Snowflake Services

Snowflake separates storage from compute, so a heavy query stops being a database problem and becomes a sizing decision. That property is why the platform is worth adopting. It is also where most implementations spend money by accident: warehouses sized by guesswork, grants handed out one person at a time, and pipelines that reload the world every night because nobody modeled the increments.

Iseyon builds Snowflake platforms and hands them over running. Clients bring us in for migrations off a legacy warehouse, for greenfield builds, and for estates that already work but cost more than they should.

What we build

Account and warehouse topology

We lay out accounts, databases and virtual warehouses so workloads stop competing. Loading, transformation, BI querying and data science each get their own compute, sized to the shape of the work and set to suspend when idle. Environments are separated so a change can be proven before it reaches production.

Layered data models

We land raw data untouched, keep an integration layer where business logic lives in exactly one place, and publish consumption models that BI tools and analysts read from. Semi-structured payloads stay queryable in VARIANT columns, and we flatten them at the point of use.

Pipelines that are cheap to rerun

We build ingestion with Snowpipe, streams and tasks, or with the orchestrator a client already runs. Transformations are incremental and idempotent, so the fix for a failed run is to run it again. Tests sit next to the models. A pipeline failure alerts a person, and bad data stops there.

Migration off an existing warehouse

We inventory what is actually queried, then move workloads in dependency order. Tables nobody reads stay behind. We rewrite legacy stored procedures for Snowflake, because the two engines reward different code. Old and new run in parallel until results reconcile, and cutover happens per subject area, so one difficult domain leaves the rest moving.

Governance and access

We model roles around how people actually work, then grant to roles and never to individuals. Row access policies, column masking on sensitive fields and object tagging are part of the build. The result is answerable: the client can say who can read a given column, and why.

Secure sharing

We use Snowflake native sharing so partners and internal consumers read live data in place. No extracts leave the platform, so the reconciliation problem copies create never starts, and revoking access stays a single action.

Cost visibility and control

We attribute spend to teams and workloads using resource monitors, warehouse separation and tagging, then tune the queries that dominate the bill. Auto-suspend, scaling policy and warehouse size become deliberate choices with an owner, and the client gets reporting that shows spend by team.

Analytics and machine learning on the platform

We keep feature engineering and scoring next to the data with Snowpark, so training sets are reproducible and models run against the governed data in place.

How an engagement runs

Snowflake runs on AWS, Azure and Google Cloud, so the platform we build sits alongside whichever cloud a client already uses. We build to leave. A handover has worked when the client's own engineers ship the next data product without calling us.