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. We are brought in on migrations off a legacy warehouse, on greenfield builds, and on 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 are flattened where they are used, rather than shredded on ingest and re-joined later.

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 a failed run gets rerun instead of untangled. Tests sit next to the models, and a pipeline failure alerts a person rather than quietly passing bad data downstream.

Migration off an existing warehouse

We inventory what is actually queried, not what merely exists, then move workloads in dependency order. Legacy stored procedures get rewritten rather than lifted, 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 does not stall the rest.

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, not a retrofit after an audit. 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 instead of receiving extracts. That removes the reconciliation problem copies create, and it keeps revoking access 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 rather than one aggregate invoice.

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 do not need a private copy of the warehouse to run.

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 rather than forcing a second one. We would rather leave than stay: a handover has worked when the client's own engineers ship the next data product without calling us.