SQL SERVER → DATABRICKS
Take SQL Server transformation logic to the Databricks lakehouse.
Datachecks automates SQL Server estate assessment, Delta table mapping, T-SQL to Spark translation, validation, and reconciliation while migration experts handle procedural logic and target modelling decisions.
Supported objects and automation depth vary by source, target, and migration scope.
THE MIGRATION CHALLENGE
SQL Server → Databricks moves logic out of the database.
Much of a SQL Server estate's behaviour lives in stored procedures, transactions, and scheduled jobs. On a lakehouse that logic becomes notebooks, workflows, and Delta operations, which is a relocation as much as a translation.
1
Expert review
Logic living in stored procedures
A large share of SQL Server behaviour sits in procedures with branching, temp tables, and dynamic SQL. On a lakehouse this becomes notebooks and workflow tasks rather than a like-for-like object.
2
Accelerated translation
T-SQL conversion
Temporary tables, MERGE patterns, variables, procedural statements, and SQL Server date handling need Spark-aware translation.
3
Automated with review
Data-type differences
DATETIME variants, MONEY, UNIQUEIDENTIFIER, BIT, and NVARCHAR need explicit Delta mappings, and several have no exact Spark counterpart.
4
Expert review
Agent jobs and scheduling
SQL Server Agent jobs encode the operational schedule. Those become Databricks workflows, and the dependency order has to be reconstructed rather than assumed.
5
Expert review
Transaction-dependent behaviour
Procedures relying on rollback, isolation levels, or multi-statement atomicity behave differently against Delta and need deliberate redesign.
6
Automated
Source-target verification
Critical tables, transformations, aggregates, and business outputs need systematic comparison between SQL Server and Delta before cutover.
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 and its jobs.
Identify schemas, tables, views, stored procedures, functions, and Agent jobs, and trace the ETL and reporting workloads that depend on them.
SQL Server estate inventory with job dependencies
02 · MAP
Source → Target Mapping
Map SQL Server schemas to Delta.
Translate SQL Server types and schemas into Delta definitions, decide medallion layering, and route ambiguous modelling choices to expert review.
Reviewed Delta mappings
03 · TRANSLATE
SQL + Procedural Translation
Convert T-SQL into Spark SQL and workflows.
Translate supported T-SQL and procedural constructs into Spark SQL or PySpark, and re-express scheduled procedural flows as workflow tasks rather than line-by-line conversions.
Spark logic and workflows with exceptions surfaced
04 · VALIDATE
Testing + Reconciliation
Compare SQL Server and Delta systematically.
Generate tests, compare row counts and aggregates, validate transformations, investigate mismatches, and reconcile critical datasets before cutover.
Validated and reconciled Delta outputs
MIGRATION EVIDENCE
Every stage leaves behind something your team can review.
SOURCE → TARGET MAPPING
CustomerId UNIQUEIDENTIFIER → customer_id STRING
TRANSFORMATION: TRIM + UPPER · CONFIDENCE 97% · REVIEWED
TRANSLATION
T-SQL → Spark SQL / PySpark
TRANSLATED · VALIDATED
EXCEPTION
Stored procedure relying on transaction rollback semantics
EXPERT REVIEW REQUIRED
DELIVERY TIME
Turn months of SQL Server migration work into weeks.
Automate inventory, Delta mapping, repetitive T-SQL conversion, test generation, and reconciliation while experts focus on workflow design and transaction-dependent 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 Delta 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.
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