Snowflake Consulting

We review your current Snowflake environment, query performance, warehouse sizing, and downstream BI layer to identify what is costing you money and breaking reports.

We build a clean semantic layer on top of Snowflake using dbt, Tableau, or Power BI so every team queries the same certified metrics.

We right-size virtual warehouses, implement auto-suspend policies, and eliminate redundant queries to reduce your monthly Snowflake bill.

We migrate your existing reports from on-premise databases or legacy warehouses to Snowflake without losing historical data or breaking downstream dashboards.

We design the account structure, databases, schemas, and warehouse layout before anyone loads a table, so the platform scales without a rebuild six months in.

We move you off Redshift, Teradata, SQL Server, or an on-premise warehouse, validating row counts and reconciling every number so the reports match on day one.

We right-size virtual warehouses, set auto-suspend and resource monitors, and kill the runaway queries that quietly inflate your monthly Snowflake bill.

We build a certified transformation layer with dbt so every team reads the same revenue, churn, and margin definitions instead of writing their own SQL.

We wire Snowflake into Tableau, Power BI, or Looker, tune query pushdown, and set up caching so live dashboards refresh in seconds, not minutes.

We define role-based access control, column masking, and row-level security so the right people see the right data and audits stop being a fire drill.

The warehouse performs well but dashboards are slow because queries are not optimized for columnar storage and clustering keys are not set correctly.

Multiple teams run uncoordinated queries on oversized warehouses with no auto-suspend, resulting in a Snowflake bill that keeps growing.

Finance, Sales, and Operations all query Snowflake directly and produce different revenue numbers because there is no certified semantic layer.

Yes. We use dbt Core and dbt Cloud to build transformation layers on Snowflake. We also integrate with Tableau, Power BI, and Looker as the BI layer on top.

Yes. Warehouse right-sizing and query optimization are part of every Snowflake engagement. Most clients see a 30 to 50 percent reduction in compute costs within the first 60 days.

No. We work with your existing Snowflake environment. We audit what you have, identify the gaps, and fix them without requiring a full rebuild.

How does Snowflake fit with our existing Tableau or Power BI setup?

Snowflake works well as the warehouse layer under Tableau or Power BI. We design the connection architecture, optimize query pushdown, and ensure live query performance meets your dashboard refresh requirements.

A Snowflake consultant designs the warehouse, models the data, and builds the analytics layer that sits on top of it. In practice that means account and warehouse architecture, migration off a legacy database, a dbt transformation layer, a certified semantic model, BI integration, and the cost governance that keeps compute spend under control. The person who scopes the work is the person who does it, so nothing gets lost in a handoff to a junior team.

It depends on your data volume, whether a migration is in scope, and how much modeling and governance you need. We scope every engagement against fixed deliverables and milestones before any work starts, so you see the number before you commit. A tuning-and-cost engagement on an existing warehouse is quick; a full migration with a new semantic layer is a larger build, and that is where most of the value sits.

Snowflake is the stronger fit when your workload is SQL analytics, BI, and governed reporting that a broad team queries, because the warehouse is simpler to run and cheaper to operate for that pattern. Databricks pulls ahead when your center of gravity is data science, machine learning, and large-scale Spark processing on unstructured data. Plenty of stacks run both. We are vendor-neutral and recommend the platform that fits your workload and your team, not the one we would rather sell.

Yes, and it is part of every engagement. Most overspend comes from oversized warehouses with no auto-suspend, uncoordinated queries from multiple teams, and models that rescan the same data repeatedly. We right-size warehouses, set auto-suspend and resource monitors, add clustering where it earns its keep, and rewrite the queries that cost the most. Most clients see a 30 to 50 percent reduction in compute cost within the first 60 days.

Yes. We run migrations in parallel: the legacy warehouse keeps serving reports while we build and validate the Snowflake environment alongside it. We reconcile row counts and key metrics against the source, run both systems side by side until the numbers match, then cut the dashboards over once you have signed off. Users keep working through the whole process and the switch happens on a schedule you control.

The honest answer is that the gap has narrowed and the decision now rests on your team more than the platforms. Snowflake starts easier for SQL-centric analytics teams and keeps compute and storage cleanly separated, which makes cost attribution simple. Databricks starts stronger where the work is data engineering, streaming, and machine learning on a shared lakehouse, and it rewards teams comfortable with Spark and notebooks. Both now do most of what the other does. We have migrated in both directions, and the projects that failed did so because the operating model never changed, not because the platform was wrong.

They bill differently enough that list prices tell you very little. Snowflake charges per second of warehouse runtime, so idle costs nothing and a poorly written query costs a lot. Databricks charges DBUs against compute you configure, which gives more tuning control and more ways to overspend. In the environments we audit the actual driver is rarely the rate. It is workloads left running, models rebuilt in full when an incremental refresh would do, and duplicate pipelines nobody retired. Benchmark both against your own top twenty queries before deciding, because published benchmarks are run by vendors on workloads that flatter them.

Expert Snowflake consulting for semantic layer design, warehouse cost optimization, and BI migration. Senior-led engagements with defined milestones.

Snowflake consulting: semantic layer design, warehouse cost optimization, and migration.

We do not start by loading tables. We sequence the work so the warehouse is modeled, governed, and cost-controlled before it ever backs a live dashboard, and your team can run it after we leave.

Map the current environment, the workloads, and exactly where compute cost and query performance break down.

Design the account, database, and warehouse layout, plus the migration plan if you are moving off a legacy system.

Build the dbt transformation layer and certify the metrics every team will read from.

Wire Snowflake into Tableau or Power BI, tune query pushdown, and validate that dashboards load fast.

Set warehouse sizing, auto-suspend, resource monitors, and RBAC so cost and access stay controlled.

Doing Snowflake right includes controlling its spend. These are the factors that move the effort.

Storage, compute, and the number of pipelines set the build and tuning effort.

Moving off a legacy warehouse adds extraction, modeling, and validation.

Warehouse sizing, auto-suspend, and spend controls are part of doing Snowflake right.

Schema design, access controls, and certified models are more than a lift-and-shift.

You are standing up or scaling Snowflake and want it modeled and governed right.

The numbers disagree by definition: see Semantic Layer Engineering.

Your bigger problem is overall cloud and AI spend: see Cloud & AI Cost Optimization.

The difference between a warehouse that pays off and one that quietly bleeds compute budget is how it is modeled and governed, not the platform. Here is what changes.

Right-sized with auto-suspend and resource monitors on every warehouse.

Each team writes its own SQL and the revenue numbers disagree.

Tuned models, clustering, and pushdown; dashboards load in seconds.

Full-table scans and repeated queries that time out or crawl.

Spend tracked per workload with monitors that flag overruns early.

Role-based access, masking, and row-level security from day one.

Broad access, no masking, and audits that turn into fire drills.

Most organizations land on Snowflake and immediately rebuild the same reporting problems they had before. The warehouse is fast. The semantic layer is missing. The dashboards still disagree. We fix the analytics layer on top of Snowflake so the investment actually pays off.

Snowflake consulting fixes the analytics layer on top of Snowflake so the investment pays off. Most organizations land on Snowflake and rebuild the same reporting problems they had before: the warehouse is fast, but the semantic layer is missing and the dashboards still disagree. Thinklytics builds the certified metric layer so the reports finally hold.

Snowflake consulting is more than turning on a warehouse. It is the full path from account design to a governed report a leader acts on, plus the cost controls that keep the platform affordable as it scales. Here is the work, and what each part delivers.

We get your data tidy and organized. That way, every Snowflake report you pull actually means something. No more scratching your head over whether the numbers line up.

We build a reliable transformation layer on Snowflake with dbt. Basically, we take your raw data and clean it up so your analysis runs smoother and quicker.

We connect your Snowflake warehouse to Tableau or Power BI using a simple, clean semantic model. No headaches, just data that works smoothly with your tools.

Start with a Snowflake Analytics Audit. We review your environment, identify what is broken, and give you a prioritized fix list before any work begins.