Snowflake consulting and migration from an independent engineering team.

No. MV.tech is not a Snowflake reseller or a member of the Snowflake partner programme; it works as an independent engineering team that designs, migrates and tunes Snowflake warehouses on your own Snowflake account. You buy Snowflake from Snowflake and engineering from us, so there is no resale margin in the advice. The founder holds the Google Cloud Professional Data Engineer certification; MV.tech makes no Snowflake certification or partner claims.

For Snowflake work, we quote after a scoping call. The estimate depends on the number of source systems, the volume of historical data, how much logic is hidden in views, stored procedures and spreadsheets, the Power BI models to be repointed and the support expected afterwards. A first domain is scoped as a bounded piece of work so the figure for the rest of the estate rests on your data rather than on a template.

Dependency analysis first, reconciliation throughout. Before anything moves we trace which reports, extracts and semantic models read each object and flag the platform-specific SQL, cross-database joins and hardcoded rules that will not survive the move. Layers are then built and compared with the legacy numbers stage by stage, and dashboards are repointed visual by visual, usually while both platforms run in parallel.

Import for most governed reporting, DirectQuery where data must be current to the minute or is too large to hold in memory, and a composite model when one report needs both. The more important decision is upstream: Power BI should read fact and dimension tables and curated views from the reporting layer, not raw tables, so the model stays small and the business logic lives in one place. We profile the existing model before recommending a change.

Usually because warehouses stay awake for queries that scan more than they should. The common causes are missing clustering on large tables, full refreshes where incremental loads would do, oversized warehouses with long auto-suspend settings and reports that repeat the same aggregate many times an hour. We measure from the query history, change the few things that account for most of the spend and show the before-and-after on your own credit consumption.

Yes. MV.tech works from Ahmedabad, India, and agrees a collaboration window in your timezone before work begins: US Eastern mornings, UK and European business hours, Gulf hours or Asia-Pacific afternoons. Snowflake work suits remote delivery because access is role-based and every change can be reviewed as SQL in a pull request. Reconciliation results, decisions and handover notes are written down where your team can read them the next morning.

Yes, and it is often the part that earns trust in the warehouse. We compare the same metric across the source systems, Snowflake and the CRM Analytics or BI layer at an agreed grain, then investigate every difference until it is fixed or documented as a deliberate change. That investigation sometimes shows that the legacy figure was the wrong one.