more efficient.
- Higher Accuracy, Lower Risk: By using data-driven matching instead of human guesswork, our engine reduces errors, ensures consistency, and provides full auditability of mappings.
- Designed for Any Legacy System: This isn’t just for SAP migrations—our framework works for SAS, SQL Server, Teradata, Exadata, Redshift, and any legacy system moving into Snowflake.
Why This Matters for Data Migrations
Organizations undergoing large-scale data migrations need a faster, more efficient way to handle source-to-target mapping. Traditional methods slow down projects and increase costs, while our AI-driven approach eliminates those inefficiencies, accelerating time-to-value and ensuring data integrity.
By automating the most complex aspects of migration, businesses can reduce engineering effort, cut costs, and ensure a seamless transition to modern data platforms without disruption.
Let’s Talk—We’re Changing the Way Data Migrations Happen
If you’re planning (or struggling with) a legacy-to-Snowflake migration, Hakkoda’s AI-driven source-to-target mapping solution can cut months off your project timeline and deliver a fully automated, AI-powered solution that ensures accuracy from day one.
Contact to one of our migration experts today to learn how we can accelerate your data migration and eliminate the challenges of source-to-target mapping.
