SQL SERVER → BIGQUERY
Move SQL Server reporting estates onto BigQuery.
Datachecks automates SQL Server estate assessment, BigQuery table design, T-SQL translation into GoogleSQL, validation and reconciliation while experts handle procedural logic and target modelling.
Supported objects and automation depth vary by source, target, and migration scope.
THE MIGRATION CHALLENGE
SQL Server → BigQuery leaves the transactional model behind.
SQL Server enforces keys, constraints and transactions. BigQuery enforces none of them and charges by data scanned, so integrity and performance assumptions move out of the database and into the pipeline and table design.
1
Automated with review
Data-type differences
DATETIME variants, MONEY, UNIQUEIDENTIFIER, BIT and NVARCHAR need explicit GoogleSQL mappings, and several have no exact BigQuery counterpart.
2
Accelerated translation
T-SQL conversion
Temp tables, MERGE patterns, variables, procedural statements and SQL Server date handling need GoogleSQL-aware translation.
3
Expert review
Stored procedures and functions
Procedures carrying branching, dynamic SQL and transaction logic do not map cleanly onto a query-priced analytical engine and often become pipeline steps.
4
Expert review
Integrity moves out of the database
SQL Server enforces keys and constraints; BigQuery does not. Any logic that depended on rejection at write time has to be relocated into ingestion or validation.
5
Automated discovery
Agent jobs and downstream consumers
Agent jobs, ETL processes and reporting tools connect directly to SQL Server. Those dependencies need cataloguing before anything is repointed.
6
Automated
Source-target verification
Critical tables, transformations, aggregates and business outputs need systematic comparison between SQL Server and BigQuery 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 consumers.
Identify schemas, tables, views, stored procedures, functions and Agent jobs, and trace the reporting and ETL workloads that depend on them.
SQL Server estate inventory with dependencies
02 · MAP
Source → Target Mapping
Design BigQuery tables from query patterns.
Map SQL Server types into GoogleSQL types, choose partitioning and clustering from real access patterns, and decide where nested fields replace joins.
Reviewed BigQuery mappings
03 · TRANSLATE
SQL + Procedural Translation
Convert T-SQL into GoogleSQL.
Translate supported T-SQL, temp tables, MERGE patterns and procedural constructs into GoogleSQL and scripting, surfacing transaction-dependent logic for review.
GoogleSQL with exceptions surfaced
04 · VALIDATE
Testing + Reconciliation
Compare SQL Server and BigQuery systematically.
Generate tests, compare row counts and aggregates, validate transformations, investigate mismatches, and reconcile critical datasets before cutover.
Validated and reconciled BigQuery 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 → GoogleSQL
TRANSLATED · VALIDATED
EXCEPTION
Procedure relying on enforced key constraints
EXPERT REVIEW REQUIRED
DELIVERY TIME
Turn months of SQL Server migration work into weeks.
Automate inventory, table design, repetitive T-SQL conversion, test generation and reconciliation while experts focus on integrity handling and cost-aware modelling.
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 BigQuery 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
FAQ
Frequently asked questions
How are T-SQL stored procedures converted for BigQuery?
What replaces SSIS in a BigQuery estate?
Do SSRS and Power BI reports keep working?
How do SQL Server temp tables and MERGE map to BigQuery?
What are the type and collation risks?
How are SQL Agent jobs reimplemented?
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