SQL Server to Snowflake Migration without breaking what the business runs on

No – and organisations that start there usually discover the gap when dashboards return wrong numbers or ETL jobs fail silently. The tables are the straightforward part. The real scope is the business logic embedded in stored procedures, views, functions, SSIS packages, SQL Agent jobs, and triggers – none of which has a direct Snowflake equivalent. SQL Server schemas built for OLTP workloads also typically need redesign for Snowflake’s columnar, analytics-optimised architecture. A direct copy produces correctly-looking data and wrong reports.

Yes, and it is worth planning for specifically. Fiserv-connected environments introduce additional complexity around data access patterns, core banking data structures, and regulatory data requirements that a standard SQL Server migration does not cover. As Fiserv winds down support for SQL Server-based analytics environments and moves clients toward Snowflake as the recommended destination, the migration timeline becomes less flexible. We map Fiserv integration paths, data flows, latency requirements, and compliance constraints separately during the assessment phase – before a single pipeline is built.

Most T-SQL business logic will need to be converted or redesigned. Snowflake’s SQL dialect is ANSI-based and does not support T-SQL-specific syntax, many proprietary functions, or procedural patterns in the same form. Automated conversion tooling like SnowConvert AI can handle a first pass on straightforward logic – but expert manual review is always required. Complex procedures, dynamic SQL, and cursor-based logic need significant rework. The better question is often whether the stored procedure logic should move to Snowflake at all, or whether it belongs in a dbt transformation layer, an orchestration tool, or a downstream application. We make that assessment during scoping.

Yes – this is our standard approach. SQL Server stays live and authoritative throughout the migration program. CDC-based replication tools keep SQL Server and Snowflake in continuous sync during the parallel run, so no data is lost and there is no need for a maintenance window at cutover. Cutover happens when automated reconciliation and stakeholder sign-off confirm parity – not when the project timeline says so.

It depends on your environment. A focused migration of a single SQL Server database with well-understood schemas, limited stored procedure logic, and no complex downstream dependencies can be delivered in 3 to 5 months – including assessment, pipeline build, validation, and cutover. A full enterprise migration covering multiple SQL Server databases, complex T-SQL logic, real-time CDC requirements, financial services compliance constraints, and a governed Snowflake analytics layer typically runs 9 to 18 months as a phased program. The assessment phase produces a realistic timeline for your specific environment – we do not give generic estimates before we have seen the actual SQL Server landscape.

Reports connected directly to SQL Server will need to be transitioned to Snowflake-backed connections – that is part of the program scope, not a follow-on task. We assess existing BI dependencies – Power BI, SSRS, Tableau, Qlik, or other tools – during scoping and plan their transition alongside the data migration. For business-critical or regulated reports, we validate outputs from Snowflake against SQL Server before switching connections. Reports that cannot be moved immediately remain SQL Server-connected until their transition is explicitly scheduled and validated.

Tool selection depends on your data volumes, latency requirements, existing ecosystem, and budget – there is no single right answer. For real-time CDC from SQL Server transaction logs, Striim, Bryteflow, Repstance, and Estuary are proven options. For Azure-centric environments, Azure Data Factory is a natural fit for orchestration and batch ingestion. For managed ELT, Fivetran and Hevo offer reliable no-code SQL Server connectors. For code conversion, SnowConvert AI handles automated T-SQL conversion as a first pass – manual expert review always follows. We cover tooling recommendation in the assessment phase, after we have seen your environment.

We plan cutover windows explicitly around your financial calendar – avoiding major transitions during month-end, quarter-end, or year-end close periods, and during key regulatory reporting cycles. SQL Server remains the authoritative source for financial data until Snowflake has passed reconciliation and sign-off from finance stakeholders. We do not decommission SQL Server-based reporting until balance checks confirm parity – the business decides when it is ready, not the project schedule.