We implement Snowflake as your analytics warehouse and, where relevant, the foundation for a composable CDP — clean, well-modeled, ready to power BI and activation alike.
Compute sized for peak load running continuously, driving unnecessary credit consumption that right-sizing alone often fixes.
Data modeled purely for internal reporting, without the clean customer-profile structure that CDP activation later requires.
Broad access grants from early setup never tightened as the team grows, creating real governance exposure.
Warehouses that work fine for dashboards but aren't structured in a way reverse ETL tools can cleanly sync from.
Repeated identical queries re-executed at full cost instead of leveraging Snowflake's result cache, quietly inflating credit spend.
BI, ETL, and ad hoc analyst queries all competing for the same compute, causing unpredictable slowdowns instead of isolated, right-sized warehouses per workload type.
We design account structure, schema, and ingestion pipelines around your actual analytics needs — not a generic template.
We migrate existing warehouses to Snowflake with full validation that migrated data and queries match the source system.
For teams building CDP capability, we structure Snowflake for clean reverse ETL activation via Hightouch or Segment.
We audit warehouse sizing and query patterns to reduce unnecessary Snowflake spend without losing performance.
We monitor performance and cost, evolving the architecture as your data volume and use cases grow.
The kind of clean, purpose-built structure that supports both BI reporting and downstream reverse ETL activation.
customer_id VARCHAR | email VARCHAR | lifecycle_stage VARCHAR | ltv_predicted NUMBER | last_active_at TIMESTAMP
event_id VARCHAR | customer_id VARCHAR | event_name VARCHAR | occurred_at TIMESTAMP | properties VARIANT
A slim, well-indexed profile table for fast lookups, a separate high-volume events table for behavioral history -- exactly the separation that keeps queries fast at scale.
One clean warehouse, feeding both internal reporting and external customer activation — no duplicate data infrastructure.
We'll assess your current situation and give you a straight recommendation — implementation, migration, or optimization.
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Founder & CEOThis page focuses specifically on the platform implementation itself — the architecture, migration, and setup work. Our Snowflake Consulting services page covers the broader consulting engagement including cost optimization and ongoing managed services. Both describe the same real capability from slightly different angles.
Both, depending on your goals — some engagements are pure analytics warehousing with no CDP component. Others specifically structure the schema for clean reverse ETL activation later. We'll ask about your actual roadmap during scoping rather than assume.
Yes — this is one of Snowflake's real strengths as a foundation. A properly modeled warehouse can serve internal BI dashboards and, via reverse ETL, external activation tools simultaneously, without duplicating the underlying data.
Right-sizing compute to actual workload, implementing proper auto-suspend policies, and structuring queries efficiently from the start — cost optimization built into the initial architecture rather than a separate cleanup project later.
It means customer data is modeled with identity resolution and clean profile structure in mind from the start — so when you're ready to add reverse ETL activation via Hightouch or Segment, the warehouse doesn't need significant rework to support it.
Yes, this is a common engagement — migrating data, rebuilding transformation logic, and validating that the new Snowflake environment produces matching results before cutting over from the legacy platform.
A focused implementation for a specific use case can take a matter of weeks. More comprehensive implementations across many data sources with deeper governance requirements take longer, scoped during the initial audit.
Yes, we offer managed services covering performance monitoring, cost management, and architecture evolution for clients who want ongoing support beyond the initial build.
Yes — see our full Snowflake vs Databricks comparison for the detailed breakdown, or we can walk through your specific workload directly.
Reach out for a free assessment — we'll look at your current situation and give you an honest scope and recommendation.
Multiple, in most real implementations -- separating BI/dashboard queries, ETL/transformation jobs, and ad hoc analyst work into differently-sized warehouses prevents any one workload from starving the others of compute, and lets you right-size (and auto-suspend) each independently based on its actual usage pattern.
The profile table holds attributes that describe current state -- one row per customer, updated in place. The events table holds a full history of things that happened -- one row per event, never updated, only appended to. Mixing these into one table is a common early mistake that causes both performance problems and modeling confusion as the table grows.