SQL SERVER → SNOWFLAKE
Move SQL Server workloads to Snowflake without manually rewriting the estate.
Datachecks automates estate assessment, SQL Server-to-Snowflake mapping, T-SQL translation, data validation, and reconciliation while migration experts handle complex procedural logic and target-design decisions.
Supported objects and automation depth vary by source, target, and migration scope.
THE MIGRATION CHALLENGE
SQL Server → Snowflake goes well beyond copying relational data.
T-SQL, stored procedures, functions, views, data types, dependencies, transformation logic, and surrounding workload patterns all need to be understood and adapted for Snowflake.
1
Automated with review
Data-type differences
SQL Server types such as DATETIME variants, MONEY, UNIQUEIDENTIFIER, BIT, NVARCHAR, DECIMAL, and other source definitions need explicit Snowflake mappings and validation.
2
Accelerated translation
T-SQL conversion
SQL Server-specific syntax, functions, variables, temporary tables, MERGE patterns, date logic, procedural statements, and other T-SQL constructs may need target-aware translation.
3
Expert review
Stored procedures and functions
Procedures and functions can contain complex branching, temporary objects, dynamic SQL, transaction logic, and application-specific rules that require more than syntax conversion.
4
Automated discovery
Dependencies
Views, stored procedures, functions, jobs, ETL processes, reporting workloads, and downstream applications may depend on migrated objects in ways that are not obvious from schema definitions alone.
5
Human-owned
Transformation logic
Legacy data warehouses and operational reporting estates frequently contain embedded cleansing, enrichment, and aggregation logic that needs to be preserved or redesigned.
6
Automated
Source-target verification
Data movement alone does not establish correctness. Critical tables, transformations, aggregates, and business outcomes need systematic comparison between SQL Server and Snowflake.
HOW DATACHECKS HELPS
Understand. Map. Translate. Validate.
The migration is executed through controlled agent workflows, with migration experts reviewing ambiguity, unsupported patterns, business rules, and critical exceptions.
01 · UNDERSTAND
Assessment + Discovery
Inventory the SQL Server estate before conversion.
Identify schemas, tables, views, stored procedures, functions, relationships, dependencies, and data patterns while classifying migration complexity.
Migration-ready SQL Server estate inventory
02 · MAP
Source → Target Mapping
Generate SQL Server-to-Snowflake mappings.
Create schema, table, column, data-type, and transformation mappings while routing ambiguous design choices for expert review.
Reviewed target mappings
03 · TRANSLATE
SQL + Procedural Translation
Accelerate T-SQL conversion.
Translate supported SQL and procedural constructs into Snowflake-compatible target logic, validate generated output, and surface complex or unsupported patterns.
Snowflake-ready logic with exceptions
04 · VALIDATE
Testing + Reconciliation
Compare SQL Server and Snowflake systematically.
Generate tests, compare row counts and aggregates, validate transformations, investigate mismatches, and reconcile critical datasets before cutover.
Validated and reconciled Snowflake outputs
MIGRATION EVIDENCE
Every stage leaves behind something your team can review.
SOURCE → TARGET MAPPING
CustomerId UNIQUEIDENTIFIER → CUSTOMER_ID VARCHAR
TRANSFORMATION: TRIM + UPPER · CONFIDENCE 97% · REVIEWED
TRANSLATION
T-SQL → Snowflake SQL
TRANSLATED · VALIDATED
EXCEPTION
Stored procedure with complex transaction-dependent behavior
EXPERT REVIEW REQUIRED
DELIVERY TIME
Turn months of SQL Server migration work into weeks.
Automate inventory, mappings, repetitive T-SQL conversion, test generation, and reconciliation while experts focus on target architecture and complex procedural logic.
TRADITIONAL MIGRATION
MONTHS
Understand
Map
Translate
Test & validate
Reconcile
Cutover
WITH DATACHECKS
WEEKS
Understand
Map
Translate + test
Validate + reconcile
Cutover
Why the timeline shrinks: automated estate analysis · generated mappings · accelerated SQL translation · generated tests · automated reconciliation. Bar lengths are illustrative, not project commitments.
MIGRATION CONFIDENCE
Prove Snowflake is right before production cutover.
Validate translated logic and migrated datasets against SQL Server, reconcile critical results, and resolve exceptions while the legacy environment is still available for comparison.
TRANSLATE
TEST ✓
TARGET LOAD
VALIDATE ✓
RECONCILE ✓
EXCEPTIONS REVIEWED ✓
READY FOR CUTOVER
HUMAN IN THE LOOP
Automate the repeatable work. Keep experts on the decisions.
AGENT EXECUTION
Estate inventory · metadata analysis · profiling · standard mappings · common SQL translation · test generation · row-count checks · aggregate comparisons
EXPERT REVIEW
Ambiguous mappings · unsupported procedural patterns · complex logic · unusual transformation patterns · reconciliation discrepancies
HUMAN-OWNED
Business-rule interpretation · critical exception resolution · acceptance criteria · migration scope decisions · cutover approval
ENTERPRISE DEPLOYMENT
Run the migration engine where the data lives.
Deploy Datachecks within enterprise-controlled infrastructure, connect approved AI models, and keep migration data, metadata, execution, and evidence inside your security boundary.
Private Deployment
BYOM
SAML SSO
SOC 2
ISO 27001
PLANNING THIS MIGRATION?
Start with the estate you already have.
Share your source environment, target architecture, approximate object volumes, and migration goals. We’ll help identify complexity, scope, and where automation can remove manual delivery work.
Useful to bring: source schemas · object counts · procedural code volume · target architecture · timelines